World malaria report 2022

World malaria report 2022 Please consult the WHO Global Malaria Programme website for the most up-to-date version of all documents (https://www.who.int/teams/global-malaria-programme). World malaria report 2022 World malaria report 2022 ISBN 978-92-4-006489-8 (electronic version) ISBN 978-92-4-006490-4 (print version) © World Health Organization 2022 Some rights reserved. This work is available under the Creative Commons Attribution- NonCommercial-ShareAlike 3.0 IGO licence (CC BY-NC-SA 3.0 IGO; https://creativecommons.org/ licenses/by-nc-sa/3.0/igo). Under the terms of this licence, you may copy, redistribute and adapt the work for non-commercial purposes, provided the work is appropriately cited, as indicated below. In any use of this work, there should be no suggestion that WHO endorses any specific organization, products or services. The use of the WHO logo is not permitted. 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The mention of specific companies or of certain manufacturers’ products does not imply that they are endorsed or recommended by WHO in preference to others of a similar nature that are not mentioned. Errors and omissions excepted, the names of proprietary products are distinguished by initial capital letters. All reasonable precautions have been taken by WHO to verify the information contained in this publication. However, the published material is being distributed without warranty of any kind, either expressed or implied. The responsibility for the interpretation and use of the material lies with the reader. In no event shall WHO be liable for damages arising from its use. Layout: Claude Cardot/designisgood.info Cover design: Lushomo (Cape Town, South Africa) Map production: WHO Global Malaria Programme and WHO Data, Analytics and Delivery for Impact team (DDI). Contents Foreword vi Acknowledgements ix Abbreviations and acronyms xiv This year’s report at a glance xvi 1. Introduction 1 2. Overview of key events in 2021–2022 2 2.1 Global Fund Seventh Replenishment 2 2.2 New WHO recommendations 2 2.3 The strategy to respond to antimalarial drug resistance in Africa 4 2.4 Initiative to stop the spread of An. stephensi 6 2.5 The Global framework for the response to malaria in urban areas 6 2.6 Rollout of the RTS,S/AS01 malaria vaccine in areas of moderate to high malaria transmission 8 2.7 Humanitarian and health emergencies 8 2.8 Malaria service disruptions during the COVID-19 pandemic 10 3. Global trends in the burden of malaria 14 3.1 Global estimates of malaria cases and deaths, 2000–2021 14 3.2 Estimated malaria cases and deaths in the WHO African Region, 2000–2021 18 3.3 Estimated malaria cases and deaths in the WHO South-East Asia Region, 2000–2021 20 3.4 Estimated malaria cases and deaths in the WHO Eastern Mediterranean Region, 2000–2021 22 3.5 Estimated malaria cases and deaths in the WHO Western Pacific Region, 2000–2021 24 3.6 Estimated malaria cases and deaths in the WHO Region of the Americas, 2000–2021 26 3.7 Estimated malaria cases and deaths in the WHO European Region, 2000–2021 28 3.8 Cases and deaths averted since 2000, globally and by WHO region 28 3.9 Burden of malaria in pregnancy 30 4. Elimination and the high burden to high impact approach 34 4.1 Nearing elimination 34 4.2 Malaria elimination certification 34 4.3 E-2025 initiative 36 4.4 The Greater Mekong subregion 38 4.5 Prevention of re-establishment 42 4.6 The high burden to high impact approach 42 5. Surveillance 44 5.1 Country implementation of digital malaria modules 44 5.2 Malaria surveillance assessments 48 6. Investments in malaria programmes and research 52 6.1 Funding trends for malaria control and elimination 52 6.2 Investments in malaria-related R&D 62 7. Distribution and coverage of malaria prevention, diagnosis and treatment 64 7.1 Distribution and coverage of ITNs 64 7.2 Population protected with IRS 67 7.3 Scale-up of SMC 68 7.4 Coverage of IPTp use by dose 70 7.5 Malaria diagnosis and treatment 71 iii 8. Global progress towards the GTS milestones 76 8.1 Global progress 76 8.2 WHO African Region 80 8.3 WHO Region of the Americas 82 8.4 WHO Eastern Mediterranean Region 84 8.5 WHO South-East Asia Region 85 8.6 WHO Western Pacific Region 86 9. Biological and other threats to malaria interventions 88 9.1 Deletions in P. falciparum histidine-rich protein 2 and protein 3 genes 88 9.2 Status of antimalarial drug efficacy and resistance (2015–2021) 89 9.3 Vector resistance to insecticides 94 9.4 Anopheles stephensi invasion and spread 96 9.5 Effectiveness of ITNs 97 9.6 Effectiveness challenges related to IRS 103 9.7 Beyond the reach of ITNs: addressing residual transmission 104 10. Malaria preferred products and R&D pipeline 106 10.1 Preferred product characteristics and target product profiles for new malaria interventions 106 10.2 Malaria diagnostics 108 10.3 Malaria medicines 110 10.4 Malaria vector control R&D pipeline 113 10.5 Malaria vaccines 115 11. Key findings and conclusion 116 11.1 Global malaria response and trends in cases and deaths 116 11.2 Risks to progress against malaria 117 11.3 Enhancing resilience 119 11.4 Preferred products and R&D pipeline 120 References 122 Annexes 131 iv W O RL D M AL AR IA R EP O RT 2 02 2 Foreword Dr Tedros Adhanom Ghebreyesus Director-General World Health Organization Each year, the World Malaria Report spotlights progress and gaps in efforts to combat malaria across the globe. This year’s report is organized around four themes: response, risks, resilience and research. Malaria-affected countries continue to respond to a host of challenges, including disruptions arising from the COVID-19 pandemic. Although hard hit, most countries held the line and were able to maintain services to prevent, detect and treat malaria - a remarkable feat in the midst of a pandemic. Nonetheless, more than 600 000 people still die of malaria every year – most of them children. Even with the heroic efforts to maintain services during the pandemic, malaria control efforts face many hurdles in addition to the already significant COVID-related disruptions and other health system challenges, including long-running humanitarian crises, limited donor funding, and the potential effect of climate change on the spread of the disease. These challenges have been further compounded by declines in the effectiveness of our primary malaria-fighting tools. Rising resistance to insecticide-treated nets and to antimalarial drug regimens remain a major concern – particularly in Africa. In some areas, malaria parasites are escaping detection from the most widely used diagnostic tests. And the invasion of a mosquito that adapts easily to urban environments, Anopheles stephensi, poses a real risk. To support the resilience of malaria programmes, WHO has published new guidance, including a new strategy to contain antimalarial drug resistance in Africa; a new framework, developed jointly by WHO and UN-Habitat, to guide city leaders in urban malaria control; and recommendations to stop the spread of Anopheles stephensi in urban environments. Countries are encouraged to adapt WHO guidance to local needs. Malaria control is intimately tied to the overall health system. Investments in well-functioning, equitable and resilient health systems built on a foundation of primary health care are critical to a successful malaria response. vi W O RL D M AL AR IA R EP O RT 2 02 2 Research and development plays a critical role as well. As described in these pages, new types of vector control technologies are being developed, including a new generation of nets to combat mosquito resistance. New diagnostics and innovations in malaria medicines are also in the pipeline. Another breakthrough is the RTS,S malaria vaccine, the first and only vaccine recommended to prevent malaria in children. More than 1.2 million children are protected by the vaccine in Ghana, Kenya and Malawi, and the vaccine will soon be expanded to several other countries. Other promising malaria tools are in development. However, there continues to be a significant funding gap for basic research and product development. There is an urgent need to ramp up investment in new malaria tools, to address emerging threats such as urban malaria and the spread of antimalarial drug resistance. We face many challenges, but there are many reasons for hope. By strengthening the response, understanding and mitigating the risks, building resilience and accelerating research, there is every reason to dream of a malaria-free future. vii viii W O RL D M AL AR IA R EP O RT 2 02 2 Acknowledgements WHO is very grateful to the numerous people who contributed to the production of the World Health Organization (WHO) World malaria report 2022. The following people collected and reviewed data from both malaria endemic and malaria free countries and areas: Ahmad Mureed Muradi, Lutfullah Noori, Naimullah Safi and Mohammad Shoaib Tamim (Afghanistan); Lammali Karima and Houria Khelifi (Algeria); Fernanda Francisco Guimaraes and Fernanda Isabel Martins da Graça do Espirito Santo Alves (Angola); Malena Basilio and Yael Provecho (Argentina); Brittney Jones (Bahamas and Turks and Caicos); Md. Jahangir Alam, Anupama Hazarika, Md. Nazrul Islam and Md. Mushfiqur Rahman (Bangladesh); Kim Bautista and Job Joseph (Belize); Rock Aikpon and Aissan Codjo Julien (Benin); Tobgyel Drukpa, Rinzin Namgay, Phurpa Tenzin, Sonam Wangdi, Tenzin Wangdi and Ugyen Zangpo (Bhutan); Alfonso Tenerio Gnecco, Percy Halkyer and Raul Marcelo Manjon Telleria (Bolivia [Plurinational State of]); Kentse Moakofhi, Mpho Mogopa, Davis Ntebela, Morarabi Oitsile and Godira Segoea (Botswana); Liana Reis Blume, Poliana de Brito Ribeiro Reis, Cristianne Aparecida Costa Haraki, Franck Cardoso de Souza, Eliandra Castro de Oliveira, Keyty Costa Cordeiro, Anderson Coutinho da Silva, Paloma Dias de Sousa, Francisco Edilson Ferreira de Lima Junior, Miguel Angel Aragon Lopez, Klauss Kleydmann Sabino Garcia, Gilberto Gilmar Moresco, Marcela Lima Dourado, Paola Barbosa Marchesini, Marcia Helena Maximiano de Almeida, Joyce Mendes Pereira, Marcelo Souza El Corab Moreira, Jessica de Oliveira Sousa, Marcio Pereira Fabiano, Ronan Rocha Coelho, Edilia Samela Freitas Santos, Pablo Sebastian Tavares Amaral, Silvia Teotonio de Almedia and Marcelo Yoshito Wada (Brazil); Gauthier Tougri (Burkina Faso); Dismas Baza and Juvenal Manirampa (Burundi); Carolina Cardoso da Silva Leite Gomes and Antonio Lima Moreira (Cabo Verde); Tol Bunkea and Pengby Ngor (Cambodia); Abomabo Moise Hugue Rene and Etienne Nnomzo’o (Cameroon); Aristide Desire Komangoya-Nzonzo and Christophe Ndoua (Central African Republic); Mahamat Idriss Djaskano, Mahamat Saleh Issakha Diar and Honore Djimrassengar (Chad); Eduin Pachon Abril, Ivan Mauricio Cardenas, Guillermo Gonzalvez and Martha Ospino (Colombia); Affane Bacar, Mohamed Issa Ibrahim, Ahamada Nassuri and Ali Saidou (Comoros); Matondo Julde Mauriciel, Hermann Ongouo and Jean-Mermoz Youndouka (Congo); Carlos Aguilar Avendao, Monica Gamboa, Gabriela Rey, Isaac Vargas Roldan and Jorge Victoria (Costa Rica); Serge Alexis Aimain (Côte d’Ivoire); Kim Yun Chol and O Nam Ju (Democratic People’s Republic of Korea); Patrick Bahizi Bizoza, Hyacinthe Kaseya Ilunga, Bacary Sambou and Eric Mukomena Sompwe (Democratic Republic of the Congo); Abdoulkader Ali Adou and Samatar Kaya (Djibouti); Olivia Brathwaite, Maria Yinet Santos Felix, Emely Fernandez and Dianelba Valdez (Dominican Republic); Julio Rivera, Washington Rueda and Aida Soto (Ecuador); Angel Manuel Alvarez and Jose Eduardo Romero Chevez (El Salvador); Angela Katherine Lao Seoane and Mathilde Riloha Rivas (Equatorial Guinea); Selam Mihreteab and Assefash Zehaie (Eritrea); Quinton Dhlamini, Kevin Makadzange and Zulisile Zulu (Eswatini); Henock Ejigu, Mebrahtom Haile, Ayele Tiyou and Bekele Worku (Ethiopia); Vincent Robert and Prabhjot Singh (French Guiana); Ghislaine Nkone Asseko and Okome Nze Gyslaine (Gabon); Momodou Kalleh and Sharmila Lareef-Jah (Gambia); Keziah L. Malm and Felicia Owusu-Antwi (Ghana); Luis Marroquin and Ericka Lidia Chavez Vasquez (Guatemala); Siriman Camara and Nouman Diakite (Guinea); Inacio Alveranga and Paulo Djata (Guinea-Bissau); Rainier Escalada, Kashana James, Reza Niles and Fabu Moses Stuart (Guyana); Antoine Darlie, Joaquim Da Silva, Jean-Marie Rwangabwoba and Marc-Aurelle Telfort (Haiti); Engels Banegas, Cinthia Contreras, Jessica Henriquez, Rosa Mejia, Jose Orlinder Nicolas and Oscar Urrutia (Honduras); Vinod Choudhary, Naresh Gill, Tanu Jain, Roop Kumari and Rinku Sharma (India); Herdiana Hasan Basri, Helen Dewi Prameswari, Riskha Tiara Puspadewi and Asik Surya (Indonesia); Elham Almasian, Leila Faraji, Firoozeh Goosheh, Fatemeh Nikpour and Ahmad Raeisi (Iran [Islamic Republic of]); Serene Joseph (Jamaica and Cayman Islands); James Kiarie and James Otieno (Kenya); Viliasak Banouvong and Viengxay Vanisaveth (Lao People’s Democratic Republic); Levi Hinneh, Moses Jeuronlon and Oliver J. Pratt (Liberia); Mauricette Andrianamanjara, Henintsoa Rabarijaona and Urbain Rabibizaka (Madagascar); Austin Gumbo ix and Michael Kayange (Malawi); Jenarun Jelip and Tam Jenn Zhueng (Malaysia); Sidibe Boubacar, Idrissa Cisse and Aissata Kone (Mali); Lemlih Baba and Sidina Mohamed Ghoulam (Mauritania); Frederic Pages (Mayotte); Santa Elizabeth Ceballos Liceaga, Fabian Correa Morales, Eric Piña and Maria Jesus Sanchez (Mexico); Balthazar Candrinho, Eva de Carvalho and Guidion Mathe (Mozambique); Nay Yi Yi Linn, Badri Thapa, Tet Tun Toe and Kyawt Mon Win (Myanmar); Rauha Jacob, Nelly Ntusi, Wilma Soroses and Petrina Uusiku (Namibia); Gokarna Dahal, Chuman Lal Das, Subhash Lakhe, Khin Pa Pa Naing and Uttam Raj Pyakurel (Nepal); Enrique Perez Flores and Holvin Martin Gutierrez Perez (Nicaragua); Fatima Aboubakar and Hadiza Jackou (Niger); Lynda Ozor and Perpetua Uhomoibhi (Nigeria); Hammad Habib, Qutbuddin Kakar, Muhammad Mukhtar, M. Shafiq and Munir-Ur-Rehman (Pakistan); Oscar Gonzalez and Gloria Hengo (Panama); John Deli (Papua New Guinea); Martha Torales and Cynthia Viveros (Paraguay); Veronica Soto Calle, Christiam Carey and Hans Salas Maronsky (Peru); Raffy Deray and Princess Mhyco L. Esguerra (Philippines); Hyesu Kan, Jae-Eun Lee and Nari Shin (Republic of Korea); Aimable Mbituyumuremvi and Jules Mugabo Semahore (Rwanda); Claudina Augusto da Cruz, Jose Alvaro Leal Duarte and Anastacio Pires (Sao Tome and Principe); Mohammed Hassan Al-Zahrani (Saudi Arabia); Ndella Diakhate and Medoune Ndiop (Senegal); Ronald Carshon-Marsh and Musa Sillah-Kanu (Sierra Leone); Ross Hutton, John Leaburi and Edgar Pollard (Solomon Islands); Abdi Abdillahi Ali, Jamal Amran, Abdifatah Ahmed Barre, Abdikarim Hussein Hassan, Ali Abdirahman Osman and Fahim Isse Yusuf (Somalia); Patrick Moonasar and Mbavhalelo Bridget Shandukani (South Africa); Ahmed Julla (South Sudan); Pubudu Chulasiri, Kumudu Gunasekera, Prasad Ranaweera and Preshila Samaraweera (Sri Lanka); Mariam Adam, Siham Abdalla Ali Mohammed and Mujahid Nouredayem (Sudan); Loretta Hardjopawiro and Oscar Martin Mesones Lapouble (Suriname); Deyer Gopinath, Jerdsuda Kanjanasuwan, Suravadee Kitchakarn, Chantana Padungtod, Aungkana Saejeng, Prayuth Sudathip, Chalita Suttiwong and Kanutcharee Thanispong (Thailand); Juliana do Rosario, Maria do Rosario de Fatima Mota, Debashish Kundu and Raul Sarmento (Timor-Leste); Kokou Mawule Davi and Tchassama Tchadjobo (Togo); Bayo Fatunmbi, Charles Katureebe, Ronald Kimuli, Paul Mbaka, John Opigo and Damian Rutazaana (Uganda); Abdullah Ali, Mohamed Haji Ali, Jovin Kitau, Anna Mahendeka, Ally Mohamed, Khalifa Munisi and Irene Mwoga (United Republic of Tanzania); Wesley Donald (Vanuatu); Franklin Hernandez, Licenciada America Rivero and Edison Soto (Venezuela [Bolivarian Republic of]); Nguyen Quy Anh (Viet Nam); Rybion Alamodi, Adel Aljassri, Methaq Alsadah, Abdallah Awash, Moamer Badi and Yasser Abdullah Baheshm (Yemen); Japhet Chiwaula, Freddie Masaninga and Mutinta Mudenda (Zambia); and Anderson Chimusoro, Joseph Mberikunashe, Jasper Pasipamire and Ottias Tapfumanei (Zimbabwe). WHO is grateful to the following people for their contributions: ■ Eliane Furrer and Mary Hamel (WHO Product and Delivery Research) provided information on the rollout of the malaria vaccine. ■ Data and information on malaria service disruptions were contributed by Lia Florey and Meera Venkatesan (United States President’s Malaria Initiative [PMI]); Melanie Renshaw (African Leaders Malaria Alliance); Marcy Erskine (Alliance for Malaria Prevention [AMP]); Mehran Hosseini and Weiyuan Cui (Global Fund to Fight AIDS, Tuberculosis and Malaria [Global Fund]); and Kathy O’Neill and Briana Rivas-Morello (WHO Integrated Health Services). ■ The estimates of Plasmodium falciparum parasite prevalence and incidence in sub-Saharan Africa were produced by Ewan Cameron, Paulina Dzianach, Ahmed Elagali, Susan Rumisha, Adam Saddler, Mauricio Van Den Berg, Camilo Vargas and Daniel Weiss of the Malaria Atlas Project (MAP, led by Peter Gething, Curtin University and Telethon Kids Institute) and Samir Bhatt (University of Copenhagen). Samir Bhatt, Amelia Bertozzi-Villa (Institute for Disease Modelling) and MAP collaborated to produce the estimates of insecticide-treated mosquito net (ITN) coverage for African countries, using data from household surveys, ITN deliveries by manufacturers and ITNs distributed by national malaria programmes (NMPs). ■ Patrick Walker (Imperial College London, United Kingdom of Great Britain and Northern Ireland [United Kingdom]) contributed to the analysis of exposure to malaria infection during pregnancy and attributable low birthweight. x W O RL D M AL AR IA R EP O RT 2 02 2 Acknowledgements ■ Results from surveillance system assessments were provided by Pascal Sandwidi and Gauthier Tougri (NMP, Burkina Faso); Alain Bokota and Eric Mukomena (NMP, Democratic Republic of the Congo); and Keziah Malm, Wahjib Mohammed and Paprah Nana Yaw (NMP, Ghana). Thibaud de Chevigny, Arnaud Le Menach and Deepa Pindolia (Clinton Health Access Initiative [CHAI]) supported countries with surveillance system assessments and the synthesis of results shown in the surveillance section. ■ Alexander Birikorang, Faadeel Ramjanally and Lisa Regis (Global Fund) supplied information on financial disbursements from the Global Fund. Nicola Wardrop and Laura Westcott (Foreign, Commonwealth and Development Office), and Stephanie Oum and Adam Wexler (Kaiser Family Foundation) provided information on financial contributions for malaria control from the United Kingdom and the United States of America (USA), respectively. Emmanuelle Bomo, Maya Goldstein and Andrew Tuttle from Policy Cures Research used their G-FINDER data in the analysis of financing for malaria research and development, and wrote the associated section. ■ John Milliner (Milliner Global Associates) provided information on long-lasting insecticidal nets delivered by manufacturers. Celine Audibert and Andre Marie Tchouatieu (Medicines for Malaria Venture [MMV]) and Paul Milligan (London School of Hygiene & Tropical Medicine [LSHTM]) contributed to updating the section on seasonal malaria chemoprevention (SMC) with recent information on implementation. The SMC map was developed by Paul Milligan using data provided to MMV by NMPs in each country and with support from Celine Audibert (MMV); Olimatou Kolley (NMP, the Gambia); Nnenna Ogbulafor and Emmanuel Shekaru (National Malaria Elimination Programme, Nigeria); Christian Rassi (Malaria Consortium, United Kingdom); Issaka Sagara (Malaria Research and Training Center, Mali) and Paul Snell (LSHTM). ■ Katherine Strong (WHO Department of Maternal, Newborn, Child and Adolescent Health) and Bochen Cao (WHO Division of Data, Analytics and Delivery for Impact) prepared the malaria cause of death fraction and the estimates of malaria mortality in children aged under 5 years, on behalf of the Maternal Child Epidemiology Estimation Group. ■ The analysis of the effectiveness of ITNs was supported by Amelia Bertozzi-Villa, Caitlin Bever and Daniel Bridenbecker (Institute for Disease Modeling); Marcy Erskine (AMP); Peter Gething, Mauricio Van Den Berg and Camilo Vargas (MAP and Telethon Kids Institute); Albert Kilian (independent consultant); Hannah Koenker (Tropical Health LLP); Marion Law and Dominic Schuller (WHO Prequalification); and Rosemary Lees and Angus Spiers (Innovation to Impact). ■ Jennifer Daily (independent consultant), Ewurama Dedea Ampadu Owusu and Rosaline Howes (Foundation for Innovative New Diagnostics [FIND]) contributed to the section on malaria diagnostics in the research and development (R&D) pipeline; Cristina Donini, George Jagoe, Wiweka Kaszubska, David Reddy and Timothy Wells contributed to the section on malaria medicines in the R&D pipeline; and Christopher Larkin, Tom McLean, Derric Nimmo and Laura Roberts contributed to the section on malaria vector control products in the R&D pipeline. ■ Corine Karema (interim Chief Executive Officer of the RBM Partnership to End Malaria) provided inputs to the key findings and conclusion section. The following WHO staff in regional and subregional offices assisted in the design of data collection forms; the collection and validation of data; and the review of epidemiological estimates, country profiles, regional profiles and sections: ■ Ebenezer Sheshi Baba, Steve Kubenga Banza, Emmanuel Chanda, Akpaka A. Kalu and Jackson Sillah (WHO Regional Office for Africa [AFRO]); ■ Khoti Gausi (AFRO/WHO Country Office South Sudan), Lamine Diawara, Elizabeth Juma, Spes Ntabangana, Adiele Onyeze and Abderrahmane Kharchi Tfeil (AFRO/Tropical and Vector-borne Diseases/Multi Country Assignment Teams); ■ Maria Paz Ade, Jean SF Alexandre, Janina Chavez, Blanca Escribano, Roberto Montoya and Dennis Navarro Costa (WHO Regional Office for the Americas); ■ Samira Al-Eryani, Lina Azkoul and Ghasem Zamani (WHO Regional Office for the Eastern Mediterranean); ■ Siddharta Sankar Datta (WHO Regional Office for Europe); xi ■ Risintha Premaratne and Neena Valecha (WHO Regional Office for South-East Asia); and ■ Elodie Jacoby, James Kelley, Rady Try and Luciano Tuseo (WHO Regional Office for the Western Pacific). The maps for country and regional profiles were produced by MAP’s Data Engineering team and funded by the Bill & Melinda Gates Foundation. The map production was led and coordinated by Jen Rozier, with help from Paul Castle and Joe Harris. Tolu Okitika coordinated MAP’s contribution to this report. WHO is also grateful to Jaline Gerardin (Northwestern University, USA) and Fredros Okumu (Ifakara Health Institute, United Republic of Tanzania) for the independent review of all sections and comments provided for improvement; Diana Estevez Fernandez, Jessica Ho and Wahyu Retno Mahanani (WHO) for statistics review; Tessa Edejer and Edith Patouillard (WHO) for review of economic evaluation and analysis; Egle Granziera and Claudia Nannini (WHO) for legal review; and Kathleen Krupinski (WHO) for map production. The publication of the World malaria report 2022 was coordinated by Abdisalan Noor, who was the lead author of the report. Significant technical contributions were made by Laura Anderson, Amy Barrette, Andrea Bosman, Yuen Ching Chan, Tamara Ehler, Beatriz Galatas, Mwalenga Nghipumbwa, Peter Olumese, Alastair Robb, Silvia Schwarte, Erin Shutes, Ryan Williams and Chunzhe Zhang. The section on Anopheles stephensi was contributed to by Seth Irish (WHO consultant). Lindsey Wu (WHO/GMP) contributed to the section on malaria preferred product characteristics and target product profiles for new malaria interventions and vaccine R&D pipeline. Laurent Bergeron (WHO/GMP) provided programmatic support for overall management of the project. The editorial committee for the report comprised Andrea Bosman, Elkhan Gasimov, Jan Kolaczinski, Leonard Ortega, Pascal Ringwald and Alastair Robb from WHO/GMP. Additional reviews were received from colleagues in WHO/GMP: Jane Cunningham, Xiao Hong Li, Charlotte Rasmussen and Saira Stewart. Report layout, design and production were coordinated by Laurent Bergeron. This year’s world malaria report is dedicated to the memory of the late Richard Cibulskis who led the publication of the report from 2008 to 2016. Funding for the production of this report was gratefully received from the Bill & Melinda Gates Foundation, the Global Fund, the government of China, the Spanish Agency for International Development Cooperation, Unitaid and the United Nations Office for Project Services (UNOPS). WHO also appreciates the support from PMI. xii W O RL D M AL AR IA R EP O RT 2 02 2 xiii Abbreviations and acronyms ACT artemisinin-based combination therapy AIDS acquired immunodeficiency syndrome AIM Action and investment to defeat malaria 2016–2030 AL artemether-lumefantrine AMP Alliance for Malaria Prevention ANC antenatal care ANC1 first ANC visit app application AQ amodiaquine AS artesunate ATSB® Attractive Targeted Sugar Bait CDC Centers for Disease Control and Prevention CI confidence interval CoD cause of death COVID-19 coronavirus disease CQ chloroquine CRS creditor reporting system DAC Development Assistance Committee DHA dihydroartemisinin dhfr dihydrofolate reductase (gene) dhps dihydropteroate synthase (gene) DHS demographic and health surveys DHIS2 District Health Information System 2 E-2020 malaria eliminating countries for 2020 E-2025 malaria eliminating countries for 2025 EHS essential health services EPI Expanded Programme of Immunization ERPd Evidence Review Panel for diagnostics FCDO Foreign, Commonwealth and Development Office (United Kingdom) FIF financial intermediary fund FIND Foundation for Innovative New Diagnostics G6PD glucose-6-phosphate dehydrogenase Gavi Gavi, the Vaccine Alliance GDP gross domestic product Global Fund Global Fund to Fight AIDS, Tuberculosis and Malaria GMP Global Malaria Programme GMS Greater Mekong subregion GOHRD Global Observatory on Health Research and Development GTS Global technical strategy for malaria 2016–2030 HBHI high burden to high impact HIV human immunodeficiency virus HMIS Health Management Information System HRP2 histidine-rich protein 2 HRP3 histidine-rich protein 3 iDES integrated drug efficacy surveillance IDSR Integrated Disease Surveillance and Response IMF International Monetary Fund IPTi intermittent preventive treatment in infants IPTp intermittent preventive treatment of malaria in pregnancy IPTp1 first dose of IPTp IPTp2 second dose of IPTp IPTp3 third dose of IPTp IPTp4 fourth dose of IPTp IPTsc intermittent preventive treatment of malaria in school-aged children IRM insecticide resistance management IRMMP insecticide resistance monitoring and management plan IRS indoor residual spraying IRST indoor residual surface treatments xiv W O RL D M AL AR IA R EP O RT 2 02 2 ITN insecticide-treated mosquito net IVB Department of Immunization, Vaccines and Biologicals IVCC Innovative Vector Control Consortium IVM integrated vector management LBW low birthweight LDH lactate dehydrogenase LLIN long-lasting insecticidal net LMIC low- and middle-income countries LMIS Logistics Management Information System LSHTM London School of Hygiene & Tropical Medicine mAbs monoclonal antibodies MAP Malaria Atlas Project MCEE WHO Maternal and Child Health Epidemiology Estimation Group MDA mass drug administration MECP Malaria Control and Elimination Program MEDB Malaria Elimination Database MEOC Malaria Elimination Oversight Committee MIS malaria indicator surveys MMV Medicines for Malaria Venture MPAG Malaria Policy Advisory Group MQ mefloquine MRP mass relapse prevention NCE new chemical entity NIH National Institutes of Health NMP national malaria programme OECD Organisation for Economic Co-operation and Development PBO piperonyl butoxide PCR polymerase chain reaction PDMC post-discharge malaria chemoprevention Pfhrp Plasmodium falciparum histidine-rich protein (gene) PfKelch13 Plasmodium falciparum Kelch13 (gene) PMC perennial malaria chemoprevention PMI President’s Malaria Initiative PPC preferred product characteristic PPQ piperaquine PPR prevention, preparedness and response PY pyronaridine R&D research and development RCT randomized controlled trial RD resistance to damage RDT rapid diagnostic test RTS,S RTS,S/AS01 SDG Sustainable Development Goal SMC seasonal malaria chemoprevention SP sulfadoxine-pyrimethamine TB tuberculosis TES therapeutic efficacy studies TPP target product profiles UN United Nations UN-Habitat United Nations Human Settlements Programme UNICEF United Nations Children’s Fund United Kingdom United Kingdom of Great Britain and Northern Ireland US United States USA United States of America USAID United States Agency for International Development VCP vector control product WHO World Health Organization WHO-CHOICE WHO-CHOosing Interventions that are Cost-Effective WHOPES WHO Pesticide Evaluation Scheme xv This year’s report at a glance KEY EVENTS IN 2021–2022 The Seventh Replenishment of the Global Fund to Fight AIDS, Tuberculosis and Malaria ■ In 2021, more than 40% of global malaria investment was channelled through the Global Fund to Fight AIDS, Tuberculosis and Malaria (Global Fund). ■ In September 2022, via the Global Fund replenishment, a total of US$ 15.9 billion was raised through countries and partners, the largest replenishment in the history of the Global Fund. ■ The replenishment fell short of the US$ 18 billion target at a time when the cost of health commodities is rising due to the coronavirus (COVID-19) pandemic, conflict and the global recession. ■ The shortfall means that the current replenishment will be under considerable strain to maintain and expand levels of malaria intervention coverage. ■ To optimize the impact of the limited resources, there will be an even greater need to maximize the efficient, effective and equitable use of malaria resources and other health system resources. ■ Countries need to be able to identify the optimal mix of interventions suited to their local context and the best means of delivering them to all people in need, guided by local data. New World Health Organization recommendations ■ In June 2022, the World Health Organization (WHO) published guidance on new and updated recommendations. The guidelines encourage countries to tailor the recommendations to local disease settings for maximum impact. ■ Updated guidelines provide recommendations on intermittent preventive treatment of malaria in pregnancy (IPTp), perennial malaria chemoprevention (PMC) and seasonal malaria chemoprevention (SMC), intermittent preventive treatment of malaria in school-aged children (IPTsc), post-discharge malaria chemoprevention (PDMC), mass drug administration (MDA) and elimination. ■ The use of IPTp with sulfadoxine-pyrimethamine (SP) in areas of moderate to high Plasmodium falciparum malaria transmission continues to be strongly recommended. ■ IPTp-SP is now recommended for all pregnant women, regardless of the number of pregnancies. ■ An updated SMC and PMC recommendation supports the broader use of chemoprevention among young children at high risk of severe malaria in areas with both seasonal and year-round transmission. ■ WHO has also issued a new recommendation on the use of IPTsc in settings with moderate to high perennial or seasonal malaria transmission. IPTsc should cover children aged 5–15 years, and its introduction should not compromise chemoprevention interventions for children aged under 5 years, who are at highest risk of severe malaria. xvi W O RL D M AL AR IA R EP O RT 2 02 2 ■ PDMC is now recommended to prevent malaria among children with severe anaemia living in areas of moderate to high transmission after they are discharged from a hospital, when they are at high risk of re-admission or death. ■ The new recommendations on malaria MDA provide guidance on rapidly reducing the malaria disease burden in emergency settings and in areas of moderate to high transmission. They also provide guidance on the use of MDA to reduce P. falciparum malaria in settings with very low to low transmission, and to reduce P. vivax transmission. ■ Elimination recommendations include “mass” strategies applied to the entire population of a delimited geographical area, whether it be a village, township or district; “targeted” strategies applied to people at greater risk of infection than the general population; and “reactive” strategies triggered in response to individual cases. New regional initiatives launched by WHO in collaboration with partners Strategy to respond to antimalarial drug resistance in Africa ■ The emergence of artemisinin partial resistance in the WHO African Region with the apparent rapid spread of mutations associated with artemisinin partial resistance is of great concern. This emergence has not spread from the Greater Mekong subregion (GMS) but has appeared de novo on the continent. ■ Artemisinin-based combination therapies (ACTs) remain efficacious; resistance to the ACT partner drugs currently in use has not been confirmed, but there are some worrying signals that need to be investigated and action must be taken before ACTs start to fail. ■ Given the heavy reliance on ACTs in Africa, the threat of artemisinin partial resistance and partner drug resistance must be monitored and addressed urgently. ■ In November 2022, WHO launched the Strategy to respond to antimalarial drug resistance in Africa, which aims to provide guidance to key stakeholders in the malaria community. ■ The strategy addresses the threat of antimalarial drug resistance in the WHO African Region through four pillars: ■ Pillar I – Strengthen surveillance of antimalarial drug efficacy and resistance. ■ Pillar II – Optimize and better regulate the use of diagnostics and therapeutics to limit drug pressure through pre-emptive measures. ■ Pillar III – React to resistance by limiting the spread of antimalarial drug-resistant parasites. ■ Pillar IV – Stimulate research and innovation to better leverage existing tools and develop new tools against resistance. ■ Each pillar comprises a set of interventions that can be implemented at local, regional and global levels. Implementing countries need to tailor the interventions in the strategy to their local context. Initiative to stop the spread of Anopheles stephensi ■ Anopheles stephensi, native to south Asia and parts of the Arabian Peninsula, has been expanding its range over the past decade, with detections reported in Djibouti (2012), Ethiopia and the Sudan (2016), Somalia (2019) and Nigeria (2020). ■ Where An. stephensi has been reported in the WHO African Region, it has been found to be resistant to many of the insecticides used in public health, posing an added challenge to its control. ■ The invasion of An. stephensi in sub-Saharan Africa – where the burden of malaria is highest and over 40% of the population lives in urban environments – has raised concern, because it is thought to have driven the resurgence of malaria in Djibouti city and to be the source of at least one outbreak in Ethiopia. ■ In September 2022, a new initiative was launched aimed at stopping the further spread of An. stephensi on the African continent and to determine whether it can be eliminated from areas that have already been invaded. xvii ■ The initiative calls for the national response to An. stephensi to be part of a comprehensive response to malaria vectors by enhancing collaboration, increasing surveillance, improving information exchange, developing guidance and prioritizing research. The Global framework for the response to malaria in urban areas ■ By 2050, almost seven in 10 people globally will live in cities and other urban settings. In malaria endemic countries, more people are likely to be in urban areas than in rural areas. ■ In the 10 highest burden countries in the WHO African Region, over 40% of the population are already considered to live in urban areas. ■ Urban areas and rural areas can differ in the dynamics of the transmission and the burden of malaria and other vector-borne diseases. ■ Overall, urbanization will reduce malaria transmission. However, unplanned urbanization will lead to focal transmission, resulting in a disease burden that is disproportionately high among the urban poor. ■ Invasion by vectors that are adapted to breeding in urban environments, such as the recent spread of An. stephensi in the WHO African Region, may be putting urban populations at increased risk. ■ Approaches that work to reduce disease burden in rural areas may not work in urban settings, or may need to occur at a smaller, more targeted scale. ■ The malaria response in urban areas requires data on the determinants that are unique to urban ecosystems, and that explain whether malaria transmission and disease burden are focal. ■ Leadership from government departments, industry and finance, research, academia and other sectors tends to be concentrated in urban areas, providing a greater opportunity for integrated, multisectoral policies, strategies and actions. ■ As urbanization rapidly increases, there is a greater focus on healthier cities, including the global political will to address the threats posed by climate change. ■ In October 2022, WHO and the United Nations Human Settlements Programme (UN-Habitat) jointly launched the Global framework for the response to malaria in urban areas, which is targeted at policy-makers and relevant stakeholders. ■ The framework is a response to malaria in towns and cities; it highlights the central role of city leadership and the need to ensure that the malaria response is part of the broader urban development and health agenda. It also emphasizes the need to implement a data-driven targeted response in the design of malaria interventions. If implemented, the framework is expected to help in improving health and well-being. Rollout of the RTS,S/AS01 malaria vaccine in areas of moderate to high malaria transmission ■ In October 2021, WHO recommended the RTS,S/AS01 malaria vaccine for the prevention of P. falciparum malaria in children living in regions with moderate to high transmission (as defined by WHO); in July 2022, WHO issued prequalification approval for the RTS,S vaccine. ■ In December 2021, the board of Gavi, the Vaccine Alliance (Gavi) approved an initial investment of almost US$ 160 million (in 2022–2025) to support the broader rollout of the malaria vaccine in Gavi-eligible countries. ■ Gavi has estimated that steady-state demand for the vaccine will exceed 80–100 million doses per year. In anticipation of a gap between supply and demand, WHO coordinated the development of a framework for allocating a limited malaria vaccine supply, to guide the fair and transparent allocation of limited doses, based on ethical principles and the best available evidence. ■ In August 2022, the United Nations Children’s Fund (UNICEF) announced that it had secured supply of the GSK-produced vaccine, for an available malaria vaccine supply of 18 million doses over the next 3 years (2023–2025). xviii W O RL D M AL AR IA R EP O RT 2 02 2 ■ Since June 2022, WHO and Gavi have supported over 20 countries through multicountry workshops focusing on the development of applications to Gavi. A key part of this initiative has been the WHO-supported subnational tailoring of the malaria vaccine rollout, based on guidance from the framework for the allocation of the limited malaria vaccine supply. Humanitarian and health emergencies ■ Malaria endemic countries have been dealing with health and humanitarian emergencies unrelated to the COVID-19 pandemic. In 2021, an estimated 268 million people were dealing with humanitarian emergencies compared with 301 million in 2020. ■ Conflicts, famine and flooding were the major contributors to these humanitarian emergencies, which were sometimes compounded by disease outbreaks. ■ Countries with the highest need in 2021 in terms of affected people were Afghanistan, Colombia, the Democratic Republic of the Congo, Ethiopia, Mali, Myanmar, Nigeria, Somalia, South Sudan, the Sudan, Uganda and Yemen. Malaria service disruptions during the COVID-19 pandemic ■ Forty-six countries had insecticide-treated mosquito net (ITN) campaigns planned in 2020, to distribute about 272 million nets. By the end of 2020, 74% of all ITNs planned for distribution in 2020 had been distributed. ■ In 2021, there were 43 countries that had ITN campaigns planned, including 14 countries that planned to distribute only ITNs carried over from 2020. ■ A total of 171 million ITNs were planned for distribution in 2021; 70 million of these ITNs were carried over from 2020. In total, 128 million (75%) ITNs were distributed in 2021. ■ Eight countries – Benin, Eritrea, Indonesia, Nigeria, Solomon Islands, Thailand, Uganda and Vanuatu – had distributed less than 60% of their ITNs. Seven countries – Botswana, the Central African Republic, Chad, Haiti, India, Pakistan and Sierra Leone – did not distribute any of the planned ITNs. India, Nigeria and Uganda – countries supported under the high burden high impact (HBHI) approach – had distributed 0%, 53% and 26%, respectively, of their ITNs planned for distribution in 2021. ■ Results from Round 3 of the essential health services (EHS) pulse survey implemented in November to December 2021 suggest that, in the WHO African Region, disruptions in diagnosis and treatment had eased considerably in the latter part of 2021, with seven countries reporting disruptions, compared with 16 in 2020 and nine in the first quarter of 2021. TRENDS IN THE BURDEN OF MALARIA Malaria cases ■ Globally, there were an estimated 247 million malaria cases in 2021 in 84 malaria endemic countries (including the territory of French Guiana), an increase from 245 million in 2020, with most of this increase coming from countries in the WHO African Region. In 2015, the baseline year of the Global technical strategy for malaria 2016–2030 (GTS), there were an estimated 230 million malaria cases. ■ Malaria case incidence (i.e. cases per 1000 population at risk) reduced from 82 in 2000 to 57 in 2019, before increasing to 59 in 2020. There was no change in case incidence between 2020 and 2021. The increase in 2020 was associated with disruption to services during the COVID-19 pandemic. xix ■ Between 2019 and 2021, an estimated additional 13.4 million cases were attributed to disruptions during the COVID-19 pandemic. ■ The proportion of cases due to P. vivax reduced from about 8% (20.5 million) in 2000 to 2% (4.9 million) in 2021. ■ Twenty-nine countries accounted for 96% of malaria cases globally, and four countries – Nigeria (27%), the Democratic Republic of the Congo (12%), Uganda (5%) and Mozambique (4%) – accounted for almost half of all cases globally. ■ The WHO African Region, with an estimated 234 million cases in 2021, accounted for about 95% of global cases. ■ Between 2000 and 2019, case incidence in the WHO African Region reduced from 373 to 225 per 1000 population at risk, but increased to 234 in 2020, mainly because of disruptions to services during the COVID-19 pandemic. In 2021, case incidence declined to 229 per 1000 population. ■ Cabo Verde reported zero indigenous cases for 3 consecutive years, ending the malaria epidemic. ■ The WHO South-East Asia Region accounted for about 2% of the burden of malaria cases globally. Malaria cases reduced by 76%, from 23 million in 2000 to about 5 million in 2021. Malaria case incidence in this region reduced by 82%, from about 18 cases per 1000 population at risk in 2000 to about three cases per 1000 population at risk in 2021. ■ India accounted for 79% of cases in the region. Sri Lanka was certified malaria free in 2016 and remains malaria free. ■ Between 2020 and 2021, there was an increase of 400 000 cases in the region, with over half of these cases being in Myanmar. ■ Malaria cases in the WHO Eastern Mediterranean Region reduced by 38%, from about 7 million cases in 2000 to about 4 million in 2015. Between 2016 and 2021, cases rose by 44% to 6.2 million. ■ Over the period 2000–2020, malaria case incidence in the WHO Eastern Mediterranean Region declined from 20 to 12 cases per 1000 population at risk. The Sudan is the leading contributor to malaria in this region, accounting for about 54% of cases. In 2021, the Islamic Republic of Iran had no indigenous malaria cases for 4 consecutive years and Saudi Arabia reported zero indigenous cases for the first time. ■ The WHO Western Pacific Region had an estimated 1.4 million cases in 2021, a decrease of 49% from the 3 million cases in 2000. Over the same period, malaria case incidence reduced from four to two cases per 1000 population at risk. Papua New Guinea accounted for nearly 87% of all cases in this region in 2021. China was certified malaria free in 2021 and Malaysia had no cases of non-zoonotic malaria for 4 consecutive years. ■ In the WHO Region of the Americas, malaria cases reduced by 60% (from 1.5 million to 0.6 million) and case incidence by 70% (from 14 to 4) between 2000 and 2021. The region’s progress in recent years has suffered from the major increase in malaria in the Bolivarian Republic of Venezuela, which had about 35 500 cases in 2000 and more than 482 000 cases by 2017. In 2020, cases reduced by more than half compared with 2019, to 223 000, and further in 2021 to 205 000 cases. This decrease was due to restrictions on movement during the COVID-19 pandemic and a shortage of fuel that affected the mining industry, which was the main contributor to the recent increase in malaria in the country. These restrictions may also have affected access to care, leading to a reduction in the number of cases reported from health facilities. ■ Estimated cases more than doubled in Honduras and Panama in 2021 compared with 2019. Over the same period substantial reductions in cases were seen in the Bolivarian Republic of Venezuela (–263 000), Brazil (–17 000), Colombia (–17 000) and Peru (–22 000). ■ The Bolivarian Republic of Venezuela, Brazil and Colombia accounted for more than 79% of all cases in this region. ■ Argentina, El Salvador and Paraguay were certified malaria free in 2019, 2021 and 2018, respectively. Belize reported zero indigenous malaria cases for the third consecutive year, ending the malaria epidemic. ■ Since 2015, the WHO European Region has been free of malaria. xx W O RL D M AL AR IA R EP O RT 2 02 2 Malaria deaths ■ Globally, malaria deaths reduced steadily over the period 2000–2019, from 897 000 in 2000 to 577 000 in 2015 and to 568 000 in 2019. In 2020, malaria deaths increased by 10% compared with 2019, to an estimated 625 000. Estimated deaths declined slightly in 2021 to 619 000. Between 2019 and 2021, there were 63 000 deaths that were due to disruptions to essential malaria services during the COVID-19 pandemic. ■ The percentage of total malaria deaths in children aged under 5 years reduced from 87% in 2000 to 76% in 2015. Since then there has been no change. ■ Globally, the malaria mortality rate (i.e. deaths per 100 000 population at risk) halved from about 30 in 2000 to 15 in 2015; it then continued to decrease but at a slower rate, falling to 14 in 2019. In 2020, the mortality rate increased again, to 15.1, before decreasing slightly to 14.8 in 2021. ■ About 96% of malaria deaths globally were in 29 countries. Four countries accounted for just over half of all malaria deaths globally in 2021: Nigeria (31%), the Democratic Republic of the Congo (13%), the Niger (4%) and the United Republic of Tanzania (4%). ■ Malaria deaths in the WHO African Region decreased from 841 000 in 2000 to 541 000 in 2018, before increasing to 599 000 in 2020. Estimated deaths decreased again to 593 000 in 2021. The malaria mortality rate reduced by 62% between 2000 and 2019, from 148 to 56 per 100 000 population at risk, before rising to 60 in 2020 and decreasing again to 58 in 2021. ■ Cabo Verde has reported zero malaria deaths since 2018. ■ In the WHO South-East Asia Region, malaria deaths reduced by 74%, from about 35 000 in 2000 to 9000 in 2019. The number of deaths has remained the same over the past 3 years. ■ India accounted for about 83% of all malaria deaths in the WHO South-East Asia Region. ■ In the WHO Eastern Mediterranean Region, malaria deaths reduced by 45%, from about 13 600 in 2000 to 7500 in 2014, and then increased by 79% between 2014 and 2021, to 13 400 deaths. Most of the increase was observed in the Sudan, where more than 90% of cases are due to P. falciparum, which is associated with a higher case fatality rate than P. vivax cases. ■ In the WHO Eastern Mediterranean Region, the malaria mortality rate reduced by 60% between 2000 and 2009; however, since 2016, mortality rates have remained largely unchanged with a slight increase of 28% from 2.0 to 2.5 deaths per 100 000 population at risk. ■ In the WHO Western Pacific Region, malaria deaths reduced by 58%, from about 6200 cases in 2000 to 2600 in 2021; the mortality rate reduced by 67% over the same period, from 0.9 to 0.3 malaria deaths per 100 000 population at risk. Papua New Guinea accounted for more than 94% of malaria deaths in 2021. ■ In the WHO Region of the Americas, malaria deaths reduced by 64% (from 919 to 334) and the mortality rate by 73% (from 0.8 to 0.2). Most of the deaths in this region were in adults (78%). Malaria cases and deaths averted ■ Globally, an estimated 2 billion malaria cases and 11.7 million malaria deaths were averted in the period 2000–2021. ■ Most of the cases (82%) and deaths (95%) averted were in the WHO African Region, followed by the WHO South-East Asia Region (cases 10% and deaths 3%). Burden of malaria in pregnancy ■ In 2021, in 38 moderate and high transmission countries in the WHO African Region, there were an estimated 40 million pregnancies, of which 13.3 million (32%) were exposed to malaria infection during pregnancy. ■ By WHO subregion, west Africa had the highest prevalence of exposure to malaria during pregnancy (40.7%), closely followed by central Africa (39.8%), while prevalence was 20% in east and southern Africa. xxi ■ It is estimated that, without a pregnancy-specific intervention, malaria infection during pregnancy in these 38 countries would have resulted in 961 000 children with low birthweight. In the 33 countries in which IPTp was implemented, an estimated 457 000 of these low birthweights were averted. ■ If all of the pregnant women visiting antenatal care (ANC) clinics at least once received a single dose of IPTp – assuming they were all eligible and that second and third doses of IPTp (IPTp2 and IPTp3) remained at current levels – an additional 55 000 low birthweights would have been averted in 33 countries with information on IPTp. ■ If IPTp3 coverage was raised to the same levels as that of ANC first visit coverage, and if subsequent ANC visits were just as high, then an additional 162 000 low birthweights would be averted. ■ If IPTp3 coverage was optimized to 90% of all pregnant women, an additional 265 000 low birthweights would be averted. ■ Given that low birthweight is a strong risk factor for neonatal and childhood mortality, averting a substantial number of low birthweights will save many lives. MALARIA ELIMINATION AND PREVENTION OF RE‑ESTABLISHMENT ■ Progress towards malaria elimination is increasing; in 2021, there were 84 malaria endemic countries compared with 108 in 2000. ■ The number of countries that were malaria endemic in 2000 and that reported fewer than 100 malaria cases increased from 6 in 2000 to 27 in 2021, remaining unchanged in 2021 when compared with 2020. ■ Between 2000 and 2021, the number of countries with fewer than 10 indigenous cases increased from four to 25. ■ In the period 2010–2021, total malaria cases in the E-2025 countries (malaria eliminating countries for 2025) reduced by 82.8%; however, when compared with 2020, these countries and territories experienced a 30.4% increase in 2021. ■ In the WHO South-East Asia Region, there has been a notable rise in the number of humans infected with P. knowlesi in some countries, especially in Malaysia. No indigenous human malaria cases or deaths have been reported in Malaysia for the past 4 years; however, since 2017, a total of 17 125 P. knowlesi cases and 48 deaths have been reported in this country. ■ In 2021 alone, 3575 P. knowlesi cases were reported that resulted in 13 deaths. Over the same period, an additional 435 P. knowlesi cases were reported in the WHO South-East Asia Region, in Indonesia, the Philippines and Thailand. ■ Belize and Cabo Verde reported zero malaria cases for the third consecutive year, with Iran (Islamic Republic of) and Malaysia reporting zero indigenous cases for the fourth consecutive year. In 2021, Timor-Leste, following an outbreak in the previous year, reported zero indigenous malaria cases, and Saudi Arabia reported zero indigenous cases for the first time. ■ Five countries – Azerbaijan, Belize, Cabo Verde, Iran (Islamic Republic of) and Tajikistan – have submitted an official request for malaria free certification. ■ Despite disruptions during the COVID-19 pandemic, 61.5% of E-2025 countries reporting cases continued making progress towards elimination and reduction of the malaria burden. Countries that continued to show reduction in cases in 2021 when compared with 2020 were Bhutan (59.1%), Botswana (20.5%), the Dominican Republic (65.6%), Mexico (32.0%), Nepal (56.2%), the Republic of Korea (23%), Saudi Arabia (100%), South Africa (33.7%), Suriname (85.9%), Thailand (22.3%), Timor- Leste (100%) and Vanuatu (36.7%). xxii W O RL D M AL AR IA R EP O RT 2 02 2 ■ When compared with 2020, the following countries (and one territory) saw an increase in cases in 2021: the Comoros (56.9%), Costa Rica (52.4%), the Democratic People’s Republic of Korea (22.8%), Ecuador (11.1%), Eswatini (53.9%), French Guiana (2.1%), Guatemala (16.9%), Honduras (47.4%), Panama (55.3%) and Sao Tome and Principe (28.9%). Ecuador, for the second consecutive year, reported increases in the number of indigenous cases. ■ Despite the setbacks, many countries continue to make immense progress – between 2011 and 2021, the classification rates within the E-2025 countries increased from 7.6% to 90.8%, demonstrating continued efforts by countries in pursuit of their elimination goals. ■ Between 2000 and 2021, the six countries of the GMS – Cambodia, China (Yunnan Province), the Lao People’s Democratic Republic, Myanmar, Thailand and Viet Nam – together reported a 76.5% decrease in indigenous malaria cases and a 94.1% decline in indigenous P. falciparum malaria cases. The rate of decline has been the fastest since 2012, when the Mekong Malaria Elimination (MME) programme was launched. Between 2012 and 2021, the region saw a staggering decrease in indigenous malaria cases (86.4%) and in indigenous P. falciparum malaria cases (95.7%). In 2021, a total of 90 082 indigenous cases and 16 484 indigenous P. falciparum cases were reported – a 17.3% increase in indigenous malaria cases and a 12.2% decline in indigenous P. falciparum cases when compared with 2020. ■ In 2021, Myanmar saw an increase in numbers of both P. falciparum and P. vivax cases because of the continued political instability in the area. In 2021, Myanmar continued to account for most of the indigenous malaria cases (87.7%) and indigenous P. falciparum malaria cases (80.9%). ■ As P. falciparum cases continue to decline, P. vivax has emerged as the dominant species within the subregion. In 2021, Cambodia reported relapses for the first time; the country reported a total of 1978 relapse cases, making up 48% of the country’s total indigenous cases. ■ Countries in the GMS are leveraging the subnational verification exercise to strengthen their programmes to prevent re-establishment and to prepare for their country’s certification by WHO. ■ Between 2000 and 2021, no country that was certified malaria free has been found to have malaria transmission re-established. HBHI APPROACH ■ Since November 2018, all 11 HBHI countries have implemented HBHI-related activities across the four response elements. ■ In 2020 and 2021, WHO and the RBM Partnership to End Malaria supported countries to implement rapid self-evaluations on progress in the HBHI objectives across the four response elements. ■ In 2021, with the ongoing increase in malaria cases, HBHI countries accounted for 68% of all cases and 70% of deaths globally. ■ Between 2020 and 2021, malaria cases in HBHI countries increased from 163 million to 168 million, while there was a reduction in deaths from 444 600 to 427 854. ■ With the ongoing increases in 2021, the following countries accounted for most of the malaria cases: Nigeria (39.0%), the Democratic Republic of the Congo (18.2%), Uganda (7.8%) and Mozambique (6.1%). ■ Five of the 11 HBHI countries – the Democratic Republic of the Congo, Ghana, India, the Niger and the United Republic of Tanzania – showed a decline in deaths, although their contributions to the malaria burden within HBHI countries is still substantial. xxiii SURVEILLANCE ■ Over 40 countries are using malaria surveillance packages designed for the District Health Information System 2 (DHIS2), and 34 countries have adopted the aggregate malaria module. ■ Standard modules specific for entomology and vector control have also been developed to support countries to improve the collection and use of entomological and vector control intervention data. ■ A suite of tools has been developed or enhanced to support malaria elimination surveillance using the DHIS2 web platform and DHIS2 capture application (app) for Android. These tools can be used for case notification, investigation and response, and for focus investigation and response. ■ A total of 12 countries have started establishing malaria repositories, which are data warehouses for all malaria-related data. ■ WHO has launched the malaria surveillance assessment toolkit, which provides a systematic and standardized way of assessing the performance of surveillance systems. ■ Four countries – Burkina Faso (2020), Cameroon (2021), the Democratic Republic of the Congo (2021) and Ghana (2021) – have undertaken a surveillance system assessment using the toolkit. The assessments from three of these countries that are available for publication (excluding Cameroon) showed that completeness and timeliness of reporting was good, but concordance of core variables between registers and aggregate reports was poor. INVESTMENTS IN MALARIA PROGRAMMES AND RESEARCH ■ The GTS sets out estimates of the funding required to achieve milestones for 2025 and 2030. Total annual resources needed were estimated at US$ 6.8 billion in 2020, rising to US$ 9.3 billion in 2025 and US$ 10.3 billion by 2030. An additional US$ 0.85 billion is estimated to be required annually for global malaria research and development (R&D) during the period 2021–2030. ■ Total funding for malaria control and elimination in 2021 was estimated at US$ 3.5 billion, compared with US$ 3.3 billion in 2020 and US$ 3.0 billion in 2019. The amount invested in 2021 fell short of the US$ 7.3 billion estimated to be required globally to stay on track towards the GTS milestones. ■ The funding gap between the amount invested and the resources needed has continued to widen dramatically over recent years, increasing from US$ 2.6 billion in 2019 to US$ 3.5 billion in 2020 and US$ 3.8 billion in 2021. ■ Over the period 2010–2021, 67% of the total funding for malaria control and elimination, almost US$ 2.4 billion, stemmed from international sources. The United States of America (USA) contributed over US$ 1.3 billion through planned bilateral funding and malaria-adjusted share of multilateral contributions agencies. This was followed by bilateral and multilateral disbursements from the United Kingdom of Great Britain and Northern Ireland (United Kingdom) and Germany of about US$ 0.2 billion; contributions of about US$ 0.1 billion from Canada, France and Japan; and a combined US$ 0.4 billion from other countries that are members of the Development Assistance Committee and from private-sector contributors. ■ Governments of malaria endemic countries contributed more than a third of total funding in 2021, with investments of over US$ 1.1 billion, of which over US$ 0.3 billion was spent on malaria case management in the public sector and US$ 0.8 billion on other malaria control activities, a US$ 0.1 billion increase from 2020. ■ Of the US$ 3.5 billion invested in 2020, over US$ 1.5 billion (44%) was channelled through the Global Fund. Compared with previous years, the Global Fund’s disbursements to malaria endemic countries increased by about US$ 0.1 billion since 2020 and US$ 0.3 billion since 2019. ■ The World Bank’s classifications by income group vary from year to year. In 2021, the 27 low-income group countries accounted for 47% of total funding, experiencing an increase in funding of more xxiv W O RL D M AL AR IA R EP O RT 2 02 2 than 50% since 2010 and representing over 90% of global malaria cases and deaths. The 39 low- and middle-income countries (LMIC) accounted for 41% of total funding in 2021, whereas the remaining countries and unspecified regions where no geographical information on recipients was available accounted for 12% of malaria funding. ■ The assessment of malaria funding per person at risk highlights the variation in domestic and international funding across WHO regions, and has shown sizeable changes over the past decade. Most WHO regions have experienced funding per person at risk falling to levels below that of 2010, apart from the WHO African Region, in which funding doubled in 2021 compared with 2010. ■ Of the US$ 3.5 billion invested in 2021, over three quarters (78%) went to the WHO African Region, 5% each went to the South-East Asia Region and the Eastern Mediterranean Region, 4% to the Region of the Americas and 3% to the Western Pacific Region. The remaining 5% of total funding in 2021 was allocated to unspecified regions. ■ Many countries have experienced changes in their real gross domestic product (GDP) due to the COVID-19 pandemic and other crises; in turn, this has affected the global economy, which expanded by 5.5% in 2021 after a contraction of 3.4% in 2020. In 2021, there was significant growth among the lower income countries and LMIC, with only 11% of such countries experiencing a negative shock to their real GDP growth compared with 70% in 2020. Despite the continued increase in funding for malaria, the actual impact of the COVID-19 pandemic, the associated economic crisis, other global factors and the increase in population at risk will continue to unfold in the coming years. ■ Total R&D funding in malaria was US$ 626 million in 2021. ■ This is the third consecutive year of funding decline since its 2018 peak, with malaria R&D funding declining across almost all product categories. In particular, the Bill & Melinda Gates Foundation reduced its investment; however, it remained the third largest overall funder of malaria vaccine R&D, behind only the US National Institutes of Health (NIH) and industry. ■ Basic research appeared to experience the largest absolute decline in funding in 2021 (–12%) and vector control production saw a decrease of just under 20% since 2020. Diagnostics funding received 2.5% of total malaria funding in 2021, its lowest share since 2013. Only R&D for therapeutics bucked the overall downward trend, with drug R&D rising by 2.3% and biologics by 7.0%. ■ The US NIH remained the top funder for malaria R&D in 2021, with funding remaining stable at US$ 189 million, followed by industry and the Bill & Melinda Gates Foundation. DISTRIBUTION AND COVERAGE OF MALARIA PREVENTION ■ Manufacturers’ delivery data for 2004–2021 show that almost 2.5 billion ITNs were supplied globally in that period, of which 2.2 billion (87%) were supplied to sub-Saharan Africa. ■ Manufacturers delivered about 220 million ITNs to malaria endemic countries in 2021. Of these, 46% were pyrethroid–piperonyl butoxide (PBO) nets and 9% were dual active ingredient ITNs. ■ By 2021, 68% of households in sub-Saharan Africa had at least one ITN, an increase from about 5% in 2000. The percentage of households owning at least one ITN for every two people increased from 1% in 2000 to 38% in 2021. In the same period, the percentage of the population with access to an ITN within their household increased from 3% to 54%. ■ The percentage of the population sleeping under an ITN also increased considerably between 2000 and 2021, for the whole population (from 2% to 47%), for children aged under 5 years (from 3% to 53%) and for pregnant women (from 3% to 53%). ■ Overall, access to and use of ITNs remains below the levels observed in 2017. ■ Globally, the percentage of the population at risk protected by indoor residual spraying (IRS) in malaria endemic countries declined from 5.5% in 2010 to 2.4% in 2021. The percentage of the population protected by IRS has remained stable since 2016, with less than 6% of the population protected in each WHO region. xxv ■ The number of people protected by IRS globally fell from 153 million in 2010 to 112 million in 2015, and further declined to 80 million in 2021. ■ The average number of children treated per cycle of SMC increased from about 0.2 million in 2012 to almost 45 million in 2021. ■ The total number of treatment doses delivered in the 15 countries implementing SMC in 2021 was about 180 million. ■ Using data from 33 countries in the WHO African Region, the percentage of IPTp use by dose was computed. In 2021, 72% of pregnant women used ANC services at least once during their pregnancy. About 55% of pregnant women received one dose of IPTp, 45% received two doses and 35% received three doses. DISTRIBUTION AND COVERAGE OF MALARIA DIAGNOSIS AND TREATMENT ■ Globally, 3.5 billion rapid diagnostic tests (RDTs) for malaria were sold by manufacturers in 2010-2021, with almost 82% of these sales being in sub-Saharan African countries. In the same period, national malaria programmes (NMPs) distributed 2.4 billion RDTs – 88% in sub-Saharan Africa. ■ In 2021, 413 million RDTs were sold by manufacturers and 262 million were distributed by NMPs. ■ Almost 3.8 billion treatment courses of ACT were delivered globally by manufacturers in 2010–2021. About 2.6 billion of these deliveries were to the public sector in malaria endemic countries; the rest were either public-sector or private-sector Affordable Medicines Facility–malaria (AMFm) or Global Fund co-payment mechanisms, or reflect deliveries to the private sector outside the Global Fund co-payment mechanism for the relevant years. ■ National data reported by NMPs in 2010–2021 show that 2.4 billion ACTs were delivered to health service providers to treat malaria patients in the public health sector. ■ In 2021, some 225 million ACTs were delivered by manufacturers to the public health sector; in that same year, 242 million ACTs were distributed to this sector by NMPs, of which 97% were in sub-Saharan Africa. ■ Aggregated data from household surveys conducted in sub-Saharan Africa between 2005 and 2021 in 20 countries with at least two surveys (baseline 2005–2011, and most recent 2015–2021) were used to analyse coverage of treatment seeking, diagnosis and use of ACTs in children aged under 5 years. ■ Comparing the baseline and latest surveys, there was little change in prevalence of fever within the 2 weeks preceding the surveys (median 25% versus 20%) or in treatment seeking for fever (median 65% versus 67%). ■ Comparisons of the source of treatment between the baseline and more recent surveys show that the proportion who received care from public health facilities increased from a median of 58% to 69%, the proportion who received care from the private sector decreased from a median of 40% to 28%, and the use of community health workers remained low, with medians of 2% and 1%, respectively. ■ The rate of diagnosis among children aged under 5 years with fever and for whom care was sought increased considerably, from a median of 30% at baseline to 57% in the latest household surveys. ■ Use of ACTs among those for whom care was sought also increased, from 14% at baseline to 24% in the latest surveys. ■ Among those for whom care was sought and who received a finger or heel prick, use of ACTs was 29% in the most recent survey, compared with 21% at baseline. xxvi W O RL D M AL AR IA R EP O RT 2 02 2 PROGRESS TOWARDS THE GTS MILESTONES OF 2020 ■ The GTS aims for a reduction in malaria case incidence and mortality rate of at least 75% by 2025 and 90% by 2030 from a 2015 baseline. In 2020, the GTS target was a reduction of 40% by 2020. ■ The number of countries that achieved the GTS targets for 2021 was derived from official burden estimates, rather than from projections (as was done in the World malaria report 2020). ■ In 2020 and 2021, the estimates included the effect of disruptions of malaria services during the pandemic and were based on a new method for quantifying the malaria cause of death (CoD) fraction. ■ Despite the considerable progress made since 2000, the GTS 2020 milestones for morbidity and mortality were not achieved globally. If the current trends continue, the GTS 2025 targets will not be achieved globally. ■ The malaria case incidence of 59 cases per 1000 population at risk in 2021 instead of the expected 31 cases per 1000 if the world were on track for the 2021 GTS morbidity milestone means that, globally, we are off track by 48%. ■ Although relative progress in the mortality rate is greater than that of case incidence, the GTS target of 7.8 malaria deaths per 100 000 population at risk in 2021 was 48% lower than the mortality rate of 14.8 observed in the same year. ■ Of the 93 countries that were malaria endemic (including the territory of French Guiana) globally in 2015, 39 (42%) met the GTS morbidity milestone for 2021, having achieved a reduction of 40% or more in case incidence or having reported zero malaria cases. ■ Nineteen countries (20%) had made progress in reducing malaria case incidence but by less than the expected target. ■ Twenty-seven countries (29%) had increased case incidence and 14 countries (15%) had an increase of 40% or more in malaria case incidence in 2021 compared with 2015. ■ In eight countries (8.6%), malaria case incidence in 2021 was similar to that of 2015. ■ Forty-three countries (46%) that were malaria endemic in 2015 achieved the GTS mortality milestone for 2021, with 28 of them reporting zero malaria deaths. ■ Twenty-two countries (24%) achieved reductions in malaria mortality rates but of less than the 40% target. ■ Malaria mortality rates remained at the same level in 2021 as they were in 2015 in nine countries (9.7%), whereas mortality rates increased in 19 countries (20%), 11 of which had increases of 40% or more. ■ The WHO South-East Asia Region met the GTS 2020 milestones for both mortality and morbidity. All countries in the region except Indonesia reduced case incidence and mortality by 40% or more. BIOLOGICAL AND OTHER THREATS TO MALARIA INTERVENTION TOOLS Parasite deletions of Pfhrp2/3 genes ■ Parasites that cannot express histidine-rich protein 2 (HRP2) may not be detectable by RDTs based on HRP2, and those that no longer express both HRP2 and histidine-rich protein 3 (HRP3) are completely undetectable by such RDTs. ■ WHO has recommended that countries with reports of Pfhrp2/3 deletions, and their neighbouring countries, should conduct representative baseline surveys among suspected malaria cases, to determine whether the prevalence of Pfhrp2/3 deletions causing false negative RDT results has reached a threshold that requires a change in RDT (>5% Pfhrp2 deletions causing false negative RDT results). xxvii ■ Alternative RDT options (e.g. based on detection of the parasite’s lactate dehydrogenase) are limited; in particular, there are currently no WHO-prequalified non-HRP2 combination tests that can detect and distinguish between P. falciparum and P. vivax. ■ WHO is tracking published reports of Pfhrp2/3 deletions using the Malaria Threats Map application and is encouraging a harmonized approach to mapping and reporting of Pfhrp2/3 deletions through publicly available survey protocols. ■ In 2022, several countries in the WHO African Region began planning or implementing representative surveys for Pfhrp2/3 deletions; results are expected in 2022 and 2023. ■ WHO has launched a dashboard for tracking surveillance activities globally to inform priorities and resource allocation and RDT forecasting, and to avoid duplication of efforts. The dashboard features key characteristics of surveillance activities as well as timelines. ■ Between September 2021 and September 2022, investigations of Pfhrp2/3 deletions were reported in 17 publications from 17 countries: Benin, Brazil, Cameroon, the Democratic Republic of the Congo, Djibouti, Ecuador, Equatorial Guinea, Eritrea, Ethiopia, Gabon, Ghana, India, Kenya, Madagascar, Rwanda, Sierra Leone and the United Republic of Tanzania. Of these, only Equatorial Guinea, Kenya and Rwanda did not identify any Pfhrp2 deletions, although deletions in these three countries have been reported in previous publications. ■ Based on data from publications included in the Malaria Threats Map, some form of investigation has been conducted in 47 countries, with the presence of deletions being confirmed in 40 of these countries. ■ The WHO Global Response Plan for Pfhrp2/3 deletions outlines several areas for action beyond scaling up of surveillance. These other areas for action include identifying new biomarkers, improving the performance of non-HRP2 RDTs, undertaking market forecasting and strengthening laboratory networks to support the demand for using molecular characterization to determine the presence or absence of these gene deletions. Parasite resistance to antimalarial drugs ■ Antimalarial drug efficacy is monitored through therapeutic efficacy studies (TES), which track clinical and parasitological outcomes among patients receiving antimalarial treatment. TES are considered the gold standard by which countries can best determine their national treatment policies. ■ Antimalarial drug resistance can be assessed using several tools. For some drugs, genetic changes associated with reduced drug sensitivity have been identified. Artemisinin partial resistance is monitored using an established list of validated and candidate PfKelch13 markers associated with delayed clearance after a treatment containing artemisinin. ■ WHO collates results of studies on antimalarial drug efficacy and resistance, and makes them available in the Malaria Threats Map. ■ WHO African Region: A total of 266 P. falciparum TES were conducted in the WHO African Region according to the WHO standard protocol between 2015 and 2021 enrolling at least 20 patients. Among these TES, six studies have demonstrated a greater than 10% failure rate: four studies with artemether-lumefantrine (AL) in Burkina Faso and in Uganda, and two studies with dihydroartemisinin-piperaquine (DHA-PPQ) in Burkina Faso. A further five studies with AL and two studies with DHA-PPQ using a methodology to distinguish between reinfection and recrudescence that differs from the WHO-recommended methodology reported treatment failure rates greater than 10%; these include studies in Angola, the Democratic Republic of the Congo and Uganda. PfKelch13 mutations have emerged in Eritrea, Rwanda and Uganda. Treatment failure rates remain below 10%, because the partner drug is still effective. Further studies are needed to determine the extent of the spread of the PfKelch13 mutations and to investigate any changes to parasite clearance time and in vitro resistance. ■ WHO Region of the Americas: Limited TES data are available from the WHO Region of the Americas. TES of AL conducted between 2015 and 2021 in Brazil and Colombia demonstrated high xxviii W O RL D M AL AR IA R EP O RT 2 02 2 efficacy. In Guyana, the PfKelch13 C580Y mutation associated with artemisinin partial resistance was sporadically observed between 2010 and 2017 but has not been found in any of the more recent samples, indicating that the mutation has probably disappeared. ■ WHO Eastern Mediterranean Region: Of 36 P. falciparum TES conducted in the WHO Eastern Mediterranean Region enrolling at least 20 patients, two studies with artesunate plus SP (AS+SP) in Somalia and the Sudan demonstrated a greater than 10% failure rate. In these countries, the first-line treatment was subsequently changed to AL. ■ WHO South-East Asia Region: Of 67 P. falciparum TES conducted in the WHO South-East Asia Region enrolling at least 20 patients, no TES reported a greater than 10% treatment failure rate. However, the presence of mutations associated with resistance to SP in central India could be an early warning sign before failure of AS+SP. In Thailand, where drug efficacy is assessed with integrated drug efficacy surveillance, treatment failure rates with DHA-PPQ plus primaquine were found to be high in Sisaket province. This led the province to change its first-line therapy to artesunate-pyronaridine (AS-PY) in 2020. In the GMS, PfKelch13 mutations associated with artemisinin partial resistance are at a high prevalence in Myanmar and Thailand. ■ WHO Western Pacific Region: Of 63 P. falciparum TES conducted in the WHO Western Pacific Region enrolling at least 20 patients, 14 studies demonstrated a greater than 10% failure rate. One study with AL in the Lao People’s Democratic Republic in 2017 found high failure rates (17.2%) but this study had only 29 patients and later studies found high AL efficacy. Two studies in Cambodia found high failure rates with artesunate-amodiaquine (AS-AQ), indicating the presence of AQ resistance in Cambodia. High rates of treatment failure were detected with DHA-PPQ in Cambodia, the Lao People’s Democratic Republic and Viet Nam, prompting changes from the use of this drug as first-line treatment. In the GMS, PfKelch13 mutations associated with artemisinin partial resistance are at a high prevalence in Cambodia, the Lao People’s Democratic Republic and Viet Nam. Additionally, in Papua New Guinea, the PfKelch13 C580Y mutation has emerged and appears to be spreading. Vector resistance to insecticides ■ From 2010 to 2020, 88 countries reported data to WHO on standard insecticide resistance monitoring, including 38 on the intensity of resistance to pyrethroids, and 32 on the ability of PBO to restore susceptibility to pyrethroids. ■ In 2020, new discriminating concentrations and procedures for monitoring resistance in malaria vectors against chlorfenapyr, clothianidin, transfluthrin, flupyradifurone and pyriproxyfen became available, and discriminating concentrations for pirimiphos-methyl and alpha-cypermethrin were revised. Countries should adjust their monitoring of insecticide resistance in malaria vectors to align with these new procedures. WHO has not received any vector resistance monitoring data for transfluthrin, flupyradifurone and pyriproxyfen. Although WHO has received some resistance monitoring data for chlorfenapyr and clothianidin, these data are insufficient to assess the potential presence of resistance to either of these two insecticides. ■ Of the 88 malaria endemic countries that provided data for 2010–2020, 78 have detected resistance to at least one insecticide class in at least one malaria vector and one collection site; 29 have already detected resistance to pyrethroids, organochlorines, carbamates and organophosphates across different sites; and 19 have confirmed resistance to all these four classes in at least one site and at least one local vector. ■ Globally, resistance to pyrethroids – the primary insecticide class currently used in ITNs – is widespread, having been detected in at least one malaria vector in 68% of the sites for which data were available. Resistance to organochlorines was reported in 64% of the sites. Resistance to carbamates and organophosphates was less prevalent, being detected in 34% and 28% of the sites that reported monitoring data, respectively. ■ Of the 38 countries that reported data on the intensity of pyrethroid resistance, high intensity resistance was detected in 27 countries and 293 sites. xxix ■ Since 2010, PBO has been observed to fully restore susceptibility in 283 sites across 29 countries. ■ To guide resistance management, countries should develop and implement national insecticide resistance monitoring and management plans, drawing on the WHO Framework for a national plan for monitoring and management of insecticide resistance in malaria vectors. The number of countries that reported having such a plan increased from 53 in 2019 to 67 in 2020. ■ Technical and funding support is required to support countries to monitor and manage insecticide resistance. ■ Standard insecticide resistance data reported to WHO are included in the WHO global database on insecticide resistance in malaria vectors and can be explored via the Malaria Threats Map. Effectiveness of ITNs ■ ITNs are considered the main drivers of the declines in malaria transmission and burden in the period 2005–2015, especially in settings with moderate to high transmission. Long-lasting insecticidal nets (LLINs) remain effective and WHO encourages their continued use to prevent malaria. ■ Given that LLINs are the main vector control tool, the factors that impair their effectiveness in malaria prevention are important in progress against malaria. These factors include the physical durability of the net (i.e. fabric integrity) and its chemical durability (i.e. bioefficacy, which is the availability of the active ingredient on the surface of the net over time), operational and behavioural constraints (i.e. delivery, access, coverage and acceptability, use, maintenance and retention), and vector dynamics (species biting and resting behaviours). ■ The emergence and wide geographical spread of pyrethroid resistance is the most recognized threat to the effectiveness of long-lasting pyrethroid-based ITNs. ■ A multicountry trial study commissioned by WHO and published in 2018 showed that ITNs remained highly protective against malaria, even in the presence of high pyrethroid resistance. ■ In contrast, data from several experimental hut studies suggest that, as vector susceptibility to pyrethroids falls, the repellent and mortality effects on mosquitoes are greatly reduced. ■ Modelling analysis further suggests that the epidemiological impact of ITNs is reduced at high levels of pyrethroid resistance. ■ The greater efficacy seen in randomized controlled trials of some of the newer generation of ITNs (compared with pyrethroid-only ITNs) suggests that insecticide resistance is having an effect on epidemiological outcomes. ■ In sub-Saharan Africa, where most ITNs are distributed, about 590 million ITNs were delivered to communities in the period 2019–2021. However, in 2021, the estimated percentage of the population with access to an ITN within their household and the percentage of the population sleeping under an ITN was 54% and 47%, respectively, owing to several factors. ■ Factors affecting universal ITN ownership and use include allocation efficiency, retention and use. Equitable allocation of ITNs is supported by identifying coverage gaps at the local level and expanding distribution to these areas. Retention of ITNs is determined by the household’s attitudes towards their nets, net handling behaviours and other hazards, and durability of the net fabric and construction. The median lifespan of current ITNs, while variable by setting, is about 1.9 years. Finally, even when nets are available to household members, they are not used 100% of the time; variation in usage is affected by age, season, gender and malaria risk. ■ An illustrative modelling analysis was undertaken. The analysis started with an “ideal” LLIN (i.e. one that is perfectly allocated, never discarded, always used, highly insecticidal and highly durable), and explored scenarios in which each of the constraints listed above was re-introduced sequentially. ■ The analysis showed that the main factors in reducing net effectiveness are the waning of insecticide efficacy and physical durability. When combined with barriers to ITN access, use rate and retention, the impact of an ITN campaign wanes dramatically by the end of the third year. xxx W O RL D M AL AR IA R EP O RT 2 02 2 ■ Solutions are needed to improve the physical and chemical durability of LLINs, their allocation efficiency, and their maintenance and use by household members. In addition, dual active ingredient nets or fully non-pyrethroid LLINs are essential to mitigate the impact of widespread insecticide resistance. Effectiveness of IRS ■ IRS is the second most widely implemented vector control intervention by NMPs. There is, however, limited trial evidence of the efficacy of IRS. ■ From observation data, when IRS is carried out correctly it is a powerful intervention to reduce adult mosquito vector density and longevity, and thus to reduce malaria transmission. ■ Some of the factors that influence IRS effectiveness are similar to those that affect ITNs. For example, most of the vector population feeds and rests indoors, people mainly sleep indoors at night and there are high levels of acceptance in the community. Factors specific to IRS include the timing of spraying and the level of training of spray operators (which affects the quality of spraying). ■ Implementation of IRS at the required quality is logistically more challenging and considerably more expensive than distribution of ITNs. ■ A recent review showed that, compared with ITNs, IRS was about five times more expensive per person protected per year, meaning that IRS is considerably less cost effective. ■ A study in Ethiopia suggests that community-based delivery of IRS (in which spray operators and materials are closer to the households and engagement of community health workers) may reduce costs, improve performance efficiencies and increase coverage without reducing performance. ■ Even where community-based delivery is used, overall costs remain, and when added to the cost for the maintenance of equipment and the sustained supervision of sprayers, it is clear that widespread scale-up of IRS requires a large budget. ■ Costs are likely to increase further as resistance to the insecticides used in IRS emerges and more expensive chemicals are needed. Beyond the reach of ITNs and IRS: addressing residual transmission ■ Beyond the genetic adaptation of mosquitoes to insecticides (i.e. insecticide resistance), an understanding of the behavioural adaptation of the mosquito – in terms of changes to biting, feeding and resting behaviour – is equally important. ■ There is increasing evidence of the vector’s attempts to reduce contact with insecticides (i.e. biting earlier before people go to bed, spending more time resting outdoors or feeding on livestock instead of humans). ■ Vectors with this behavioural plasticity become more dominant than those with limited behavioural changes. Behavioural adaptation on the part of the vector will require investment in R&D of interventions that target outdoor transmission. MALARIA R&D AND PRODUCTS IN THE PIPELINE Preferred product characteristics and target product profiles ■ The WHO Global Malaria Programme (WHO/GMP) has been involved in the development of preferred product characteristics (PPCs) and target product profiles (TPPs), as key tools to incentivize and guide the development of products with high public health impact and suitability for use in LMIC. xxxi ■ WHO is coordinating the development of TPPs for glucose-6-phosphate dehydrogenase (G6PD) point-of-care tests to meet the needs for P. vivax control. ■ Building on the RTS,S malaria vaccine experience, WHO published updated malaria vaccine PPCs in 2022. The PPCs include an expanded set of strategic goals for malaria vaccine R&D for the prevention of blood-stage infection, reduction of morbidity and mortality, and reduction of community-level transmission. ■ WHO has also developed several PPCs for vector control including those for ITNs for malaria transmission control in insecticide-resistant mosquito populations (published in 2021), for vector control interventions to control malaria in complex humanitarian emergencies and in response to natural disasters (published in 2021), for endectocide and ectocide products for malaria transmission control (published in 2022) and for indoor residual surface treatment for malaria transmission control in areas with insecticide-resistant mosquitoes (published in 2022). ■ For chemoprevention, WHO has developed PPCs for malaria chemoprevention drugs aimed at addressing several use-cases: chemoprevention in children (SMC, PMC [previously known as intermittent preventive treatment in infants, or IPTi], IPTsc and PDMC), during pregnancy (IPTp) and in nonimmune travellers. ■ In November 2021, WHO/GMP and the WHO Department of Immunization, Vaccines and Biologicals (IVB) convened a scientific development group to develop PPCs and address clinical development considerations for monoclonal antibodies (mAbs) for malaria prevention. The most immediate public health priority is the reduction of morbidity and mortality in infants and children due to P. falciparum. ■ WHO has also worked in close coordination with product development partners such as the Foundation for Innovative New Diagnostics (FIND), the Medicines for Malaria Venture (MMV) and the Innovative Vector Control Consortium (IVCC) to assemble a database of malaria products in the R&D pipeline. Diagnostic tests and antimalarial medicines ■ On malaria diagnostic tests, the spread of P. falciparum parasites with Pfhrp2/3 gene deletions presents a major threat to reliable diagnosis; a diversified diagnostic landscape is needed to address this liability. Key R&D challenges are the inadequate sensitivity of tools for non-P. falciparum species and a broader range of diagnostic use-cases (e.g. highly sensitive noninvasive screening for the prevention of re-introduction). ■ The current R&D pipeline includes products to address these gaps through efforts to improve existing point-of-care platforms (specifically, microscopy and the RDTs lateral-flow immune- chromatographic antigen-detection tests) and develop alternatives that use a range of sample types and technologies. ■ The current focus of the malaria medicine R&D is on the development of next-generation life- saving medicines for adults, particularly pregnant women, and children who are at elevated risk from the consequences of malarial infection. Another high priority is delivering a non-ACT treatment option, to simplify therapy or as a contingency against the intensification of either artemisinin or the partner drug resistance. ■ Under MMV coordination, the R&D in malaria medicines has focused on two main areas: innovations with existing antimalarial treatments and next-generation antimalarial treatments. In relation to existing treatments, work is ongoing on triple ACT, ACT plus single low-dose primaquine for transmission blocking and artemether-lumefantrine for neonates. ■ There are several next-generation medicines at different trial phases: ganaplacide-lumefantrine, M5717-pyronaridine, ZY19489-ferroquine and cipargamin. In addition, there are seven next- generation molecules under investigation. xxxii W O RL D M AL AR IA R EP O RT 2 02 2 Vector control ■ The WHO Global Observatory on Health Research and Development (GOHRD) currently lists 28 vector control products in the R&D pipeline. Eleven (39%) of these products are ITNs and seven (25%) are indoor residual wall treatments. Thirteen products (46%) are in the data-generation stage to support assessment of safety, quality and entomological efficacy. Seven products (25%) are undergoing epidemiological trials, six (21%) are being assessed by WHO to inform prequalification listing or WHO policy recommendation and two (7%) are at the prototype development stage. ■ IVCC provided an overview of the current and previous malaria intervention products pipeline that lists several novel and repurposed insecticides with different modes of action, for use in ITNs and IRS, to support best practice insecticide resistance management. ■ Among the products that IVCC has helped bring to market for IRS are pirimiphos-methyl (listed by the WHO Pesticide Evaluation Scheme [WHOPES] in January 2013), clothianidin (prequalified by WHO in 2017) and clothianidin plus deltamethrin (prequalified by WHO in 2018). ■ In addition, chlorfenapyr and broflanilide are under review and are expected to be listed for prequalification in the near future. ■ Interceptor® G2, a new ITN, combines a pyrethroid with a repurposed insecticide, chlorfenapyr, which has a different mode of action. The mixture of these two active ingredients is a step forward in insecticide resistance. Three further active ingredients are either in late-stage research or have progressed to full development and are expected to obtain a prequalification listing between 2026 and 2030. ■ An expanded vector control toolbox is under development that includes larviciding, lethal house lures (eave tubes) and Attractive Targeted Sugar Bait. These tools are integral to managing insecticide resistance and reducing outdoor biting. Vaccines ■ The malaria vaccines currently in clinical development target the pre-erythrocytic phase of the parasite; that is, they target the sporozoite or liver stage of the parasite, the parasite blood stage, the gametocytes or the parasite development in the mosquito. ■ Detailed information can be found in the vaccine dashboard of the GOHRD. In brief, of the vaccine candidates under development, six target the parasite at the pre-erythrocytic stage (P. falciparum), three target the blood stage (P. falciparum), and six target the sexual stage or block transmission (of these, four target P. vivax and two target P. falciparum malaria in pregnancy). xxxiii Dr Tedros Adhanom Ghebreyesus Directeur général de l’Organisation mondiale de la Santé (OMS) Chaque année, le Rapport sur le paludisme dans le monde met en lumière les progrès et les lacunes des efforts antipaludiques déployés au niveau mondial. Le rapport de cette année s’articule autour de quatre thèmes : riposte, risques, résilience et recherche. Les pays touchés par le paludisme continuent de relever une multitude de défis, notamment les perturbations liées à la pandémie de COVID 19. Bien que durement touchés, la plupart des pays ont tenu bon et ont pu maintenir les services de prévention, de détection et de traitement du paludisme, un exploit remarquable en pleine pandémie. Néanmoins, plus de 600 000 personnes meurent encore chaque année du paludisme, et la grande majorité sont des enfants. Malgré les efforts héroïques déployés pour maintenir les services pendant la pandémie, la lutte contre le paludisme se heurte à de nombreux obstacles qui viennent s’ajouter aux perturbations déjà importantes liées à la COVID et aux autres problèmes des systèmes de santé, notamment des crises humanitaires interminables, des financements limités de la part des bailleurs de fonds et l’effet potentiel du changement climatique sur la propagation de la maladie. Toutes ces difficultés ont été aggravées par la baisse d’efficacité de nos principaux outils antipaludiques. La résistance croissante aux moustiquaires imprégnées d’insecticide et aux médicaments antipaludiques reste très préoccupante, surtout en Afrique. Dans certaines zones, les parasites du paludisme échappent à la détection des tests les plus couramment utilisés. En outre, l’invasion d’un moustique qui s’adapte facilement aux environnements urbains, Anopheles stephensi, présente un risque réel. Pour soutenir la résilience des programmes de lutte contre le paludisme, l’OMS a publié de nouvelles orientations, notamment une nouvelle stratégie pour contenir la résistance aux médicaments antipaludiques en Afrique, un nouveau cadre développé conjointement par l’OMS et ONU Habitat pour guider les responsables municipaux dans leur lutte contre le paludisme en milieu urbain, ainsi que des recommandations pour stopper la propagation d’Anopheles stephensi en milieu urbain. Les pays sont encouragés à adapter les orientations de l’OMS aux conditions locales. La lutte contre le paludisme est intimement liée à l’ensemble du système de santé. Pour que la lutte contre le paludisme soit efficace, il est essentiel d’investir dans des systèmes de santé qui fonctionnement bien, qui sont équitables et résilients, et qui reposent sur des soins de santé primaires. Avant-propos xxxiv W O RL D M AL AR IA R EP O RT 2 02 2 La recherche et le développement jouent également un rôle essentiel. Comme décrit dans ce rapport, de nouveaux types de technologies de lutte antivectorielle sont en cours de développement, notamment une nouvelle génération de moustiquaires pour combattre la résistance des moustiques. De nouveaux diagnostics et des innovations dans les médicaments antipaludiques sont également en préparation. Une autre avancée majeure est le vaccin antipaludique RTS,S, le premier et le seul vaccin recommandé pour prévenir le paludisme chez les enfants. Plus d’1,2 million d’enfants sont protégés par ce vaccin au Ghana, au Kenya et au Malawi, et le vaccin sera bientôt étendu à plusieurs autres pays. D’autres outils antipaludiques prometteurs sont en cours de développement. Toutefois, la recherche fondamentale et le développement de produits continuent de souffrir d’un important déficit de financement. Il est urgent d’accélérer les investissements dans de nouveaux outils antipaludiques, afin de faire face aux menaces émergentes telles que le paludisme en milieu urbain et la propagation de la résistance aux médicaments antipaludiques. De nombreux défis nous attendent, mais les raisons d’espérer ne manquent pas. En renforçant la réponse, en comprenant et en atténuant les risques, en développant la résilience et en accélérant la recherche, nous avons toutes les raisons de rêver à un avenir sans paludisme. xxxv Le rapport de cette année en un clin d’œil ÉVÉNEMENTS CLÉS EN 2021‑2022 Septième reconstitution des ressources du Fonds mondial de lutte contre le sida, la tuberculose et le paludisme ■ En 2021, plus de 40 % des investissements mondiaux dans la lutte contre le paludisme ont transité par le Fonds mondial de lutte contre le sida, la tuberculose et le paludisme (Fonds mondial). ■ Au total, US$ 15,9 milliards ont été réunis en septembre 2022 par les différents pays et partenaires dans le cadre de la reconstitution des ressources du Fonds mondial, le montant le plus important dans l’histoire du Fonds mondial. ■ Cette collecte de fonds reste néanmoins en deçà de l’objectif de US$ 18 milliards, dans un contexte d’augmentation du prix des produits de santé du fait de la pandémie de coronavirus (COVID-19), des conflits et de la récession mondiale. ■ À cause de ce déficit, une pression considérable va peser sur les ressources reconstituées en vue de maintenir, voire d’élever les niveaux de couverture des interventions antipaludiques. ■ Pour optimiser l’impact de ressources financières limitées, il va donc falloir utiliser les fonds pour la lutte contre le paludisme et les systèmes de santé de manière plus efficace, efficiente et équitable. ■ Les pays doivent être en mesure d’identifier la meilleure combinaison d’interventions adaptées au contexte local et le meilleur moyen de les fournir à ceux qui en ont besoin, tout en tenant compte des données locales. Nouvelles recommandations de l’Organisation mondiale de la Santé ■ En juin 2022, l’Organisation mondiale de la Santé (OMS) a publié des lignes directrices concernant ses recommandations nouvelles ou mises à jour. Ces lignes directrices encouragent les pays à adapter les recommandations au contexte local pour un impact maximal. ■ Les lignes directrices misent à jour incluent des recommandations sur le traitement préventif intermittent du paludisme pendant la grossesse (TPIp), la chimioprévention du paludisme pérenne (CPP) et la chimioprévention du paludisme saisonnier (CPS), le traitement préventif intermittent chez les enfants d’âge scolaire (TPIsc), la chimioprévention du paludisme après la sortie de l’établissement de soins (PDMC), l’administration de masse de médicaments (AMD) et l’élimination du paludisme. ■ L’utilisation du TPIp par sulfadoxine-pyriméthamine (SP) dans les zones de transmission modérée à élevée du paludisme à Plasmodium falciparum reste fortement recommandée. ■ L’utilisation du TPIp par SP est désormais recommandée pour toutes les femmes enceintes, indifféremment du nombre de grossesses qu’elles ont menées. ■ L’actualisation de la recommandation concernant la CPP et la CPS plaide pour un usage plus large de la chimioprévention chez les jeunes enfants présentant un risque élevé de paludisme grave dans les zones de transmission saisonnière ou pérenne. ■ L’OMS a également énoncé une nouvelle recommandation sur l’utilisation du TPIsc dans les zones de transmission modérée à élevée, qu’elle soit pérenne ou saisonnière. Le TPIsc est censé couvrir les enfants âgés de 5 à 15 ans. Son introduction ne doit pas compromettre les interventions de chimioprévention auprès des enfants de moins de 5 ans, qui présentent le risque le plus élevé de paludisme grave. xxxvi W O RL D M AL AR IA R EP O RT 2 02 2 ■ La PDMC est aujourd’hui recommandée en prévention du paludisme chez les enfants souffrant d’anémie grave et qui vivent dans des zones de transmission modérée à élevée, suite à leur sortie de l’hôpital, lorsqu’ils présentent un risque important de réadmission ou de décès. ■ Les nouvelles recommandations concernant l’administration de masse de médicaments antipaludiques portent sur la réduction rapide du poids du paludisme dans les situations d’urgence et dans les zones de transmission modérée à élevée. Elles donnent également des orientations sur l’utilisation de l’AMD pour réduire le paludisme à P. falciparum dans les zones de transmission très faible à faible, ainsi que pour réduire la transmission du paludisme à P. vivax. ■ Les recommandations concernant l’élimination intègrent des stratégies « de masse » appliquées à l’ensemble de la population d’une zone géographique définie (qu’il s’agisse d’un village, d’un canton ou d’un district), des stratégies « ciblées » appliquées aux personnes présentant un plus grand risque d’infection que le reste de la population, ainsi que des stratégies « réactives » déclenchées en réponse à des cas individuels. Nouvelles initiatives régionales lancées par l’OMS en collaboration avec des partenaires Stratégie de riposte face à la résistance aux médicaments antipaludiques en Afrique ■ L’émergence d’une résistance partielle à l’artémisinine dans la région Afrique de l’OMS et la propagation manifestement rapide des mutations associées à cette résistance partielle sont inquiétantes. Cette émergence ne s’est pas répandue depuis la sous-région du Grand Mékong, mais est apparue de novo sur le continent. ■ Les combinaisons thérapeutiques à base d’artémisinine (ACT) demeurent efficaces. Même si la résistance aux médicaments partenaires des ACT actuellement utilisés n’a pas été confirmée, il va s’avérer nécessaire de se pencher sur certains signaux préoccupants et prendre des mesures avant que l’effet des ACT ne commence à s’estomper. ■ Compte tenu de la forte dépendance aux ACT en Afrique, la menace d’une résistance partielle à l’artémisinine et d’une résistance aux médicaments partenaires doit être surveillée et traitée d’urgence. ■ En novembre 2022, l’OMS a lancé la Stratégie de riposte face à la résistance aux médicaments antipaludiques en Afrique dans le but de définir des orientations pour les principaux intervenants de la communauté antipaludique. ■ Cette stratégie apporte une réponse à la menace que représente la résistance aux médicaments antipaludiques dans la région Afrique de l’OMS et s’articule autour de quatre piliers : ■ Pilier I - Renforcer la surveillance de l’efficacité des médicaments antipaludiques et de la résistance à ces médicaments. ■ Pilier II - Optimiser et mieux réglementer, par l’intermédiaire de mesures préventives, l’utilisation des produits de diagnostic et des traitements pour limiter la pression médicamenteuse. ■ Pilier III - Faire face à la résistance en limitant la propagation de parasites résistants aux médicaments antipaludiques. ■ Pilier IV - Stimuler la recherche et l’innovation afin de mieux tirer parti des outils existants et de mettre au point de nouveaux outils contre la résistance aux médicaments antipaludiques. ■ Chaque pilier correspond à un ensemble d’interventions, qui peuvent être mises en œuvre séparément à l’échelle locale, régionale et mondiale. Les pays adoptant cette stratégie doivent adapter les interventions à leur contexte local. Initiative pour stopper la propagation du moustique Anopheles stephensi ■ Originaire d’Asie du Sud et de certaines zones de la péninsule arabique, le moustique Anopheles stephensi n’a cessé d’étendre son territoire ces dix dernières années. Il a été détecté à Djibouti (2012), en Éthiopie et au Soudan (2016), en Somalie (2019) et au Nigéria (2020). ■ Au moment où le moustique An. stephensi a été signalé dans la région Afrique de l’OMS, il s’est avéré résistant à plusieurs des insecticides utilisés dans les établissements de santé publics, ce qui a ajouté une difficulté supplémentaire à son contrôle. ■ L’invasion de l’Afrique subsaharienne par An. stephensi, région la plus lourdement touchée par le paludisme et où plus de 40 % de la population vit en milieu urbain, soulève l’inquiétude, car il semble qu’il ait provoqué la résurgence du paludisme dans la ville de Djibouti et qu’il ait été à l’origine d’au moins une flambée épidémique en Éthiopie. xxxvii ■ En septembre 2022, une nouvelle initiative a été lancée dans le but d’enrayer la propagation d’An. stephensi sur le continent africain et de déterminer s’il peut être éliminé de certaines régions qu’il a déjà envahies. ■ Cette initiative plaide pour que la riposte nationale face à An. stephensi fasse partie d’une réponse plus vaste aux vecteurs du paludisme grâce à l’amélioration de la collaboration, l’augmentation de la surveillance, l’optimisation des échanges d’informations, le développement d’orientations et la priorisation de la recherche. Cadre mondial pour répondre au paludisme en milieu urbain ■ D’ici 2050, près de sept personnes sur dix dans le monde vivront dans les villes et autres zones urbaines. Dans les pays d’endémie palustre, il est même probable que plus de gens vivent en zones urbaines qu’en zones rurales. ■ D’ailleurs, dans les 10 pays où le paludisme sévit le plus au sein de la région Afrique de l’OMS, on considère que plus de 40 % de la population vit déjà en milieu urbain. ■ La dynamique de la transmission et le poids du paludisme, ainsi que d’autres maladies vectorielles, peuvent ne pas être identiques dans les zones urbaines et rurales. ■ D’une manière générale, l’urbanisation va réduire la transmission du paludisme. Toutefois, une urbanisation sauvage entraînera une transmission focale, qui se traduira par un poids du paludisme démesuré parmi les populations pauvres en milieu urbain. ■ L’invasion par des vecteurs adaptés à se reproduire en environnement urbain, comme la propagation récente du moustique An. stephensi dans la région Afrique de l’OMS, peut faire courir un plus grand risque aux populations des villes. ■ Il se peut que les approches visant à réduire le poids du paludisme dans les zones rurales ne fonctionnent pas en milieu urbain ou qu’elles nécessitent une application ciblée à plus petite échelle. ■ La riposte face au paludisme dans les zones urbaines requiert des données sur les déterminants qui sont propres aux écosystèmes urbains et qui expliquent si la transmission et le poids du paludisme sont focalisés. ■ Le leadership des ministères, de l’industrie et de la finance, de la recherche, des milieux universitaires et d’autres secteurs a tendance à se concentrer dans les zones urbaines, offrant davantage de chances aux politiques, stratégies et actions multisectorielles intégrées. ■ Avec l’accélération de l’urbanisation, l’accès est mis sur des villes plus saines, en intégrant la volonté politique, au niveau mondial, de contrer les menaces que représente le changement climatique. ■ En octobre 2022, l’OMS et le Programme des Nations unies pour les établissements humains (ONU-Habitat) ont lancé conjointement le Cadre mondial pour répondre au paludisme en milieu urbain, qui s’adresse aux décideurs politiques et aux intervenants concernés. ■ Ce cadre conceptuel est une réponse au paludisme dans les villes. Il souligne le rôle central des autorités municipales et la nécessite de veiller à ce que la riposte face au paludisme s'inscrive dans le cadre plus large du développement urbain et du programme de santé. Il insiste également sur le besoin de mettre en œuvre une riposte ciblée, basée sur des données, dans le cadre de la conception des interventions antipaludiques. S’il est appliqué, ce cadre devrait présenter de nombreux avantages pour améliorer la santé et le bien être. Déploiement du vaccin antipaludique RTS,S/AS01 dans les zones de transmission modérée à élevée ■ En octobre 2021, l’OMS a recommandé l’utilisation du vaccin RTS,S/AS01 pour la prévention du paludisme à P. falciparum chez les enfants vivant dans des zones de transmission modérée à élevée, telles que définies par l’OMS. En juillet 2022, l’OMS a délivré son approbation pour la préqualification du vaccin RTS,S. ■ En décembre 2021, le Conseil d’administration de Gavi, l’Alliance du Vaccin, a approuvé un investissement initial de près de US$ 160 millions (sur la période 2022-2025) pour étendre le déploiement du vaccin contre le paludisme dans les pays éligibles au soutien de Gavi. ■ Gavi a estimé que la demande régulière de vaccins devrait dépasser 80 à 100 millions de doses par an. Pour anticiper un écart entre l’offre et la demande, l’OMS a coordonné le développement d’un cadre pour la répartition des approvisionnements limités en vaccins antipaludiques, afin de déterminer où le nombre limité de doses devra être alloué de manière transparente et équitable, sur la base de principes éthiques et des meilleures données disponibles. xxxviii W O RL D M AL AR IA R EP O RT 2 02 2 ■ En août 2022, le Fonds des Nations Unies pour l’enfance (UNICEF) a annoncé qu’il avait assuré l’approvisionnement en vaccins produits par GSK, à savoir la fourniture de 18 millions de doses de vaccin antipaludique sur les trois prochaines années (2023-2025). ■ Depuis juin 2022, l’OMS et Gavi ont soutenu plus de 20 pays au travers d’ateliers multinationaux dédiés à la préparation de dossiers de demande de subventions à Gavi. Appuyée par l’OMS, l’adaptation au niveau sous-national du déploiement du vaccin antipaludique, sur la base des orientations du Cadre pour la répartition des approvisionnements limités en vaccins antipaludiques, représente une part essentielle de cette initiative. Urgences sanitaires et humanitaires ■ Les pays d’endémie palustre ont été confrontés à diverses urgences sanitaires et humanitaires sans rapport avec la pandémie de COVID-19. En 2021, on estime que 268 millions de personnes ont dû faire face à des urgences humanitaires, contre 301 millions en 2020. ■ Les conflits, la famine et les inondations ont été les principales causes d’urgences humanitaires, parfois aggravées par des épidémies. ■ En 2021, les pays les plus touchés (en nombre de personnes affectées) par les crises sanitaires et humanitaires ont été l’Afghanistan, la Colombie, l’Éthiopie, le Mali, le Myanmar, le Nigéria, l’Ouganda, la République démocratique du Congo, la Somalie, le Soudan, le Soudan du Sud et le Yémen. Perturbation des services antipaludiques durant la pandémie de COVID-19 ■ Quarante-six pays avaient des campagnes de distribution de moustiquaires imprégnées d’insecticide (MII) prévues sur 2020, pour un total de 272 millions de moustiquaires à distribuer. Fin 2020, 74 % de toutes les MII dont la distribution était prévue sur l’année avaient effectivement été distribuées. ■ Quarante trois pays avaient prévu des campagnes de distribution de MII sur 2021 et quatorze de ces pays ne prévoyaient de distribuer que les moustiquaires prévues pour 2020. ■ Globalement, il était donc prévu de distribuer 171 millions de MII en 2021 dont 70 millions étaient initialement programmées pour 2020. Au total, 128 millions de MII (soit 75 %) ont été distribuées en 2021. ■ Huit pays (Bénin, Érythrée, Îles Salomon, Indonésie, Nigéria, Ouganda, Thaïlande et Vanuatu) ont distribué moins de 60 % de leurs MII en 2021. Sept pays (Botswana, Haïti, Inde, Pakistan, République centrafricaine, Sierra Leone et Tchad) n’ont distribué aucune des moustiquaires prévues pour 2021. L’Inde, le Nigéria et l’Ouganda, trois pays soutenus dans le cadre de l’approche « high burden to high impact » (HBHI), ont distribué respectivement 0 %, 53 % et 26 % des moustiquaires prévues pour 2021. ■ Les résultats de la 3e série d’enquêtes indicatives sur les services de santé essentiels, menée de novembre à décembre 2021, suggèrent que les perturbations dans l’accès au diagnostic et au traitement du paludisme dans la région Afrique de l’OMS se sont largement dissipées au cours des derniers mois de 2021, avec sept pays signalant des perturbations, contre 16 en 2020 et neuf au premier trimestre 2021. ÉVOLUTION DU NOMBRE DE CAS ET DE DÉCÈS Cas de paludisme ■ Au niveau mondial, le nombre de cas de paludisme est estimé à 247 millions en 2021 dans 84 pays d’endémie palustre (y compris le territoire de la Guyane française), soit une hausse par rapport aux 245 millions de 2020. La plupart des cas supplémentaires sont estimés dans la région Afrique de l’OMS. En 2015, l’année de référence de la Stratégie technique mondiale de lutte contre le paludisme 2016-2030 ([le] GTS), le nombre de cas de paludisme était estimé à 230 millions. ■ L’incidence du paludisme (i. e. nombre de cas pour 1 000 habitants exposés au risque de paludisme) a reculé au niveau mondial, passant de 82 en 2000 à 57 en 2019, avant d’augmenter à nouveau pour atteindre 59 en 2020. Ce nombre n’a pas évolué de 2020 à 2021. L’augmentation de 2020 est associée à la perturbation des services durant la pandémie de COVID-19. xxxix ■ Entre 2019 et 2021, 13,4 millions de cas supplémentaires ont été attribués aux perturbations dues à la pandémie de COVID-19, selon les estimations. ■ Le pourcentage des infections à P. vivax a diminué, passant de 8 % (20,5 millions) en 2000 à 2 % (4,9 millions) en 2021. ■ Vingt-neuf pays ont concentré 96 % du nombre total de cas de paludisme dans le monde. Quatre d’entre eux ont enregistré, à eux seuls, près de la moitié des cas : le Nigéria (27 %), la République démocratique du Congo (12 %), l’Ouganda (5 %) et le Mozambique (4 %). ■ En 2021, la région Afrique de l’OMS représentait environ 95 % (234 millions) des cas estimés dans le monde. ■ Dans la région Afrique de l’OMS, l’incidence du paludisme a baissé de 373 à 225 cas pour 1 000 habitants exposés au risque de paludisme sur la période 2000-2019 avant de remonter à 234 en 2020, principalement en raison de la perturbation des services pendant la pandémie de COVID-19. En 2021, l’incidence du paludisme a reculé pour atteindre 229 cas pour 1 000 habitants. ■ Le Cabo Verde a rapporté zéro cas de paludisme indigène pour la troisième année consécutive, mettant fin à l’épidémie de paludisme. ■ La région Asie du Sud-Est de l’OMS a concentré près de 2 % des cas de paludisme dans le monde. Le nombre de cas y a chuté de 76 %, passant de 23 millions en 2000 à près de 5 millions en 2021. De même, l’incidence du paludisme dans cette région a diminué de 82 %, avec quelque 18 cas pour 1 000 habitants exposés au risque de paludisme en 2000, contre 3 en 2021. ■ L’Inde a représenté à elle seule 79 % des cas de paludisme dans la région. Le Sri Lanka a été certifié exempt de paludisme en 2016 et reste sans paludisme. ■ Entre 2020 et 2021, 400 000 cas supplémentaires ont été dénombrés dans la région, dont la moitié au Myanmar. ■ Le nombre de cas de paludisme dans la région Méditerranée orientale de l’OMS a réduit de 38 %, passant de près de 7 millions de cas en 2000 à 4 millions environ en 2015. Entre 2016 et 2021, il a augmenté de 44 % pour atteindre 6,2 millions. ■ Sur la période 2000-2020, l’incidence du paludisme dans la région Méditerranée orientale de l’OMS a diminué de 20 à 12 cas pour 1 000 habitants exposés au risque de paludisme. Avec quelque 54 % des cas, le Soudan est le pays le plus touché dans cette région. En 2021 et pour la quatrième année consécutive, la République islamique d’Iran a rapporté zéro cas de paludisme indigène. Quant à l’Arabie saoudite, elle n’a signalé, en 2021 et pour la première fois, aucun cas de paludisme indigène. ■ Dans la région Pacifique occidental de l’OMS, 1,4 million de cas ont été estimés en 2021, soit une baisse de 49 % par rapport aux 3 millions de 2000. Sur la même période, l’incidence du paludisme est passée de quatre à deux cas pour 1 000 habitants exposés au risque de paludisme. La Papouasie-Nouvelle- Guinée a enregistré près de 87 % des cas dans cette région en 2021. En 2021, la Chine a été certifiée exempte de paludisme par l’OMS et, pour la quatrième année de suite, la Malaisie n’a rapporté aucun cas de paludisme humain en 2021. ■ Dans la région Amériques de l’OMS, le nombre de cas de paludisme a diminué de 60 % (passant de 1,5 million à 0,6 million) et l’incidence du paludisme de 70 % (de 14 à 4) entre 2000 et 2021. Les progrès réalisés dans cette région ces dernières années ont souffert de la forte hausse du paludisme en République bolivarienne du Venezuela, qui avait recensé près de 35 500 cas en 2000 contre plus de 482 000 en 2017. En 2020, le nombre de cas y a été réduit de plus de 50 % (223 000) par rapport à 2019, avant de continuer à décroître (205 000) en 2021. Ce recul s’explique par la limitation des déplacements due à la pandémie de COVID-19 et la pénurie de carburant ayant affecté l’industrie minière, qui contribue grandement à l’augmentation récente du paludisme dans le pays. Cette limitation des déplacements peut aussi avoir freiné l’accès aux soins, réduisant ainsi le nombre de cas de paludisme rapportés par les établissements de santé. ■ En 2021, les cas estimés ont plus que doublé au Honduras et au Panama par rapport à 2019. Sur la même période, des baisses significatives des cas de paludisme ont été enregistrées en République bolivarienne du Venezuela (-263 000), au Brésil (-17 000), en Colombie (-17 000) et au Pérou (-22 000). ■ Le Brésil, la Colombie et la République bolivarienne du Venezuela ont concentré plus de 79 % des cas dans cette région. ■ L’Argentine, El Salvador et le Paraguay ont été certifiés exempts de paludisme en 2019, 2021 et 2018, respectivement. Le Belize n’a signalé aucun cas de paludisme indigène pour la troisième année consécutive, mettant fin à l’épidémie de paludisme. ■ Depuis 2015, la région Europe de l’OMS est exempte de paludisme. xl W O RL D M AL AR IA R EP O RT 2 02 2 Mortalité associée ■ Au niveau mondial, le nombre de décès dus au paludisme a baissé de façon régulière sur la période 2000-2019, passant de 897 000 en 2000 à 577 000 en 2015, puis à 568 000 en 2019. En 2020, le nombre de décès dus au paludisme a augmenté de 10 % par rapport à 2019, atteignant 625 000 selon les estimations. En revanche, les estimations concernant le nombre de décès en 2021 ont légèrement diminué pour s’élever à 619 000. Entre 2019 et 2021, 63 000 décès ont été provoqués par les perturbations des services antipaludiques essentiels pendant la pandémie de COVID-19. ■ Les enfants de moins de 5 ans représentaient 87 % des décès associés au paludisme en 2000, contre 76 % en 2015. Aucune évolution n’est à noter depuis lors. ■ La mortalité associée au paludisme (i. e. nombre de décès pour 100 000 habitants exposés au risque de paludisme) a diminué de moitié au niveau mondial, passant de 30 en 2000 à 15 en 2015. La baisse s’est ensuite poursuivie à un rythme plus modeste pour atteindre 14 en 2019. En 2020, le taux de mortalité est remonté à 15,1, avant de légèrement diminuer à 14,8 en 2021. ■ Au niveau mondial, près de 96 % des décès dus au paludisme ont été enregistrés dans 29 pays. Quatre pays ont concentré un peu plus de la moitié des décès dus au paludisme dans le monde en 2021 : le Nigéria (31 %), la République démocratique du Congo (13 %), le Niger (4 %) et la République Unie de Tanzanie (4 %). ■ Dans la région Afrique de l’OMS, le nombre de décès dus au paludisme a diminué, passant de 841 000 en 2000 à 541 000 en 2018, avant de remonter à 599 000 en 2020. Selon les estimations, le nombre de décès a de nouveau baissé en 2021 pour atteindre 593 000. Sur la période 2000-2019, la mortalité associée a baissé de 62 %, chutant de 148 à 56 décès pour 100 000 habitants exposés au risque de paludisme, avant de remonter à 60 en 2020 pour finalement retomber à 58 en 2021. ■ Depuis 2018, le Cabo Verde n’a signalé aucun décès dû au paludisme. ■ Dans la région Asie du Sud-Est de l’OMS, le nombre de décès dus au paludisme a diminué de 74 %, avec 35 000 décès en 2000 contre 9 000 en 2019. Ce nombre est resté inchangé depuis trois ans. ■ L’Inde a concentré environ 83 % des décès dus au paludisme dans la région Asie du Sud-Est de l’OMS. ■ Dans la région Méditerranée orientale de l’OMS, le nombre de décès dus au paludisme a diminué de 45 %, passant de 13 600 en 2000 à 7 500 en 2014. Il a ensuite augmenté de 79 % entre 2014 et 2021 pour atteindre 13 400. La plus grande partie de cette augmentation a été observée au Soudan, où plus de 90 % des cas sont des infections à P. falciparum, dont le taux de létalité est supérieur aux infections à P. vivax. ■ Dans la région Méditerranée orientale de l’OMS, la mortalité liée au paludisme a baissé de 60 % entre 2000 et 2009. Toutefois, elle est restée pratiquement inchangée depuis 2016, affichant une légère augmentation de 28 % pour passer de 2 à 2,5 décès pour 100 000 habitants à risque. ■ Dans la région Pacifique occidental de l’OMS, le nombre de décès dus au paludisme a diminué de 58 %, passant de 6 200 en 2000 à 2 600 en 2021. Sur la même période, la mortalité associée a baissé de 67 %, chutant de 0,9 à 0,3 décès pour 100 000 habitants exposés au risque de paludisme. Dans cette région, la Papouasie-Nouvelle-Guinée a enregistré près de 94 % des décès dus au paludisme en 2021. ■ Dans la région Amériques de l’OMS, le nombre de décès dus au paludisme a diminué de 64 % (919 contre 334) et la mortalité associée de 73 % (0,8 contre 0,2). La plupart des décès (78 %) dans cette région ont été enregistrés parmi les adultes. Nombre de cas de paludisme et de décès évités ■ Selon les estimations, 2 milliards de cas de paludisme et 11,7 millions de décès associés ont été évités dans le monde entre 2000 et 2021. ■ La plupart des cas (82 %) et des décès (95 %) évités l’auraient été dans la région Afrique de l’OMS, suivie par la région Asie du Sud-Est (10 % des cas et 3 % des décès). Poids du paludisme pendant la grossesse ■ En 2021, sur les 40 millions de femmes enceintes vivant dans 38 pays de la région Afrique de l’OMS où la transmission est modérée à élevée, 13,3 millions (32 %) ont été exposées à une infection palustre durant leur grossesse. xli ■ En détaillant les sous-régions de l’OMS, l’Afrique de l’Ouest a affiché la plus forte prévalence d’exposition au paludisme durant la grossesse (40,7 %), suivie de près par l’Afrique centrale (39,8 %), alors que la prévalence était de 20 % en Afrique de l’Est et en Afrique australe. ■ Sans prévention du paludisme pendant la grossesse, il est estimé que 961 000 enfants auraient présenté un faible poids à la naissance dans ces 38 pays. Dans les 33 pays ayant mis en œuvre le TPIp, 457 000 cas estimés de faible poids à la naissance ont été évités. ■ Si toutes les femmes enceintes se rendant au moins une fois à une consultation prénatale recevaient une seule dose de TPIp, en supposant qu’elles soient toutes éligibles et que le taux de couverture en TPIp par deux et trois doses restait aux niveaux actuels, 55 000 cas supplémentaires de faible poids à la naissance auraient été évités dans ces 33 pays qui fournissent des données sur ce traitement. ■ Si la couverture en TPIp par trois doses atteignait le taux de couverture des soins prénataux (une visite), et si ce taux de couverture était maintenu pour les consultations prénatales suivantes, 162 000 enfants supplémentaires ne présenteraient pas un faible poids à la naissance. ■ Si 90 % des femmes enceintes recevaient trois doses de TPIp, 265 000 enfants supplémentaires ne présenteraient pas un faible poids à la naissance. ■ Éviter l’insuffisance pondérale à la naissance, qui représente un risque important de mortalité néonatale et infantile, permettrait de sauver de nombreuses vies. ÉLIMINATION DU PALUDISME ET PRÉVENTION DE SA RÉAPPARITION ■ Les progrès vers l’élimination du paludisme s’accélèrent : les pays endémiques n’étaient que 84 en 2021 contre 108 en 2000. ■ Le nombre de pays où le paludisme était endémique en 2000 et qui ont rapporté moins de 100 cas a augmenté, passant de 6 en 2000 à 27 en 2020 comme en 2021. ■ Entre 2000 et 2021, les pays comptant moins de 10 cas de paludisme indigène sont passés de 4 à 25. ■ Sur la période 2010-2021, le nombre total de cas de paludisme dans les pays « visant l’élimination du paludisme d’ici 2025 » (initiative E-2025) a diminué de 82,8 %. Cependant, ces pays et territoires ont enregistré une hausse des cas de 30,4 % entre 2020 et 2021. ■ Dans la région Asie du Sud-Est de l’OMS, le nombre d’infections par P. knowlesi chez l’homme a considérablement augmenté dans certains pays, notamment la Malaisie. Aucun cas de paludisme indigène chez l’homme ou décès associé n’a été signalé en Malaisie ces quatre dernières années. Néanmoins, 17 125 cas d’infections par P. knowlesi et 48 décès ont été rapportés au total dans ce pays. ■ Au cours de la seule année 2021, 3 575 infections par P. knowlesi ont été signalées, entraînant 13 décès. Sur la même période, 435 infections supplémentaires par P. knowlesi ont été signalées dans la région Asie du Sud-Est de l’OMS, à savoir en Indonésie, aux Philippines et en Thaïlande. ■ Le Belize et le Cabo Verde n’ont signalé aucun cas de paludisme pour la troisième année consécutive, alors que la République islamique d’Iran et la Malaisie ont rapporté zéro cas de paludisme indigène pour la quatrième année de suite. En 2021, le Timor-Leste, suite à la flambée épidémique de l’année précédente, a rapporté zéro cas de paludisme indigène. Quant à l’Arabie saoudite, elle n’a signalé, en 2021 et pour la première fois, aucun cas de paludisme indigène. ■ Cinq pays (Azerbaïdjan, Belize, Cabo Verde, République islamique d’Iran et Tadjikistan) ont déposé une demande formelle de certification. ■ Malgré les perturbations dues à a pandémie de COVID-19, 61,5 % des pays E-2025 signalant des cas de paludisme ont continué à enregistrer des progrès vers la réduction du poids de la maladie et son élimination. En 2021, plusieurs pays ont affiché des baisses de cas par rapport à 2020 : Afrique du Sud (33,7 %), Arabie saoudite (100 %), Bhoutan (59,1 %), Botswana (20,5 %), Mexique (32 %), Népal (56,2 %), République dominicaine (65,6 %), République de Corée (23 %), Suriname (85,9 %), Thaïlande (22,3 %), Timor-Leste (100 %) et Vanuatu (36,7 %). ■ En revanche, les pays (et un territoire) suivants ont affiché des hausses en 2021 par rapport à 2020 : Comores (56,9 %), Costa Rica (52,4 %), Équateur (11,1 %), Eswatini (53,9 %), Guatemala (16.9 %), Guyane française (2,1 %), Honduras (47,4 %), Panama (55,3 %), République populaire démocratique de Corée (22,8 %) et Sao Tomé-et-Principe (28.9 %). Pour la deuxième année consécutive, le nombre de cas de paludisme indigène a augmenté en Équateur. xlii W O RL D M AL AR IA R EP O RT 2 02 2 ■ En dépit des obstacles, de nombreux pays réalisent d’immenses progrès : entre 2011 et 2021, le taux de classification des cas au sein des pays E-2025 a augmenté de 7,6 % à 90,8 %, prouvant les efforts soutenus de ces pays dans la poursuite des objectifs d’élimination. ■ Dans les six pays de la sous-région du Grand Mékong (Cambodge, Chine [province du Yunnan], Myanmar, République démocratique populaire lao, Thaïlande et Viet Nam), le nombre total de cas de paludisme indigène a globalement chuté de 76,5 %, alors que le nombre de cas de paludisme indigène à P. falciparum a diminué de 94,1 % entre 2000 et 2021. Ce recul a connu une accélération plus rapide depuis 2012, date à laquelle le programme « Mekong Malaria Elimination » (MME) a été lancé. Entre 2012 et 2021, la région a enregistré une baisse impressionnante du nombre de cas de paludisme indigène (86,4 %) et du nombre de cas de paludisme indigène à P. falciparum (95,7 %). En 2021, 90 082 cas de paludisme indigène et 16 484 cas de paludisme indigène à P. falciparum ont été signalés, soit une hausse de 17,3 % des cas de paludisme indigène et une diminution de 12,2 % des cas de paludisme indigène à P. falciparum par rapport à 2020. ■ Les cas de paludisme à P. falciparum et P. vivax ont augmenté au Myanmar en 2021 du fait de l’instabilité politique persistante dans la zone. En 2021, le Myanmar a d’ailleurs continué à cumuler la majeure partie des cas de paludisme indigène (87,7 %) et des cas de paludisme indigène à P. falciparum (80,9 %). ■ Les cas à P. falciparum ne cessant de diminuer, P. vivax apparaît comme l’espèce dominante dans la sous-région. En 2021, le Cambodge a signalé des rechutes pour la toute première fois : le pays a enregistré 1 978 cas de rechute au total, ce qui représente 48 % de tous les cas de paludisme indigène au niveau national. ■ Les pays de la sous-région du Grand Mékong tirent parti de l’exercice de vérification au niveau sous- national pour renforcer leurs programmes de prévention de la réapparition du paludisme et préparer leur certification par l’OMS. ■ De 2000 à 2021, la transmission du paludisme n’est réapparue dans aucun des pays préalablement certifiés exempts du paludisme. APPROCHE « HIGH BURDEN TO HIGH IMPACT » ■ Depuis novembre 2018, les 11 pays de l’approche « high burden to high impact » (HBHI) ont mis en œuvre des activités en rapport avec les quatre éléments de riposte définis. ■ En 2020 et 2021, l’OMS et le Partenariat RBM pour en finir avec le paludisme ont apporté leur soutien à ces pays afin qu’ils réalisent des auto-évaluations rapides sur les progrès accomplis dans l’atteinte des objectifs HBHI relatifs aux quatre éléments de riposte. ■ En 2021, avec l’augmentation continue des cas de paludisme, les pays HBHI représentaient 68 % des cas de paludisme et 70 % des décès associés. ■ Entre 2020 et 2021, le nombre de cas de paludisme dans les pays HBHI est passé de 163 millions à 168 millions, alors que le nombre de décès a diminué de 444 600 à 427 854. ■ En 2021, avec l’augmentation continue des cas de paludisme, les pays suivants ont recensé la majeure partie des cas de paludisme : Nigéria (39 %), République démocratique du Congo (18,2 %), Ouganda (7,8 %) et Mozambique (6,1 %). ■ Cinq des 11 pays HBHI (Ghana, Inde, Niger, République démocratique du Congo et République Unie de Tanzanie) ont enregistré une baisse du nombre de décès dus au paludisme, même si la maladie y sévit encore lourdement par rapport à l’ensemble des pays HBHI. SURVEILLANCE ■ Plus de 40 pays utilisent à l’heure actuelle les kits de surveillance du paludisme conçus pour le système d’information sur la santé au niveau du district (DHIS2) et 34 pays ont adopté le module sur le paludisme. xliii ■ Des modules standardisés dédiés à l’entomologie et à la lutte antivectorielle ont également été développés pour aider les pays à améliorer la collecte et l’utilisation des données sur les interventions entomologiques et de lutte antivectorielle. ■ Une série d’outils a été développée ou améliorée pour aider à la surveillance de l’élimination du paludisme par la plate-forme web DHIS2 et de l’application de saisie des données DHIS2 pour Android. Ces outils peuvent être utilisés pour la notification et l’investigation des cas, pour l’investigation des foyers et, de manière générale, pour la définition des éléments de réponse. ■ Au total, 12 pays ont commencé à établir des référentiels sur le paludisme, qui constituent des entrepôts réunissant toutes les données sur le paludisme. ■ L’OMS a lancé la Boîte à outils d’évaluation de la surveillance du paludisme, qui offre une méthode standardisée et systématique pour évaluer la performance des systèmes de surveillance. ■ Le Burkina Faso (2020), le Cameroun (2021), le Ghana (2021) et la République démocratique du Congo (2021) ont entrepris une évaluation de leur système de surveillance à l’aide de cette boîte à outils. Les évaluations de ces pays (sauf celle du Cameroun) sont prêtes à être publiées et mettent en lumière le bon niveau d’exhaustivité et d’actualité du reporting, mais un manque de correspondance entre les variables de base des registres et des rapports agrégés. INVESTISSEMENTS DANS LES PROGRAMMES ET LA RECHERCHE ANTIPALUDIQUES ■ Le GTS donne une estimation des fonds requis pour atteindre les objectifs intermédiaires de 2025 et 2030. Au total, les ressources annuelles nécessaires ont été estimées à US$ 6,8 milliards en 2020, avec une hausse à US$ 9,3 milliards en 2025 et US$ 10,3 milliards d’ici 2030. Toujours selon les estimations, US$ 850 000 millions supplémentaires seront requis chaque année pour la recherche et le développement (R&D) sur le paludisme au niveau mondial durant la période 2021-2030. ■ En 2021, US$ 3,5 milliards ont été investis au total pour le contrôle et l’élimination du paludisme, contre US$ 3,3 milliards en 2020 et US$ 3,0 milliards en 2019. Les investissements de 2021 ont été bien inférieurs aux US$ 7,3 milliards estimés nécessaires au niveau mondial pour rester sur la voie des objectifs du GTS. ■ L’écart entre investissements et ressources nécessaires a augmenté de façon spectaculaire au cours de ces dernières années, passant de US$ 2,6 milliards en 2019 à US$ 3,5 milliards en 2020, puis à US$ 3,8 milliards en 2021. ■ Sur la période 2010-2021, 67 % des fonds dédiés à la lutte contre le paludisme et à son élimination, soit près de US$ 2,4 milliards, provenaient de sources internationales. La contribution des États-Unis s’est élevée à plus de US$ 1,3 milliard au travers de financements bilatéraux planifiés et de la part, ajustée au paludisme, provenant des agences de contributions multilatérales. Sont venues s’y ajouter, par ordre d’importance, les contributions de partenaires bilatéraux et multilatéraux : le Royaume Uni de Grande-Bretagne et d’Irlande du Nord (Royaume Uni) et l’Allemagne (environ US$ 200 millions), le Canada, la France et le Japon (environ US$ 100 millions), ainsi que d’autres pays membres du Comité d’aide au développement et bailleurs de fonds du secteur privé pour des contributions totales à hauteur de US$ 400 millions. ■ En 2021, les gouvernements des pays d’endémie ont contribué à hauteur de plus d’un tiers du financement total, soit plus de US$ 1,1 milliard. Sur ce montant, US$ 300 millions ont été investis dans la prise en charge des cas de paludisme dans le secteur public et US$ 800 millions dans d’autres activités antipaludiques, ce qui correspond à une augmentation de US$ 100 millions par rapport à 2020. ■ Sur les US$ 3,5 milliards investis en 2020, plus de US$ 1,5 milliard (44 %) ont transité par le Fonds mondial. Par rapport aux années précédentes, les décaissements du Fonds mondial en faveur des pays d’endémie ont augmenté de près de US$ 100 millions depuis 2020 et de US$ 300 millions depuis 2019. ■ Les classifications de la Banque mondiale par groupes de revenus varient d’une année sur l’autre. En 2021, les 27 pays du groupe à faible revenu ont représenté 47 % du financement total. Leur financement a augmenté de plus de 50 % depuis 2010 et ils recensent plus de 90 % des cas de paludisme et décès associés au niveau mondial. Les 39 pays à revenu faible et intermédiaire ont représenté 41 % du financement total en 2021. Les autres pays et certaines régions non précisées, qui n’avaient fourni xliv W O RL D M AL AR IA R EP O RT 2 02 2 aucune donnée géographique sur les destinataires, ont représenté 12 % du financement de la lutte contre le paludisme. ■ L’évaluation du financement de la lutte contre le paludisme par personne à risque révèle des écarts entre les financements nationaux et internationaux au sein des régions de l’OMS, et montre des changements importants au cours des dix dernières années. La plupart des régions de l’OMS ont vu le financement par personne à risque chuter à des niveaux inférieurs à 2010, hormis la région Afrique de l’OMS, où le financement a doublé en 2021 par rapport à 2010. ■ Sur les US$ 3,5 milliards investis en 2021, plus des trois quarts (78 %) ont été dirigés vers la région Afrique de l’OMS, suivie par les régions Asie du Sud-Est et Méditerranée orientale (5 % chacune), Amériques (4 %) et Pacifique occidental (3 %). Les 5 % restants ont été alloués à des régions non précisées. ■ De nombreux pays ont vu leur produit intérieur brut (PIB) réel fluctuer du fait de la pandémie de COVID-19 et d’autres crises. Ces fluctuations ont affecté l’économie mondiale, qui a progressé de 5,5 % en 2021 après une contraction de 3,4 % en 2020. En 2021, les pays à plus faible revenu ainsi qu’à revenu faible et intermédiaire ont affiché une croissance significative. En effet, seuls 11 % de ces pays ont subi un contrecoup négatif sur leur PIB réel alors qu’ils étaient 70 % en 2020. Bien que le financement de la lutte contre le paludisme n’ait cessé d’augmenter, l’impact réel de la pandémie de COVID-19, la crise économique qui en a découlé, divers autres facteurs internationaux et l’accroissement de la population à risque vont continuer de peser dans les années à venir. ■ Les fonds dédiés à la recherche et au développement (R&D) ont atteint US$ 626 millions en 2021. ■ Depuis le pic de 2018, ce financement diminue pour la troisième année de suite et ce, dans presque toutes les catégories de produits antipaludiques. La Fondation Bill & Melinda Gates a notamment réduit le montant de ses investissements. Elle reste pourtant le troisième plus important bailleur de fonds engagé dans la recherche et le développement du vaccin antipaludique, après les Instituts nationaux de santé américains (NIH) et les acteurs du secteur en général. ■ La recherche fondamentale a connu la plus forte diminution en valeur absolue de son financement en 2021 (-12 %), alors que les fonds pour la production de produits de lutte antivectorielle ont décliné d’un peu moins de 20 % depuis 2020. En 2021, le financement de R&D concernant les outils de diagnostic s’est élevé à 2,5 % du financement total de la lutte contre le paludisme, son niveau le plus bas depuis 2013. Seuls la recherche et le développement de traitements ont évolué à contre-courant de la tendance générale à la baisse, avec une hausse de 2,3 % pour les médicaments et de 7,0 % pour les produits biologiques. ■ En matière de R&D et avec un financement stable d’environ US$ 189 millions, les Instituts nationaux de santé américains demeurent le principal bailleur de fonds en 2021, suivis par les acteurs du secteur et la Fondation Bill & Melinda Gates. DISTRIBUTION ET COUVERTURE DES OUTILS DE PRÉVENTION DU PALUDISME ■ Les fabricants de MII ont indiqué en avoir livré près de 2,5 milliards dans le monde entre 2004 et 2021, dont 2,2 milliards (87 %) en Afrique subsaharienne. ■ En 2021, ces fabricants ont livré près de 220 millions de MII à des pays d’endémie. Sur ces 220 millions, 46 % étaient des moustiquaires imprégnées de butoxyde de pipéronyle (PBO) et 9 % des MII à double substance active. ■ En 2021, 68 % des ménages vivant en Afrique subsaharienne disposaient d’au moins une MII, contre 5 % environ en 2000. Le pourcentage des ménages disposant d’au moins une MII pour deux membres du foyer est passé de 1 % en 2000 à 38 % en 2021. Durant la même période, le pourcentage de la population ayant accès à une MII dans son foyer a augmenté de 3 % à 54 %. ■ Le pourcentage de la population dormant sous une MII a aussi considérablement augmenté entre 2000 et 2021, qu’il s’agisse de la population dans son ensemble (de 2 % à 47 %), des enfants âgés de moins de 5 ans (de 3 % à 53 %) ou des femmes enceintes (de 3 % à 53 %). ■ Cependant, depuis 2017, les indicateurs sur l’accès aux MII et sur leur utilisation sont en baisse. ■ Au niveau mondial, le pourcentage de la population à risque protégée par pulvérisation intradomiciliaire d’insecticides à effet rémanent (PID) dans les pays d’endémie a reculé, passant de xlv 5,5 % en 2010 à 2,4 % en 2021. Le pourcentage de la population protégée par PID est stable depuis 2016, avec moins de 6 % dans chacune des régions de l’OMS. ■ Au niveau mondial, le nombre de personnes protégées par PID a chuté de 153 millions en 2010 à 112 millions en 2015, puis à 80 millions en 2021. ■ Le nombre moyen d’enfants traités par cycle de CPS n’a cessé d’augmenter, passant de quelque 0,2 million en 2012 à près de 45 millions en 2021. ■ Dans les 15 pays ayant mis en œuvre la CPS, près de 180 millions de doses ont été livrées en 2021. ■ Le pourcentage d’utilisation (par nombre de doses) du TPIp a été calculé sur la base des données provenant de 33 pays de la région Afrique de l’OMS. En 2021, 72 % des femmes enceintes ont reçu des soins prénataux au moins une fois durant leur grossesse. Environ 55 % des femmes enceintes ont reçu une dose de TPIp, alors que 45 % ont reçu deux doses, et 35 % trois doses. DISTRIBUTION ET COUVERTURE DES OUTILS DE DIAGNOSTIC ET DE TRAITEMENT DU PALUDISME ■ De 2010 à 2021, 3,5 milliards de tests de diagnostic rapide (TDR) du paludisme ont été vendus dans le monde, dont 82 % à destination des pays d’Afrique subsaharienne. Durant la même période, 2,4 milliards de TDR ont été distribués par les programmes nationaux de lutte contre le paludisme (PNLP), dont 88 % en Afrique subsaharienne. ■ En 2021, 413 millions de TDR ont été vendus par les fabricants et 262 millions distribués par les PNLP. ■ Entre 2010 et 2021, près de 3,8 milliards de traitements par ACT ont été vendus dans le monde. Sur ces ventes, environ 2,6 milliards ont été livrés au secteur public des pays d’endémie. Le reste correspond à des mécanismes de co paiement public ou privé du Fonds pour des médicaments antipaludéens à des prix abordables (AMFm) ou du Fonds mondial, voire à des livraisons au secteur privé en dehors du mécanisme de co paiement du Fonds mondial pour chacune de ces deux années. ■ Les données nationales rapportées par les PNLP montrent que, de 2010 à 2021, 2,4 milliards de traitements par ACT ont été livrés à des prestataires de santé pour le traitement des patients au sein d’un établissement public. ■ En 2021, quelque 225 millions de traitements par ACT ont été livrés par les fabricants au secteur public. Cette même année, les PNLP ont distribué 242 millions de traitements par ACT dans ce secteur, dont 97 % en Afrique subsaharienne. ■ Les données compilées à partir d’enquêtes réalisées auprès des ménages entre 2005 et 2021 dans 20 pays d’Afrique subsaharienne (ayant mené au moins deux enquêtes : l’une entre 2005-2011 pour servir de référence et l’autre entre 2015-2021 pour la plus récente) ont permis d’analyser le taux de sollicitation de traitement, la couverture en diagnostic et l’utilisation des ACT chez les enfants de moins de 5 ans. ■ En comparant enquêtes de référence et enquêtes plus récentes, peu de différences sont apparues concernant la prévalence de la fièvre dans les 2 semaines précédant les enquêtes (médiane de 25 % contre 20 %) ou la sollicitation de traitement en cas de fièvre (médiane de 65 % contre 67 %). ■ Les comparaisons de la source du traitement entre enquêtes de référence et enquêtes plus récentes indiquent une médiane de 58 % contre 69 % pour les soins reçus dans des établissements de santé publics, et une médiane de 40 % contre 28 % pour les soins administrés dans le secteur privé. Le recours aux agents de santé communautaires est resté faible sur ces deux périodes, avec une médiane de 2 % (enquêtes de référence) et 1 % (enquêtes plus récentes). ■ Le taux de couverture en diagnostic chez les enfants de moins de 5 ans avec de la fièvre et pour lesquels des soins ont été sollicités a largement progressé, d’une médiane de 30 % au départ à 57 % dans les dernières enquêtes. ■ L’utilisation des ACT parmi les enfants fiévreux pour lesquels des soins ont été sollicités a également augmenté, passant de 14 % à 24 % dans les dernières enquêtes. ■ Parmi les enfants fiévreux ayant subi un prélèvement sanguin au doigt ou au talon, le recours aux ACT a atteint 29 % dans les dernières enquêtes, alors qu’il correspondait à 21 % dans les enquêtes de référence. xlvi W O RL D M AL AR IA R EP O RT 2 02 2 PROGRÈS VERS L’ATTEINTE DES OBJECTIFS DU GTS POUR 2020 ■ Le GTS vise à réduire l’incidence du paludisme et la mortalité associée d’au moins 75 % d’ici 2025 et 90 % d’ici 2030 en se basant sur les données de référence de 2015. En 2020, le GTS avait pour objectif d’atteindre une réduction de 40 %. ■ Le nombre de pays ayant atteint les objectifs du GTS pour 2021 a été extrapolé à partir des estimations officielles du poids du paludisme plutôt qu’à partir de projections (selon la méthode déjà utilisée dans le Rapport sur le paludisme dans le monde 2020). ■ En 2020 et 2021, les estimations tiennent compte de l’effet des perturbations des services antipaludiques durant la pandémie et sont basées sur une nouvelle méthode de quantification de la part des décès attribuables au paludisme. ■ En dépit des progrès considérables accomplis depuis 2000, les objectifs intermédiaires du GTS pour 2020 relatifs à la morbidité et la mortalité n’ont pas été atteints au niveau mondial. Si la tendance actuelle se confirme, l’ensemble des objectifs du GTS pour 2025 ne seront également pas atteints au niveau mondial. ■ En 2021, l’incidence du paludisme s’est établie à 59 cas pour 1 000 habitants à risque, au lieu des 31 cas représentés par l’objectif intermédiaire de morbidité fixé dans le GTS. En d’autres termes, nous sommes à 48 % en deçà de notre objectif. ■ Même si la baisse de la mortalité est plus nette, relativement, que la baisse de l’incidence, l’objectif intermédiaire du GTS pour 2021 défini à 7,8 décès pour 100 000 habitants exposés au risque de paludisme était 48 % en deçà de la mortalité réellement établie au niveau mondial à 14,8 pour 100 000 en 2021. ■ Sur les 93 pays où le paludisme était endémique en 2015 (y compris le territoire de la Guyane française), 39 (42 %) ont atteint l’objectif intermédiaire pour 2021 en matière de morbidité. En effet, ils ont réduit leur incidence de 40 % ou plus, ou ont rapporté zéro cas de paludisme. ■ Dix-neuf pays (20 %) ont réussi à faire baisser l’incidence du paludisme, mais pas suffisamment pour atteindre l’objectif fixé. ■ Vingt-sept pays (29 %) ont enregistré une hausse de l’incidence, et dans 14 pays (15 %) elle était en hausse de 40 % ou plus en 2021 par rapport à 2015. ■ Dans huit pays (8,6 %), l’incidence du paludisme en 2021 a été estimée à un niveau équivalent à celui de 2015. ■ Quarante-trois pays (46 %) où le paludisme était endémique en 2015 ont atteint l’objectif intermédiaire du GTS pour 2021 en matière de mortalité, et 28 d’entre eux ont rapporté zéro décès lié au paludisme. ■ Vingt-deux pays (24 %) ont réduit la mortalité due au paludisme, mais leurs progrès sont restés en deçà de l’objectif de 40 %. ■ En 2021, la mortalité due au paludisme est restée au même niveau qu’en 2015 dans neuf pays (9,7 %) ; elle a augmenté dans 19 pays (20 %), et de 40 % ou plus dans 11 d’entre eux. ■ La région Asie du Sud-Est de l’OMS a atteint les objectifs intermédiaires du GTS pour 2020 à la fois en matière de morbidité et de mortalité. Tous les pays de la région, à l’exception de l’Indonésie, ont réduit l’incidence et la mortalité de 40 % ou plus. MENACES BIOLOGIQUES ET AUTRES PESANT SUR LES OUTILS D’INTERVENTION Suppression des gènes pfhrp2/3 du parasite ■ Les parasites incapables d’exprimer la protéine riche en histidine 2 (HRP2) présentent le risque de ne pas être détectés par les TDR basés sur HRP2. Quant aux parasites qui n’expriment plus HRP2 ni la protéine riche en histidine 3 (HRP3), ils sont complètement indétectables au moyen de ce type de TDR. ■ L’OMS a recommandé aux pays rapportant des suppressions des gènes pfhrp2/3 et à leurs pays voisins de mener des études de référence représentatives sur les cas suspectés de paludisme, afin de déterminer si la prévalence des suppressions pfhrp2/3 causant des « faux » résultats de TDR négatifs avait atteint un seuil qui nécessite un changement de TDR (suppressions du gène pfhrp2 > 5 % causant des faux résultats de TDR négatifs). xlvii ■ Les alternatives aux TDR (par exemple, basées sur la détection du lactate déshydrogénase du parasite [pLDH]) sont limitées. Il n’existe à l’heure actuelle aucune combinaison de tests non HRP2 préqualifiée par l’OMS, capable de faire la distinction entre P. falciparum et P. vivax. ■ L’OMS effectue un suivi des rapports publiés sur les suppressions des gènes pfhrp2/3 par le biais de l’outil de cartographie Carte des menaces du paludisme, et encourage une approche harmonisée de cartographie et de signalement des suppressions des gènes pfhrp2/3 grâce à des protocoles d’enquête accessibles au public. ■ En 2022, plusieurs pays de la région Afrique de l’OMS ont initié la planification et la conduite d’études représentatives sur les suppressions des gènes pfhrp2/3. Les résultats sont attendus en 2022 et 2023. ■ L’OMS a introduit un tableau de bord dans le but d’effectuer le suivi des activités de surveillance dans le monde et de fournir ainsi des renseignements sur les priorités, l’affectation des ressources et les prévisions concernant les TDR, ainsi que d’éviter la duplication des efforts. Ce tableau de bord comprend les principales caractéristiques des activités de surveillance et le calendrier correspondant. ■ Entre septembre 2021 et septembre 2022, des enquêtes sur la suppression des gènes pfhrp2/3 ont été rapportées dans 17 publications émanant de 17 pays : Bénin, Brésil, Cameroun, Djibouti, Équateur, Érythrée, Éthiopie, Gabon, Ghana, Guinée équatoriale, Inde, Kenya, Madagascar, République démocratique du Congo, République Unie de Tanzanie, Rwanda et Sierra Leone. Parmi ces pays, seuls la Guinée équatoriale, le Kenya et le Rwanda n’ont pas détecté de suppressions du gène pfhrp2, bien que ces trois pays aient signalé des suppressions dans des publications précédentes. ■ En se basant sur les données de ces publications, y compris la Carte des menaces du paludisme, une forme d’enquête a été menée dans 47 pays, et la présence de suppressions est confirmée dans 40 d’entre eux. ■ Dans son plan de réponse mondiale aux délétions pfhrp2/3, l’OMS expose plusieurs domaines d’action allant au-delà de l’intensification de la surveillance. Ces autres domaines d’action incluent l’identification de nouveaux biomarqueurs, l’amélioration des performances des TDR non HRP2, la réalisation de prévisions de marché et le renforcement des réseaux de laboratoires pour soutenir la demande concernant l’utilisation de la caractérisation moléculaire pour déterminer la présence ou l’absence de ces suppressions de gènes. Résistance des parasites aux antipaludiques ■ L’efficacité des médicaments antipaludiques fait l’objet d’une surveillance par le biais d’études relatives à l’efficacité thérapeutique, qui suivent les résultats cliniques et parasitologiques parmi les patients recevant des médicaments antipaludiques. Ces études relatives à l’efficacité thérapeutique sont considérées comme la norme de référence grâce à laquelle les pays peuvent établir au mieux leurs politiques de traitement. ■ Plusieurs outils permettent d’évaluer la résistance aux médicaments antipaludiques. Dans certains cas, des mutations génétiques associées à une sensibilité réduite à des médicaments antipaludiques ont été identifiées. La résistance partielle à l’artémisinine est surveillée grâce à une liste établie de marqueurs PfKelch13 candidats et validés, associés à une élimination retardée suite à un traitement contenant de l’artémisinine. ■ L’OMS compile les résultats des études menées sur l’efficacité des médicaments antipaludiques et sur la résistance à ces médicaments, avant de les mettre à disposition via la Carte des menaces du paludisme. ■ Région Afrique de l’OMS : entre 2015 et 2021, 266 études relatives à l’efficacité thérapeutique contre les infections à P. falciparum ont été menées au total dans la région Afrique de l’OMS en suivant le protocole normalisé de l’OMS, avec la participation d’au moins 20 patients. Parmi toutes ces études, six ont constaté un taux d’échec supérieur à 10 % : quatre avec l’artéméther-luméfantrine (AL) au Burkina Faso et en Ouganda, et deux avec la dihydroartémisinine-pipéraquine (DHA-PPQ) au Burkina Faso. Cinq autres études avec l’AL et deux avec la DHA-PPQ basées sur une méthodologie sans rapport avec les recommandations de l’OMS, mais opérant une distinction entre réinfection et recrudescence, ont rapporté des taux d’échec au traitement supérieurs à 10 %. Ces études ont été menées en Angola, en République démocratique du Congo et en Ouganda. Des mutations du gène PfKelch13 sont apparues en Érythrée, au Rwanda et en Ouganda. Les taux d’échec au traitement y restent inférieurs à 10 %, car le médicament partenaire demeure efficace. D’autres études sont nécessaires pour déterminer l’étendue de la propagation des mutations du gène PfKelch13, ainsi que pour étudier tout changement de durée de clairance parasitaire et de résistance in vitro. xlviii W O RL D M AL AR IA R EP O RT 2 02 2 ■ Région Amériques de l’OMS : les données tirées d’études relatives à l’efficacité thérapeutique fournies par la région Amériques de l’OMS sont limitées. Les études relatives à l’efficacité thérapeutique de l’AL conduites entre 2015 et 2021 au Brésil et en Colombie ont démontré une efficacité élevée. Au Guyana, la mutation PfKelch13 C580Y associée à la résistance à l’artémisinine a été observée sporadiquement entre 2010 et 2017, mais n’a pas été détectée parmi les échantillons plus récents, ce qui indique que cette mutation a probablement disparu. ■ Région Méditerranée orientale de l’OMS : sur les 36 études relatives à l’efficacité thérapeutique contre les infections à P. falciparum menées dans la région Méditerranée orientale de l’OMS avec la participation d’au moins 20 patients, deux études avec l’artésunate-sulfadoxine-pyriméthamine (AS + SP) en Somalie et au Soudan ont constaté un taux d’échec supérieur à 10 %. Dans ces pays, les traitements de première intention sont désormais à base d’AL. ■ Région Asie du Sud-Est de l’OMS : sur les 67 études relatives à l’efficacité thérapeutique contre les infections à P. falciparum menées dans la région Asie du Sud-Est de l’OMS avec la participation d’au moins 20 patients, aucune n’a signalé un taux d’échec supérieur à 10 %. Toutefois, la présence de mutations associées à la résistance à la SP en Inde centrale pourrait être un signe avant coureur de l’échec au traitement par AS + SP. En Thaïlande, où l’efficacité des médicaments est évaluée grâce à une surveillance intégrée de l’efficacité thérapeutique, les taux d’échec au traitement par DHA-PPQ et primaquine se sont avérés élevés dans la province du Sisaket. Cette province a donc modifié son traitement de première intention pour adopter l’artésunate-amodiaquine (AS-AQ) en 2020. Dans la sous-région du Grand Mékong, les mutations PfKelch13 associées à la résistance à l’artémisinine affichent une forte prévalence au Myanmar et en Thaïlande. ■ Région Pacifique occidental de l’OMS : sur les 63 études relatives à l’efficacité thérapeutique contre les infections à P. falciparum menées dans la région Pacifique occidental de l’OMS avec la participation d’au moins 20 patients, quatorze ont constaté un taux d’échec supérieur à 10 %. Seule une étude avec l’AL menée en République démocratique populaire lao en 2017 a détecté de forts taux d’échec (17,2 %), mais cette étude n’était basée que sur 29 patients et les études ultérieures ont prouvé la haute efficacité de l’AL. Deux études au Cambodge ont observé de forts taux d’échec avec l’artésunate- amodiaquine (AS-AQ), ce qui indique la présence d’une résistance à l’AQ au Cambodge. Des taux importants d’échec au traitement par DHA-PPQ ont été détectés au Cambodge, en République démocratique populaire lao et au Viet Nam, poussant ces pays à changer de médicament pour leur traitement de première intention. Dans la sous région du Grand Mékong, les mutations PfKelch13 associées à la résistance à l’artémisinine affichent une forte prévalence au Cambodge, en République démocratique populaire lao et au Viet Nam. La mutation PfKelch13 C580Y est également apparue en Papouasie Nouvelle Guinée et elle semble se propager. Résistance des vecteurs aux insecticides ■ De 2010 à 2020, quelque 88 pays ont transmis à l’OMS des données de surveillance sur la résistance aux insecticides, y compris sur l’intensité de la résistance aux pyréthoïdes (38 pays), ainsi que sur la capacité du butoxyde de pipéronyle (PBO) à restaurer la sensibilité aux pyréthoïdes (32 pays). ■ En 2020, de nouveaux dosages discriminants et de nouvelles procédures de surveillance de la résistance des vecteurs du paludisme au chlorofénapyr, à la clothianidine, à la transfluthrine, au flupyradifurone et au pyriproxyfène ont été mis à disposition. Par ailleurs, les dosages discriminants pour le pyrimiphos-méthyl et l’alpha-cyperméthrine ont été révisés. Les pays doivent ajuster la surveillance de la résistance des vecteurs du paludisme aux insecticides conformément à ces nouvelles procédures. L’OMS n’a pas reçu de données sur la résistance des vecteurs à la transfluthrine, au flupyradifurone et au pyriproxyfène. Même si l’OMS a reçu quelques données sur la surveillance de la résistance au chlorfénapyr et à la clothianidine, ces données restent insuffisantes pour évaluer toute résistance potentielle à l’un de ces deux insecticides. ■ Sur les 88 pays d’endémie ayant fourni des données pour la période 2010-2020, 78 ont détecté une résistance à au moins une des classes d’insecticides chez l’un des vecteurs du paludisme et sur un site de collecte. Par ailleurs, 29 pays ont constaté une résistance aux pyréthoïdes, aux organochlorés, aux carbamates et aux organophosphorés sur différents sites, et 19 pays ont confirmé la résistance à ces quatre classes d’insecticides chez au moins un des vecteurs du paludisme et sur au moins un site de collecte. ■ Au niveau mondial, la résistance aux pyréthoïdes, la principale classe d’insecticides actuellement utilisés dans les MII, est largement répandue. Elle a été détectée chez au moins un des vecteurs du paludisme sur 68 % des sites pour lesquels des données sont disponibles. La résistance aux organochlorés a été rapportée sur 64 % des sites. La résistance aux carbamates et aux xlix organophosphorés a été moins prévalente, mais a été détectée, respectivement, sur 34 % et 28 % des sites disposant de données de surveillance. ■ Sur les 38 pays ayant fourni des données sur l’intensité de la résistance aux pyréthoïdes, une résistance de forte intensité a été observée sur 293 sites répartis dans 27 pays. ■ Depuis 2010, il a été noté que le butoxyde de pipéronyle (PBO) avait complètement restauré la sensibilité aux pyréthoïdes sur 283 sites répartis dans 29 pays. ■ Pour orienter la gestion de la résistance, les pays doivent développer et mettre en œuvre des plans nationaux de suivi et de gestion de la résistance aux insecticides, en se basant sur le Cadre conceptuel d’un plan national de suivi et de gestion de la résistance aux insecticides chez les vecteurs du paludisme élaboré par l’OMS. Le nombre de pays ayant établi un tel plan est passé de 53 en 2019 à 67 en 2020. ■ Un support technique et financier est nécessaire pour aider les pays à surveiller et à gérer la résistance aux insecticides. ■ Toutes les données standard sur la résistance aux insecticides rapportées à l’OMS sont intégrées à la base de données mondiales de l’OMS sur la résistance aux insecticides chez les vecteurs du paludisme, et leur accès à des fins d’exploration est possible via la Carte des menaces du paludisme. Efficacité des MII ■ Les MII sont considérées comme la raison principale de la baisse de la transmission du paludisme et du poids de la maladie durant la période 2005-2015, surtout dans les zones de transmission modérée à élevée. Les moustiquaires imprégnées d’insecticide longue durée (MILD) restent efficaces et l’OMS en recommande l’utilisation continue pour prévenir le paludisme. ■ Comme les MILD constituent le principal outil de lutte antivectorielle, les facteurs nuisant à leur efficacité dans le cadre de la prévention du paludisme sont importants pour les progrès réalisés contre la maladie. Ces facteurs comprennent la résistance physique de la moustiquaire (c’est-à-dire l’intégrité de sa structure) et sa longévité chimique (à savoir sa bio efficacité, qui correspond à la disponibilité de l’ingrédient actif à la surface de la moustiquaire au fil du temps), les contraintes opérationnelles et comportementales (livraison, accès, couverture et acceptation, utilisation, maintenance et préservation) et la dynamique des vecteurs (comportements des espèces lors des piqûres et au repos). ■ L’émergence et la large propagation géographique de la résistance aux pyréthoïdes forment la menace la plus reconnue à l’efficacité des moustiquaires imprégnées de pyréthoïdes longue durée. ■ Une étude clinique réalisée dans plusieurs pays, commandée par l’OMS et publiée en 2018 a montré que les MII continuaient de représenter un haut niveau de protection contre le paludisme, même en présence d’une forte résistance aux pyréthoïdes. ■ À l’inverse, les données tirées de plusieurs études sur le terrain dans des cases expérimentales suggèrent que, plus la sensibilité des vecteurs aux pyréthoïdes diminue, plus les effets de répulsion et de mortalité sur les moustiques s’amenuisent. ■ Une analyse par modélisation suggère d’ailleurs que l’impact épidémiologique des MII diminue en cas de hauts niveaux de résistance aux pyréthoïdes. ■ La plus grande efficacité observée lors des essais aléatoires et contrôlés de certaines des MII de nouvelle génération (par rapport aux MII imprégnées uniquement de pyréthoïdes) laisse penser que la résistance aux insecticides a un impact sur les résultats épidémiologiques. ■ En Afrique subsaharienne, destinataire de la plupart des MII, près de 590 millions de MII ont été distribuées aux communautés sur la période 2019-2021. Toutefois, en 2021, le pourcentage de la population ayant eu accès à une MII au sein des ménages et le pourcentage de la population dormant sous MII a été estimé à 54 % et 47 %, respectivement, du fait de plusieurs facteurs. ■ Répartir efficacement les MII, les employer et les préserver comptent parmi les facteurs affectant la part de la population qui possède et utilise une moustiquaire. La répartition équitable des MII s’appuie sur l’identification des écarts de couverture au niveau local et l’élargissement de la distribution dans les zones concernées. La préservation des MII est déterminée par les attitudes des ménages vis-à-vis des moustiquaires, les comportements lors de leur manipulation et d’autres aléas, ainsi que la résistance des matériaux et de la structure des moustiquaires. Même si elle varie d’une zone à l’autre, la durée de vie médiane des MII est égale à 1,9 an. Pour finir, les moustiquaires ne sont pas utilisées 100 % du temps même si elles sont à disposition des membres du foyer : leur usage dépend de l’âge et du sexe de chaque membre, de la saison et du risque de paludisme. l W O RL D M AL AR IA R EP O RT 2 02 2 ■ Une analyse d’illustration a été lancée par modélisation. Elle a pour point de départ une MILD « idéale » (à savoir une moustiquaire parfaitement répartie, jamais laissée de côté, toujours utilisée, hautement insecticide et de longue durée) et explore des scénarios au cours desquels chacune des contraintes précitées est réintroduite au fur et à mesure. ■ Cette analyse a démontré que l’efficacité en baisse de l’insecticide et la diminution de la résistance physique de la moustiquaire sont les principaux facteurs de réduction de son efficacité. En combinant ces facteurs avec les obstacles liés à l’accès aux MII, leur taux d’utilisation et leur préservation, l’impact d’une campagne de distribution de MII décroît considérablement à la fin de la troisième année. ■ Des solutions sont nécessaires pour améliorer la durabilité chimique et physique des MILD, leur répartition efficace, leur maintenance et leur utilisation par les membres des ménages. De plus, des moustiquaires à double substance active ou des MILD totalement non pyréthoïdes sont essentielles pour atténuer l’impact de la résistance répandue aux insecticides. Efficacité de la PID ■ La PID est la deuxième intervention de lutte antivectorielle la plus mise en œuvre par les programmes nationaux de lutte contre le paludisme (PNLP). Il n’existe toutefois que très peu de résultats d’essais sur l’efficacité de la PID. ■ À partir des données d’observation, il apparaît qu’une PID menée correctement est une intervention de poids pour réduire la densité et la longévité des moustiques adultes vecteurs, et donc la transmission du paludisme. ■ Certains des facteurs ayant des répercussions sur l’efficacité de la PID et des MII sont identiques. Par exemple, la majeure partie de la population de vecteurs se nourrit et se repose à l’intérieur, les gens dorment généralement la nuit à l’intérieur, et les niveaux d’acceptation au sein de la communauté sont élevés. Les facteurs spécifiques à la PID comprennent la période de pulvérisation et le niveau de formation des opérateurs de pulvérisation (ce qui en affecte la qualité). ■ Mettre en œuvre la PID avec le niveau de qualité requis est plus compliqué sur le plan logistique et coûte nettement plus cher que de distribuer des MII. ■ Une étude récente a démontré que le coût annuel d’une personne protégée par la PID était cinq fois supérieur au coût d’une personne protégée par MII, ce qui signifie que la PID est nettement moins rentable. ■ Une étude menée en Éthiopie suggère que les services communautaires de PID (où les opérateurs de pulvérisation et le matériel nécessaire sont plus proches des ménages, et où les agents de santé communautaires sont impliqués) peuvent réduire les coûts, améliorer l’efficacité et élargir la couverture sans nuire à la performance. ■ Même dans le cas de services communautaires, les coûts totaux restent élevés. S’il faut y ajouter le coût de la maintenance de l’équipement et de la supervision continue des opérateurs, il est clair que l’intensification à grande échelle des campagnes de PID réclame un budget conséquent. ■ Ces coûts vont probablement continuer d’augmenter du fait de l’apparition de la résistance aux insecticides utilisés pour les PID et de la nécessité d’opter pour des produits chimiques plus onéreux. Au-delà des MII et de la PID : résoudre le problème de la transmission résiduelle ■ Au-delà de l’adaptation génétique des moustiques aux insecticides (i. e. résistance aux insecticides), comprendre l’adaptation comportementale des moustiques, à savoir la modification des habitudes de piqûres, d’alimentation et de repos, s’avère tout aussi important. ■ De plus en plus d’éléments tendent à prouver que les vecteurs s’efforcent de réduire les contacts avec les insecticides (en piquant plus tôt dans la journée, avant que les gens ne se mettent au lit, en passant plus de temps à se reposer dehors ou en se nourrissant sur le bétail plutôt que sur la population, par exemple). ■ Les vecteurs affichant cette malléabilité comportementale se taillent une place dominante par rapport aux vecteurs dont les changements comportementaux sont limités. L’adaptation comportementale des vecteurs va exiger des investissements en matière de R&D pour développer des interventions ciblant la transmission en extérieur. li R&D EN MATIÈRE DE PALUDISME ET PRODUITS EN COURS DE DÉVELOPPEMENT Caractéristiques produits préférés et profils de produits ciblés ■ Le programme mondial de lutte antipaludique de l’OMS a participé à l’élaboration de caractéristiques produits préférés (PPC) et de profils de produits ciblés (TPP), afin qu’ils servent d’outils essentiels pour inciter et guider le développement de produits à fort impact sur la santé publique et adaptés à une utilisation dans les pays à revenu faible et intermédiaire. ■ L’OMS coordonne actuellement l’établissement de TPP pour les essais sur les lieux de soins de la glucose-6-phosphate déshydrogénase (G6PD) afin de répondre aux exigences de la lutte contre les infections à P. vivax. ■ En se fondant sur l’expérience acquise avec le vaccin RTS,S, l’OMS a publié en 2022 des PCC actualisées au sujet des vaccins antipaludiques. Ces PPC comprennent un ensemble plus large d’objectifs stratégiques en matière de R&D autour des vaccins antipaludiques en vue de prévenir les infections au stade sanguin, de réduire la morbidité et la mortalité, ainsi que de diminuer la transmission au niveau communautaire. ■ L’OMS a également élaboré plusieurs PPC dans le domaine de la lutte antivectorielle, y compris des PCC pour les MII servant à lutter contre la transmission du paludisme au sein des populations de moustiques résistantes aux insecticides (publication en 2021) ; pour les interventions de lutte antivectorielle afin de lutter contre le paludisme dans des situations d’urgences humanitaires complexes et en réponse aux catastrophes naturelles (publication en 2021) ; pour les produits endectocides et ectocides destinés à la lutte contre la transmission du paludisme (publication en 2022) ; et pour le traitement, avec effet rémanent, des surfaces intérieures pour lutter contre la transmission du paludisme dans les zones où les moustiques sont résistants aux insecticides (publication en 2022). ■ L’OMS a développé des PPC pour les médicaments antipaludiques en chimioprévention, qui ont pour but d’aborder divers cas d’utilisation : la chimioprévention chez les enfants (chimioprévention du paludisme saisonnier [CPS], chimioprévention du paludisme pérenne [CPP, précédemment appelée « traitement préventif intermittent du nourrisson »], traitement préventif intermittent chez les enfants d’âge scolaire [TPIsc] et chimioprévention du paludisme après la sortie de l’établissement de soins [PDMC], durant la grossesse (TPIp) et chez les voyageurs non immunisés. ■ En novembre 2021, le Programme mondial de lutte antipaludique de l’OMS et son Département Vaccination, vaccins et produits biologiques ont rassemblé un groupe de développement scientifique afin d’élaborer des PPC et d’examiner les considérations liées au développement clinique d’anticorps monoclonaux (AcM) pour la prévention du paludisme. La priorité la plus urgente en matière de santé publique est de réduire la morbidité et la mortalité dues à P. falciparum chez les nourrissons et les enfants. ■ L’OMS a également travaillé en étroite coordination avec des partenaires de développement de produits, tels que FIND (Foundation for Innovative New Diagnostics), MMV (Medicines for Malaria Venture) et le Consortium innovant de lutte antivectorielle (IVCC), pour collecter une base de données sur des produits antipaludiques actuellement en phase de R&D. Tests de diagnostic et médicaments antipaludiques ■ A sujet des tests de diagnostic du paludisme, la propagation de parasites P. falciparum avec des suppressions des gènes pfhrp2/3 représente une menace grave pour la fiabilité des diagnostics. Un éventail diversifié d’outils de diagnostic est nécessaire pour résoudre ce problème de fiabilité. La sensibilité inadaptée des outils face aux espèces non-P. falciparum et une gamme plus large de cas d’utilisation de diagnostics (par exemple, un dépistage non invasif extrêmement sensible pour la prévention de la réintroduction) représentent les principaux défis en matière de R&D. ■ Les travaux de R&D en cours englobent des produits qui permettront de combler ces lacunes en déployant des efforts pour améliorer les plates-formes de soins existantes (en particulier, la microscopie et les tests de détection d’un antigène par immunochromatographie à flux latéral) et pour développer des alternatives basées sur une sélection de types d’échantillons et de technologies. ■ Pour l’heure, les activités de R&D autour des médicaments antipaludiques donnent la priorité au développement de médicaments de nouvelle génération capables de sauver des vies parmi la population adulte, plus particulièrement les femmes enceintes, et les enfants présentant un risque lii W O RL D M AL AR IA R EP O RT 2 02 2 élevé par les conséquences d’une infection palustre. Une autre des priorités concerne la fourniture d’une option thérapeutique non ACT afin de simplifier le traitement ou de se prémunir contre l’intensification éventuelle de la résistance à l’artémisinine ou aux médicaments partenaires. ■ Sous la coordination de MMV, les travaux de R&D dans le domaine des médicaments antipaludiques tournent autour de deux grands axes : innover sur la base des traitements antipaludiques actuels et développer la prochaine génération de traitements antipaludiques. En ce qui concerne les traitements existants, des projets sont en cours sur une ACT triple, une ACT additionnée à une seule dose faible de primaquine pour bloquer la transmission et l’artéméther-luméfantrine pour les nouveau nés. ■ Plusieurs médicaments de nouvelle génération entrent dans différentes phases d’essai : ganaplacide- luméfantrine, M5717-pyronaridine, ZY19489-ferroquine et cipargamine. Sept molécules de nouvelle génération sont également à l’étude à l’heure actuelle. Lutte antivectorielle ■ L’Observatoire mondial de la recherche-développement en santé (GOHRD) de l’OMS répertorie à l’heure actuelle 28 produits de lutte antivectorielle en phase de R&D. Onze de ces produits (39 %) sont des MII et sept (25 %) sont des traitements à effet rémanent pour les murs intérieurs des habitations. Treize produits (46 %) en sont à l’étape de génération des données concernant l’évaluation de la sécurité, la qualité et l’efficacité entomologique. Sept produits (25 %) sont soumis à des essais épidémiologiques, six (21 %) sont en cours d’évaluation par l’OMS en vue de répondre aux exigences de préqualification ou des recommandations de politiques de l’OMS, et deux (7 %) en sont au stade du développement d’un prototype. ■ Le Consortium innovant de lutte antivectorielle (IVCC) a fourni une vue d’ensemble des produits destinés aux interventions antipaludiques, qui ont été ou sont encore en cours de développement. Cette vue d’ensemble dresse la liste de plusieurs insecticides, nouveaux ou recyclés, offrant différents modes d’action, à utiliser pour les MII et la PID afin de soutenir les meilleures pratiques en matière de gestion de la résistance aux insecticides. ■ Le pyrimiphos-méthyl (listé par l’OMS dans le système OMS d’évaluation des pesticides utilisés en santé publique [WHOPES] en janvier 2013), la clothianidine (préqualifiée par l’OMS en 2017) et la clothianidine associée à la deltaméthrine (préqualifiée par l’OMS en 2018) figurent parmi les produits que l’IVCC a aidé à commercialiser pour la PID. ■ Le chlorfenapyr et le broflanilide sont également soumis à étude et devraient figurer sous peu dans la liste de préqualification. ■ Interceptor® G2, une nouvelle MII, combine un pyréthoïde et un insecticide recyclé, le chlorfenapyr, qui offre un mode d’action différent. Le mélange de ces deux agents actifs est un pas de plus pour lutter contre la résistance aux insecticides. Trois autres agents actifs en sont soit au dernier stade de la recherche, soit en voie de développement intégral. Il est prévu qu’ils obtiennent une préqualification entre 2026 et 2030. ■ Une boîte à outils, enrichie, est en cours de développement pour la lutte antivectorielle. Elle comprend un larvicide, des pièges à moustiques (« Eave Tubes ») et des appâts sucrés toxiques attrayants (ATSB). Ces outils font partie intégrante de l’arsenal de gestion de la résistance aux insecticides et de réduction du nombre de piqûres à l’extérieur. Vaccins ■ Les vaccins antipaludiques actuellement en cours de développement clinique ciblent la phase pré érythrocytaire du parasite. Autrement dit, ils se focalisent sur les sporozoïtes ou la phase hépatique du paludisme, la libération des parasites dans la circulation sanguine, les gamétocytes ou le développement du parasite chez le moustique. ■ Le tableau de bord sur les vaccins du GOHRD fournit des informations détaillées à ce sujet. Pour résumer, sur les divers vaccins expérimentaux en cours de développement, six ciblent le parasite au stade pré érythrocytaire (P. falciparum), trois ciblent la phase sanguine (P. falciparum) et six ciblent l’étape sexuelle ou le blocage de la transmission (dont quatre s’intéressent au paludisme à P. vivax et deux au paludisme à P. falciparum durant la grossesse). liii Dr Tedros Adhanom Ghebreyesus Director General Organización Mundial de la Salud (OMS) Cada año, el Informe Mundial sobre la Malaria resalta los avances y las deficiencias en la lucha contra esta enfermedad en todo el mundo. El informe de este año se organiza en torno a cuatro temas: respuesta, riesgos, resiliencia e investigación. Los países afectados por la malaria siguen respondiendo a una serie de retos, entre ellos las alteraciones derivadas de la pandemia de COVID-19. Aunque duramente golpeados, la mayoría de los países resistieron y pudieron mantener los servicios de prevención, detección y tratamiento de la malaria, una hazaña notable en medio de una pandemia. No obstante, más de 600 000 personas siguen muriendo por malaria cada año, la mayoría niños. Incluso con los heroicos esfuerzos por mantener los servicios durante la pandemia, los esfuerzos de control de malaria se enfrentan a muchos obstáculos, además de las ya importantes alteraciones relacionadas con la COVID y otros retos de los sistemas de salud, como las largas crisis humanitarias, la limitada financiación por parte de los donantes y el posible efecto del cambio climático en la propagación de la enfermedad. Estos retos se han visto agravados por la disminución de la efectividad de nuestras principales herramientas de lucha contra la malaria. El aumento de la resistencia a los mosquiteros tratados con insecticida y a los regímenes de tratamientos antimaláricos sigue siendo motivo de gran preocupación, sobre todo en África. En algunas zonas, los parásitos de la malaria escapan a la detección de las pruebas de diagnóstico más utilizadas. Y la invasión de un mosquito que se adapta fácilmente a los entornos urbanos, el Anopheles stephensi, supone un riesgo real. Para apoyar la capacidad de recuperación de los programas contra la malaria, la OMS ha publicado nuevas orientaciones, entre ellas una nueva estrategia para contener la resistencia a los medicamentos antimaláricos en África; un nuevo marco, elaborado conjuntamente por la OMS y ONU-Hábitat, para orientar a los dirigentes de las ciudades en la lucha contra la malaria urbana; y recomendaciones para detener la propagación del Anopheles stephensi en entornos urbanos. Se recomienda a los países adaptar las orientaciones de la OMS a las necesidades locales. El control de la malaria está íntimamente ligado a los sistemas de salud. Las inversiones para que los sistemas de salud funcionen bien, sean equitativos y resilientes y se basen en la atención primaria son fundamentales para el éxito de la respuesta contra la malaria. Prefacio liv W O RL D M AL AR IA R EP O RT 2 02 2 La investigación y el desarrollo también desempeñan un papel fundamental. Como se describe en estas páginas, se están desarrollando nuevos tipos de tecnologías de control de vectores, incluida una nueva generación de mosquiteros para combatir la resistencia de los mosquitos. También se están preparando nuevos diagnósticos e innovaciones en los medicamentos contra la malaria. Otro gran avance es la vacuna antimalárica RTS,S, la primera y única vacuna recomendada para prevenir la malaria en niños. Más de 1,2 millones de niños están protegidos por la vacuna en Ghana, Kenia y Malawi, y pronto se ampliará a otros países. Se están desarrollando otras herramientas prometedoras contra la malaria. Sin embargo, sigue habiendo un importante déficit de financiación para la investigación básica y el desarrollo de productos. Hay una necesidad urgente de aumentar la inversión en nuevas herramientas contra la malaria para hacer frente a amenazas emergentes como la malaria urbana y la propagación de la resistencia a los medicamentos antimaláricos. Nos enfrentamos a muchos retos, pero hay muchas razones para la esperanza. Al fortalecer la respuesta, comprender y mitigar los riesgos, aumentar la resiliencia y acelerar la investigación, habría muchas razones para soñar con un futuro libre de malaria. lv El informe de este año de un vistazo EVENTOS CLAVE EN 2021–2022 La séptima reposición del Fondo Mundial de Lucha contra el Sida, la Tuberculosis y la Malaria ■ En 2021, más del 40% de la inversión mundial en malaria se canalizó a través del Fondo Mundial de Lucha contra el Sida, la Tuberculosis y la Malaria (Fondo Mundial). ■ En septiembre de 2022, los países y socios del Fondo Mundial recaudaron un total de 15.900 millones de dólares durante su reposición, lo que supuso la mayor reposición en la historia del Fondo Mundial. ■ La reposición no alcanzó el objetivo de 18.000 millones de dólares en un momento en que el coste de productos sanitarios está aumentando debido a la pandemia del coronavirus (COVID-19), los conflictos y la recesión mundial. ■ El déficit significa que la reposición actual estará sometida a una presión considerable para mantener y ampliar los niveles de cobertura de las intervenciones contra la malaria. ■ Para optimizar el impacto de la financiación disponible, será aún más necesario maximizar el uso de los recursos para luchar contra la malaria y otros recursos del sistema de salud, de forma eficiente, eficaz y equitativa. ■ Los países deben ser capaces de identificar la combinación óptima de intervenciones que se adapten a su contexto local, y los mejores medios para hacerlas llegar a todas las personas que las necesiten, guiándose por datos locales. Nuevas recomendaciones de la Organización Mundial de la Salud ■ En junio de 2022, la Organización Mundial de la Salud (OMS) publicó orientaciones con recomendaciones nuevas y actualizadas. Las directrices alientan a los países a adaptar las recomendaciones a los entornos locales para lograr el máximo impacto. ■ Las directrices actualizadas proporcionan recomendaciones sobre el tratamiento preventivo intermitente de la malaria en el embarazo (TPI), la quimio prevención de la malaria perenne (QMP) y la quimio prevención de la malaria estacional (QME), el tratamiento preventivo intermitente de la malaria en niños en edad escolar (TPIesc), la quimio prevención de la malaria después del alta (QMDA), la administración masiva de medicamentos (AMM) y la eliminación. ■ Se sigue recomendando encarecidamente el uso de TPI con sulfadoxina-pirimetamina (SP) en zonas de transmisión de Plasmodium falciparum moderada o alta de malaria. ■ El TPI-SP se recomienda ahora para todas las mujeres embarazadas, independientemente del número de embarazos. ■ La recomendación actualizada de la quimio prevención de la malaria estacional y de la malaria perenne promueve el uso más amplio de la quimio prevención entre los niños pequeños con alto riesgo de malaria grave en zonas con transmisión estacional y durante todo el año. ■ La OMS también ha emitido una nueva recomendación sobre el uso de tratamiento preventivo intermitente de la malaria en niños en edad escolar (TPIesc) en entornos con transmisión de malaria moderada a alta perenne o estacional. El TPIesc debe cubrir a los niños de 5 a 15 años, y su introducción no debe comprometer las intervenciones de quimio prevención para los niños menores de 5 años, que son los que corren mayor riesgo de padecer malaria grave. lvi W O RL D M AL AR IA R EP O RT 2 02 2 ■ La quimio prevención de la malaria después del alta (QMDA) se recomienda ahora para prevenir la malaria en niños con anemia grave que viven en zonas de transmisión moderada o alta después de recibir el alta hospitalaria, cuando corren un alto riesgo de reingreso o muerte. ■ Las nuevas recomendaciones sobre la administración masiva de medicamentos AMM para malaria dan orientación para reducir rápidamente la carga de la enfermedad en situaciones de emergencia y en zonas de transmisión moderada a alta. También ofrecen orientación sobre el uso de la AMM para reducir la malaria por P. falciparum en entornos con una transmisión muy baja o baja, y para reducir la transmisión por P. vivax. ■ Las recomendaciones para la eliminación incluyen estrategias "masivas" aplicadas a toda la población de una zona geográfica delimitada, ya sea una localidad, un municipio o un distrito; estrategias "selectivas" aplicadas a personas con mayor riesgo de infección que la población general; y estrategias "reactivas" que se deben activar en respuesta a casos individuales. Nuevas iniciativas regionales lanzadas por la OMS en colaboración con socios Estrategia para responder a la resistencia a medicamentos antimaláricos en África ■ La aparición de resistencia parcial a la artemisinina en la región africana de la OMS, con la aparente rápida propagación de mutaciones asociadas a esta resistencia, es muy preocupante. Esta aparición no se ha propagado desde la subregión del Gran Mekong, sino que ha aparecido de novo en el continente. ■ Las terapias combinadas basadas en la artemisinina (TCA) siguen siendo eficaces, pero, aunque no se ha confirmado la resistencia a los medicamentos asociados a las TCA que se utilizan actualmente, hay algunas señales preocupantes que deben investigarse y hay que actuar antes de que las TCA empiecen a fallar. ■ Dada la gran dependencia en las TCA en África, la amenaza de la resistencia parcial a la artemisinina y la resistencia a los medicamentos asociados debe vigilarse y abordarse con urgencia. ■ En noviembre de 2022, la OMS lanzó la Estrategia para responder a la resistencia a los medicamentos antimaláricos en África, cuyo objetivo es proporcionar orientación a las principales partes interesadas en la comunidad de la malaria. ■ La estrategia aborda la amenaza de la resistencia a los medicamentos antimaláricos en la Región de África de la OMS a través de cuatro pilares: ■ Pilar I – Reforzar la vigilancia de la eficacia de y la resistencia a los medicamentos antimaláricos. ■ Pilar II – Optimizar y regular mejor el uso del diagnóstico y tratamiento para limitar la presión de los medicamentos mediante medidas preventivas. ■ Pilar III – Reaccionar ante la resistencia limitando la propagación de parásitos resistentes a los medicamentos antimaláricos. ■ Pilar IV – Estimular la investigación y la innovación para aprovechar mejor las herramientas existentes y desarrollar otras nuevas contra la resistencia. ■ Cada pilar comprende un conjunto de intervenciones que pueden aplicarse a nivel local, regional y mundial. Los países deben adaptar las intervenciones de la estrategia a su contexto local. Iniciativa para detener la propagación de Anopheles stephensi ■ Anopheles stephensi, originario del sur de Asia y partes de la Península Arábiga, ha ido ampliando su área de distribución en la última década, con detecciones notificadas en Yibuti (2012), Etiopía y Sudán (2016), Somalia (2019) y Nigeria (2020). ■ En los casos en que se ha notificado la presencia de An. stephensi en la Región de África de la OMS, se ha descubierto que es resistente a muchos de los insecticidas utilizados en salud pública, lo que supone un reto adicional para su control. ■ La invasión de An. stephensi en el África subsahariana -donde la carga de malaria es mayor y más del 40% de la población vive en entornos urbanosha suscitado preocupación, porque se cree que ha impulsado el resurgimiento de la malaria en la ciudad de Yibuti y que es el origen de al menos un brote en Etiopía. lvii ■ En septiembre de 2022 se puso en marcha una nueva iniciativa destinada a detener la propagación de An. stephensi en el continente africano y a determinar si se puede eliminar de las zonas que ya han sido invadidas. ■ La iniciativa exhorta a que la respuesta nacional a An. stephensi forme parte de una respuesta integral a los vectores de la malaria, reforzando la colaboración, aumentando la vigilancia, mejorando el intercambio de información, elaborando orientaciones y priorizando la investigación. Marco global para la respuesta a la malaria en zonas urbanas ■ Para 2050, casi siete de cada diez personas en el mundo vivirán en ciudades y otros entornos urbanos. En los países donde la malaria es endémica, es probable que haya más personas en las zonas urbanas que en las rurales. ■ En los 10 países con mayor carga de la Región de África de la OMS, se considera que más del 40% de la población ya vive en zonas urbanas. ■ Las zonas urbanas y las rurales pueden diferir en la dinámica de la transmisión y la carga de la malaria y otras enfermedades transmitidas por vectores. ■ En general, la urbanización reducirá la transmisión de la malaria. Sin embargo, la urbanización no planificada llevará a una transmisión focal, lo que dará lugar a una carga de enfermedad desproporcionadamente alta entre los más pobres de las áreas urbanas. ■ La invasión de vectores cuya reproducción se ha adaptado a los entornos urbanos, como la reciente propagación de An. stephensi en la región africana de la OMS, puede poner en mayor riesgo a las poblaciones urbanas. ■ Las estrategias que funcionan para reducir la carga de la enfermedad en las zonas rurales pueden no funcionar en los entornos urbanos, o pueden tener que producirse a una escala más pequeña y específica. ■ La respuesta a la malaria en las zonas urbanas requiere de datos sobre los determinantes propios de los ecosistemas urbanos, y que ayuden a identificar si la transmisión de la malaria y la carga de la enfermedad son focales. ■ El liderazgo de los departamentos gubernamentales, la industria y el sector financiero, la investigación, la academia y otros sectores tiende a concentrarse en las zonas urbanas, lo que ofrece una mayor oportunidad para realizar políticas, estrategias y acciones integradas y multisectoriales. ■ A medida que la urbanización aumenta rápidamente, hay una mayor atención hacia ciudades más saludables, incluida la voluntad política mundial de hacer frente a las amenazas que plantea el cambio climático. ■ En octubre de 2022, la OMS y el Programa de las Naciones Unidas para los Asentamientos Humanos (ONU-Hábitat) lanzaron conjuntamente el Marco mundial para la respuesta a la malaria en zonas urbanas, que está dirigido a los formuladores de políticas y a las partes interesadas. ■ El marco es una respuesta a la malaria en pueblos y ciudades; destaca el papel central del liderazgo de las ciudades y la necesidad de garantizar que la respuesta a la malaria forme parte de una agenda más amplia de desarrollo urbano y salud. También hace hincapié en la necesidad de aplicar una respuesta específica basada en evidencia, para el diseño de las intervenciones contra la malaria. Si se aplica, se espera que el marco tenga muchos beneficios para mejorar la salud y el bienestar. Despliegue de la vacuna antimalárica RTS,S/AS01 en zonas de transmisión de malaria de moderada a alta ■ En octubre de 2021, la OMS recomendó la vacuna contra la malaria RTS,S/AS01 para la prevención de la malaria por P. falciparum en niños que viven en regiones con transmisión de moderada a alta, según la definición de la OMS. En julio de 2022, la OMS emitió la aprobación de precalificación para la vacuna RTS,S. ■ En diciembre de 2021, la junta de la Alianza para las Vacunas (Gavi) aprobó una inversión inicial de casi 160 millones de dólares (para 2022-2025) para apoyar el despliegue más amplio de la vacuna contra la malaria en los países elegibles para Gavi. ■ Gavi ha calculado que la demanda estable de la vacuna superará los 80-100 millones de dosis al año. Dada la previsión de un desfase entre la oferta y la demanda, la OMS coordinó el desarrollo de un marco para la asignación del suministro limitado de vacunas contra la malaria, con el fin de orientar dónde deben asignarse las dosis limitadas de forma justa y transparente, basándose en principios éticos y en los mejores datos disponibles. lviii W O RL D M AL AR IA R EP O RT 2 02 2 ■ En agosto de 2022, el Fondo de las Naciones Unidas para la Infancia (UNICEF) anunció que había asegurado el suministro de la vacuna producida por GSK, para un suministro disponible de vacunas contra la malaria de 18 millones de dosis para los próximos 3 años (2023-2025). ■ Desde junio de 2022, la OMS y Gavi han apoyado a más de 20 países a través de talleres multinacionales centrados en el desarrollo de solicitudes a Gavi. Una parte clave de esta iniciativa ha sido la adaptación subnacional del despliegue de la vacuna contra la malaria, apoyada por la OMS, basada en la orientación del marco para la asignación del suministro limitado de vacunas. Emergencias humanitarias y sanitarias ■ Varios países donde la malaria es endémica han estado lidiando con emergencias sanitarias y humanitarias no relacionadas con la pandemia de COVID-19. Se calcula que en 2021 hubo 268 millones de personas que se enfrentaron a emergencias humanitarias, frente a los 301 millones de 2020. ■ Los conflictos, la hambruna y las inundaciones fueron los principales causantes de estas emergencias humanitarias, que en ocasiones se vieron agravadas por brotes de enfermedades. ■ Los países con mayores necesidades en 2021 en términos de personas afectadas fueron Afganistán, Colombia, la República Democrática del Congo, Etiopía, Malí, Myanmar, Nigeria, Somalia, Sudán del Sur, Sudán, Uganda y Yemen. Interrupciones en los servicios de malaria durante la pandemia de COVID-19 ■ Cuarenta y seis países tenían previstas campañas de redes mosquiteras tratados con insecticida (MTI) en 2020, para distribuir unos 272 millones de redes. A finales de 2020, se había distribuido el 74% de todas las redes mosquiteras previstas para 2020. ■ En 2021, 43 países planearon campañas de distribución de MTI, de los cuales, 14, planificaron distribuir MTIs que sobraron del 2020. ■ Se planificó la distribución de un total de 171 millones de redes mosquiteras en 2021 y 70 millones de estas redes fueron los que quedaron de 2020. En total, se distribuyeron 128 millones (75%) de redes mosquiteras en 2021. ■ Ocho países – Benin, Eritrea, Indonesia, Nigeria, las Islas Salomón, Tailandia, Uganda, y Vanuatu distribuyeron menos del 60% de sus MTIs en 2021. Siete países – Botswana, la República Centroafricana, el Chad, Haití, India, Pakistán, y Sierra Leona no distribuyeron los MTIs que habían planificado distribuir. India, Nigeria y Uganda -países apoyados bajo el enfoque de alta carga y alto impacto (ACAI) distribuyeron el 0%, 53% y el 26%, respectivamente, de sus MTI previstas para ser distribuidas en 2021. ■ Los resultados de la tercera ronda de la encuesta sobre los servicios sanitarios esenciales (SSE), realizada entre noviembre y diciembre de 2021, sugieren que en la Región de África de la OMS las interrupciones en el diagnóstico y tratamiento han disminuido considerablemente en la última parte de 2021, con siete países que informaron de interrupciones, en comparación con 16 en 2020 y nueve en el primer trimestre de 2021. TENDENCIAS DE LA CARGA DE LA MALARIA Casos de malaria ■ A nivel mundial, se estima que en 2021 hubo 247 millones de casos de malaria en 84 países donde la enfermedad es endémica (incluido el territorio de la Guayana Francesa), lo que supone un aumento respecto a los 245 millones de 2020, siendo la mayor parte de este aumento procedente de los países de la Región de África de la OMS. En 2015, el año de referencia de la Estrategia Técnica Mundial contra la Malaria 2016-2030 (ETM), se estimó que había 230 millones de casos de malaria. ■ La incidencia de casos de malaria (es decir, casos por cada 1000 habitantes en riesgo) se redujo de 82 en 2000 a 57 en 2019, antes de aumentar a 59 en 2020. No ha habido cambios en la incidencia de casos entre 2020 y 2021. El aumento en 2020 se asoció a la interrupción de los servicios durante la pandemia de COVID-19. lix ■ Entre 2019 y 2021, se estima que 13,4 millones de casos adicionales se atribuyeron a las interrupciones durante la pandemia de COVID-19. ■ La proporción de casos debidos a P. vivax se redujo de aproximadamente el 8% (20,5 millones) en 2000 al 2% (4,9 millones) en 2021. ■ El 96% de los casos de malaria en el mundo se produjeron en 29 países, y cuatro países -Nigeria (27%), la República Democrática del Congo (12%), Uganda (5%) y Mozambique (4%)representaron casi la mitad de todos los casos en el mundo. ■ La Región de África de la OMS, con una estimación de 234 millones de casos en 2021, representó alrededor del 95% de los casos mundiales. ■ Entre 2000 y 2019, la incidencia de casos en la Región Africana de la OMS se redujo de 373 a 225 por cada 1000 habitantes en riesgo, pero aumentó a 234 en 2020, principalmente debido a las interrupciones de los servicios durante la pandemia de COVID-19. En 2021, la incidencia de casos se redujo a 229 por cada 1000 habitantes. ■ Cabo Verde notificó cero casos autóctonos durante 3 años consecutivos, poniendo fin a la epidemia de malaria. ■ La Región de Asia Sudoriental de la OMS representó alrededor del 2% de la carga de casos de malaria a nivel mundial. Los casos de malaria se redujeron en un 76%, pasando de 23 millones en 2000 a unos 5 millones en 2021. La incidencia de casos de malaria en esta región se redujo en un 82%, pasando de unos 18 casos por cada 1000 habitantes en riesgo en 2000 a unos tres casos por cada 1000 habitantes en riesgo en 2021. ■ El 79% de los casos de la región se produjeron en India. Sri Lanka fue certificada como libre de malaria en 2016 y sigue estando libre de malaria. ■ Entre 2020 y 2021, hubo un aumento de 400 000 casos en la región, con más de la mitad de estos casos reportados en Myanmar. ■ Los casos de malaria en la Región del Mediterráneo Oriental de la OMS se redujeron en un 38%, pasando de unos 7 millones de casos en 2000 a unos 4 millones en 2015. Entre 2016 y 2021, los casos aumentaron un 44%, hasta los 6,2 millones. ■ Durante el período 2000-2020, la incidencia de casos de malaria en la Región del Mediterráneo Oriental de la OMS se redujo de 20 a 12 casos por cada 1000 habitantes en riesgo. Sudán fue el principal contribuyente de casos de malaria en esta región, con cerca del 54% de los casos. En 2021, la República Islámica de Irán no tuvo ningún caso autóctono de malaria después de 4 años consecutivos y Arabia Saudita notificó cero casos autóctonos por primera vez. ■ La Región del Pacífico Occidental de la OMS tuvo un estimado de 1,4 millones de casos en 2021, lo que supone un descenso del 49% respecto a los 3 millones de casos del año 2000. En el mismo periodo, la incidencia de casos de malaria se redujo de cuatro a dos casos por cada 1000 habitantes en riesgo. Papúa Nueva Guinea representó casi el 87% de todos los casos de esta región en 2021. China fue certificada libre de malaria en 2021 y Malasia no tuvo casos de malaria no zoonótica durante 4 años consecutivos. ■ En la Región de las Américas de la OMS, los casos de malaria se redujeron en un 60% (de 1,5 millones a 0,60 millones) y la incidencia de casos en un 70% (de 14 a 4) entre 2000 y 2021. El progreso de la región en los últimos años se ha visto afectado por el gran aumento de la malaria en la República Bolivariana de Venezuela, que tenía unos 35 500 casos en 2000 y más de 482 000 en 2017. En 2020, los casos se redujeron a más de la mitad en comparación con 2019, con 223 000, y se redujeron aún más en 2021, con 205 000 casos. Este descenso se debió a las restricciones a la circulación durante la pandemia de la COVID-19 y a la escasez de combustible que afectó a la industria minera, que es el principal contribuyente al reciente aumento de la malaria en el país. Estas restricciones también pueden haber afectado al acceso a la atención, reduciendo los casos notificados desde los centros de salud. ■ Los casos estimados se duplicaron en Honduras y Panamá en 2021 en comparación con 2019. En el mismo periodo de tiempo se observaron reducciones sustanciales de casos en la República Bolivariana de Venezuela (-263 000), Brasil (-17 000), Colombia (-17 000) y Perú (-22 000). ■ La República Bolivariana de Venezuela, Brasil y Colombia representaron más del 79% de todos los casos en esta región. ■ Argentina, El Salvador y Paraguay fueron certificados como libres de malaria en 2019, 2021 y 2018, respectivamente. Belice informó de cero casos autóctonos de malaria por tercer año consecutivo, poniendo fin a la epidemia de malaria. ■ Desde 2015, la Región Europea de la OMS está libre de malaria. lx W O RL D M AL AR IA R EP O RT 2 02 2 Muertes por malaria ■ A nivel mundial, las muertes por malaria se redujeron constantemente durante el período 2000-2019, pasando de 897 000 en 2000 a 577 000 en 2015 y a 568 000 en 2019. En 2020, las muertes por malaria aumentaron un 10% en comparación con 2019, hasta una estimación de 625 000. Las muertes estimadas disminuyeron ligeramente en 2021, hasta 619 000. Entre 2019 y 2021, hubo 63 000 muertes que se debieron a las interrupciones de los servicios esenciales de malaria durante la pandemia de COVID-19. ■ El porcentaje de muertes totales por malaria en niños menores de 5 años se redujo del 87% en 2000 al 76% en 2015. Desde entonces no ha habido ningún cambio. ■ A nivel mundial, la tasa de mortalidad por malaria (es decir, las muertes por cada 100 000 habitantes en riesgo) se redujo a la mitad, pasando de unos 30 en 2000 a 15 en 2015; luego siguió disminuyendo, pero a un ritmo más lento, hasta llegar a 14 en 2019. En 2020, la tasa de mortalidad volvió a aumentar, a 15,1, antes de disminuir ligeramente a 14,8 en 2021. ■ Alrededor del 96% de las muertes por malaria a nivel mundial se produjeron en 29 países. Cuatro países -Nigeria (31%), la República Democrática del Congo (13%), Níger (4%) y la República Unida de Tanzania (4%)representaron algo más de la mitad de todas las muertes por malaria a nivel mundial en 2021. ■ Las muertes por malaria en la Región de África de la OMS disminuyeron de 841 000 en 2000 a 541 000 en 2018, antes de aumentar a 599 000 en 2020. Las muertes estimadas volvieron a disminuir a 593 000 en 2021. La tasa de mortalidad por malaria se redujo en un 62% entre 2000 y 2019, de 148 a 56 por cada 100 000 habitantes en riesgo, antes de aumentar a 60 en 2020 y volver a disminuir a 58 en 2021. ■ Cabo Verde ha notificado cero muertes por malaria desde 2018. ■ En la Región de Asia Sudoriental de la OMS, las muertes por malaria se redujeron en un 74%, pasando de unas 35 000 en 2000 a 9000 en 2019. El número de muertes se ha mantenido igual en los últimos 3 años. ■ En India se reportaron alrededor del 83% de todas las muertes por malaria de la Región de Asia Sudoriental de la OMS. ■ En la Región del Mediterráneo Oriental de la OMS, las muertes por malaria se redujeron en un 45%, de alrededor de 13 600 en 2000 a 7500 en 2014, y luego aumentaron en un 79% entre 2014 y 2021, hasta 13 400 muertes. La mayor parte del aumento se observó en Sudán, donde más del 90% de los casos fueron por P. falciparum, que se asocia a una mayor tasa de letalidad que los casos de P. vivax. ■ En la Región del Mediterráneo Oriental de la OMS, la tasa de mortalidad por malaria se redujo en un 60% entre 2000 y 2009; sin embargo, desde 2016, las tasas de mortalidad se han mantenido prácticamente sin cambios, con un ligero aumento del 28%, de 2 a 2,5 muertes por cada 100 000 habitantes en riesgo. ■ En la Región del Pacífico Occidental de la OMS, las muertes por malaria se redujeron en un 58%, pasando de unos 6200 casos en 2000 a 2600 en 2021. La tasa de mortalidad se redujo en un 67% durante el mismo período, pasando de 0,9 a 0,3 muertes por malaria por cada 100 000 habitantes en riesgo. En Papúa Nueva Guinea sucedieron más del 94% de las muertes por malaria en 2021. ■ En la Región de las Américas de la OMS, las muertes por malaria se redujeron en un 64% (de 919 a 334) y la tasa de mortalidad en un 73% (de 0,8 a 0,2). La mayoría de las muertes en esta región sucedieron en adultos (78%). Casos de malaria y muertes evitadas ■ A nivel mundial, se estima que se evitaron 2.000 millones de casos de malaria y 11,7 millones de muertes por malaria en el periodo 2000-2021. ■ La mayoría de los casos (82%) y muertes (95%) evitados se produjeron en la Región de África de la OMS, seguida por la Región de Asia Sudoriental de la OMS (casos 10% y muertes 3%). Carga de la malaria en el embarazo ■ En 2021, en 38 países de transmisión moderada y alta de la Región de África de la OMS, se estimó que hubo 40 millones de embarazos, de los cuales 13,3 millones (32%) estuvieron expuestos a la infección por malaria durante el embarazo. lxi ■ Por subregiones de la OMS, África occidental tuvo la mayor prevalencia de exposición a la malaria durante el embarazo (40,7%), seguida de cerca por África central (39,8%), mientras que la prevalencia fue del 20% en África oriental y meridional. ■ Se calcula que, sin una intervención específica en el embarazo, la infección por malaria durante el mismo en estos 38 países habría provocado 961 000 niños con bajo peso al nacer. En los 33 países en los que se aplicó el tratamiento preventivo intermitente (TPI), se estima que se evitó que 457 000 niños tuvieran bajo peso al nacer. ■ Si todas las mujeres embarazadas que acuden a las clínicas de atención prenatal (CAP) al menos una vez reciben una sola dosis de TPI -suponiendo que todas fueran elegibles y que la segunda y tercera dosis de TPI (TPI2 y TPI3) se mantuvieran en los niveles actualesse habría evitado adicionalmente que unos 55 000 niños tuvieran bajo peso al nacer en 33 países para los cuales existe información sobre TPI. ■ Si la cobertura de la TPI3 se elevara a los mismos niveles que la cobertura de la primera visita al centro de atención prenatal, y si las visitas posteriores al centro de atención prenatal fueran igual de elevadas, se evitarían adicionalmente 162 000 niños con bajo peso al nacer. ■ Si la cobertura de TPI3 se optimizara hasta el 90% de todas las mujeres embarazadas, se evitaría que otros 265 000 niños tuvieran bajo peso al nacer. ■ Dado que el bajo peso al nacer es un importante factor de riesgo para la mortalidad neonatal e infantil, evitar un número considerable de niños con bajo peso al nacer salvará muchas vidas. ELIMINACIÓN DEL MALARIA Y PREVENCIÓN DE SU RESTABLECIMIENTO ■ El progreso hacia la eliminación de la malaria está aumentando; en 2021, hubo 84 países con malaria endémica, frente a 108 en 2000. ■ El número de países en los que la malaria era endémica en 2000 y que notificaron menos de 100 casos de malaria aumentó de 6 en 2000 a 27 en 2021, permaneciendo sin cambios en 2021 en comparación con 2020. ■ Entre 2000 y 2021, el número de países con menos de 10 casos autóctonos aumentó de 4 a 25. ■ En el periodo 2010-2021, el total de casos de malaria en los países E-2025 (países que eliminarán la malaria en 2025) se redujo en un 82,8%; sin embargo, en comparación con 2020, estos países y territorios experimentaron un aumento del 30,4% en 2021. ■ En la Región de Asia Sudoriental de la OMS, se ha producido un notable aumento del número de personas infectadas por P. knowlesi en algunos países, especialmente en Malasia. En los últimos 4 años no se han notificado casos autóctonos ni muertes por malaria humana en Malasia. Sin embargo, desde 2017, se han notificado un total de 17 125 casos de P. knowlesi y 48 muertes en este país. ■ Solo en 2021, se notificaron 3575 casos de P. knowlesi que provocaron 13 muertes. Durante el mismo periodo, se notificaron otros 435 casos de P. knowlesi en la Región de Asia Sudoriental de la OMS, en Indonesia, Filipinas y Tailandia. ■ Belice y Cabo Verde notificaron cero casos de malaria por tercer año consecutivo, e Irán (República Islámica de) y Malasia notificaron cero casos autóctonos por cuarto año consecutivo. En 2021, Timor Oriental, tras un brote en el año anterior, notificó cero casos autóctonos de malaria, y Arabia Saudita notificó cero casos autóctonos por primera vez. ■ Cinco países -Azerbaiyán, Belice, Cabo Verde, Irán (República Islámica de) y Tayikistánhan presentado una solicitud oficial de certificación de estar libres de malaria. ■ A pesar de las interrupciones que se produjeron durante la pandemia de COVID-19, el 61,5% de los países del E-2025 que notificaron casos siguieron avanzando hacia la eliminación y la reducción de la carga de malaria. Los países que siguieron mostrando una reducción de casos en 2021 en comparación con 2020 fueron Bután (59,1%), Botsuana (20,5%), la República Dominicana (65,6%), México (32%), Nepal (56,2%), la República de Corea (23%), Arabia Saudita (100%), Sudáfrica (33,7%), Surinam (85,9%), Tailandia (22,3%), Timor Oriental (100%) y Vanuatu (36,7%). ■ En comparación con 2020, los siguientes países (y un territorio) experimentaron un aumento de casos en 2021: Comoras (56,9%), Costa Rica (52,4%), República Popular Democrática de Corea (22,8%), Ecuador (11,1%), Eswatini (53,9%), Guayana Francesa (2,1%), Guatemala (16,9%), Honduras (47,4%), lxii W O RL D M AL AR IA R EP O RT 2 02 2 Panamá (55,3%) y Santo Tomé y Príncipe (28,9%). Ecuador, por segundo año consecutivo, informó del aumento del número de casos autóctonos. ■ A pesar de los contratiempos, muchos países siguen haciendo inmensos progresos: entre 2011 y 2021, las tasas de clasificación dentro de los países del E-2025 aumentaron del 7,6% al 90,8%, lo que demuestra los continuos esfuerzos de los países en pro de sus objetivos de eliminación. ■ Entre 2000 y 2021, los seis países de la subregión del Gran Mekong Camboya, China (provincia de Yunnan), la República Democrática Popular Lao, Myanmar, Tailandia y Vietnamnotificaron en conjunto una disminución del 76,5% de los casos de malaria autóctona y del 94,1% de los casos de malaria autóctona por P. falciparum. La reducción ha sido la más drástica desde el 2012, cuando se puso en marcha el programa de eliminación de la malaria del Mekong. Entre 2012 y 2021, la región experimentó un asombroso descenso de los casos de malaria autóctonos (86,4%) y de los casos de malaria autóctonos por P. falciparum (95,7%). En 2021, se notificaron un total de 90 082 casos de malaria autóctonos y 16 484 casos autóctonos de P. falciparum, lo que supone un aumento del 17,3% en los casos autóctonos de malaria y un descenso del 12,2% en los casos autóctonos de P. falciparum en comparación con 2020. ■ En 2021, Myanmar experimentó un aumento en el número de casos tanto de P. falciparum como de P. vivax debido a la continua inestabilidad política en la zona. En 2021, Myanmar siguió representando la mayor parte de los casos de malaria autóctona (87,7%) y de malaria autóctona por P. falciparum (80,9%). ■ Mientras los casos de P. falciparum siguen disminuyendo, P. vivax se ha convertido en la especie dominante en la subregión. En 2021, Camboya notificó recaídas por primera vez; notificando un total de 1978, lo que representa el 48% del total de casos autóctonos del país. ■ Los países de la SGM están aprovechando el ejercicio de verificación subnacional para reforzar sus programas de prevención de recidivas y prepararse para la certificación de su país por la OMS. ■ Entre 2000 y 2021, no se ha detectado el restablecimiento de la transmisión de la malaria en ningún país que haya sido certificado como libre de malaria. ENFOQUE DE ALTA CARGA A ALTO IMPACTO (ACAI) ■ Desde noviembre de 2018, los 11 países del enfoque ACAI han implementado actividades relacionadas con el enfoque en los cuatro elementos de respuesta. ■ En 2020 y 2021, la OMS y la Asociación Hacer Retroceder la Malaria para poner fin a la malaria apoyaron a los países para implementar autoevaluaciones rápidas sobre el progreso de los objetivos de ACAI en los cuatro elementos de respuesta. ■ En 2021, con el aumento continuo de los casos de malaria, los países del enfoque ACAI representaron el 68% de todos los casos y el 70% de las muertes a nivel mundial. ■ Entre 2020 y 2021, los casos de malaria en los países de ACAI aumentaron de 163 millones a 168 millones, mientras que hubo una reducción de las muertes de 444 600 a 427 854. ■ Con los aumentos en curso en 2021, los siguientes países representaron la mayoría de los casos de malaria: Nigeria (39%), la República Democrática del Congo (18,2%), Uganda (7,8%) y Mozambique (6,1%). ■ Cinco de los 11 países ACAI -la República Democrática del Congo, Ghana, India, Níger y la República Unida de Tanzaniamostraron un descenso en el número de muertes, aunque su contribución a la carga de malaria dentro de los países ACAI sigue siendo considerable. VIGILANCIA ■ Más de 40 países utilizan paquetes de vigilancia de la malaria diseñados para el Sistema de Información Sanitaria de los Distritos 2 (DHIS2), y 34 países han adoptado el módulo de malaria agregada. ■ También se han desarrollado módulos estándar específicos para la entomología y el control de vectores con el fin de ayudar a los países a mejorar la recopilación y el uso de los datos de las intervenciones entomológicas y de control de vectores. lxiii ■ Se ha desarrollado o mejorado un conjunto de herramientas para apoyar la vigilancia de la eliminación de la malaria utilizando la plataforma web DHIS2 y la aplicación de captura DHIS2 (app) para Android. Estas herramientas pueden utilizarse para la notificación, investigación y respuesta a los casos, así como para la investigación y respuesta focalizadas. ■ Un total de 12 países han comenzado a establecer repositorios de malaria, que son almacenes de datos para todos los datos relacionados con la malaria. ■ La OMS ha lanzado el kit de herramientas de evaluación de la vigilancia de la malaria, que proporciona una forma sistemática y estandarizada de evaluar el rendimiento de los sistemas de vigilancia. ■ Cuatro países -Burkina Faso (2020), Camerún (2021), la República Democrática del Congo (2021) y Ghana (2021)han llevado a cabo una evaluación del sistema de vigilancia utilizando el conjunto de herramientas. Las evaluaciones de tres de estos países que están disponibles para su publicación (excluyendo a Camerún) mostraron que la exhaustividad y la puntualidad de los informes eran buenas, pero la concordancia de las variables básicas entre los registros y los informes agregados era pobre. INVERSIONES EN LOS PROGRAMAS E INVESTIGACIÓN SOBRE LA MALARIA ■ La Estrategia Técnica Mundial (ETM) establece estimaciones de la financiación necesaria para alcanzar los hitos para 2025 y 2030. El total de recursos anuales necesarios se estimó en 6.800 millones de dólares para el 2020, aumentando a 9.300 millones en 2025 y a 10.300 millones en 2030. Se calcula que se necesitarán otros 850 millones de dólares anuales para investigación y desarrollo (I+D) de la malaria a nivel mundial durante el periodo 2021-2030. ■ La financiación total para el control y la eliminación de la malaria en 2021 se estimó en 3.500 millones de dólares, frente a 3.300 millones en 2020 y 3.000 millones en 2019. La cantidad invertida en 2021 fue inferior a los 7.300 millones de dólares que se calcula que se necesitan en todo el mundo para mantener el rumbo hacia los hitos de la ETM de lucha contra la Malaria. ■ La brecha de financiación entre la cantidad invertida y los recursos necesarios ha seguido aumentando drásticamente en los últimos años, pasando de 2.600 millones de dólares en 2019 a 3.500 millones en 2020 y 3.800 millones en 2021. ■ Durante el periodo 2010-2021, el 67% de la financiación total para el control y la eliminación de la malaria, casi 2.400 millones de dólares, provienen de fuentes internacionales. Los Estados Unidos de América (EE.UU.) contribuyeron con más de 1.300 millones de dólares a través de la financiación bilateral prevista y por parte de las agencias de contribuciones multilaterales. Le siguieron los desembolsos bilaterales y multilaterales del Reino Unido de Gran Bretaña e Irlanda del Norte (Reino Unido) y de Alemania, que ascendieron a unos 200 millones de dólares; las contribuciones de Canadá, Francia y Japón, que ascendieron a unos 100 millones de dólares; y un total de 400 millones de dólares de otros países miembros del Comité de Ayuda al Desarrollo y de contribuyentes del sector privado. ■ Los gobiernos de los países donde la malaria es endémica aportaron más de un tercio de la financiación total en 2021, con inversiones de más de 1.100 millones de dólares, de los cuales más de 300 millones se destinaron al manejo de casos de malaria en el sector público y 800 millones a otras actividades de control de la malaria, lo que supone un aumento de 100 millones de dólares respecto a 2020. ■ De los 3.500 millones de dólares invertidos en 2020, más de 1.500 millones (44%) se canalizaron a través del Fondo Mundial. En comparación con años anteriores, los desembolsos del Fondo Mundial a los países donde la malaria es endémica aumentaron en unos 100 millones de dólares desde 2020 y en 300 millones desde 2019. ■ Las clasificaciones del Banco Mundial por grupos de ingresos varían de un año a otro. En 2021, los 27 países del grupo de ingresos bajos representaron el 47% de la financiación total, experimentando un aumento de la financiación de más del 50% desde 2010 y representando más del 90% de los casos y muertes por malaria a nivel mundial. Los 39 países de ingresos bajos y medios representaron el 41% de la financiación total en 2021, mientras que el resto de países y las regiones no especificadas en las que no se disponía de información geográfica sobre los receptores representaron el 12% de la financiación para la malaria. lxiv W O RL D M AL AR IA R EP O RT 2 02 2 ■ La evaluación de la financiación de la malaria por persona en riesgo pone de manifiesto la variación de la financiación nacional e internacional en las regiones de la OMS, y ha mostrado cambios considerables en la última década. En la mayoría de las regiones de la OMS la financiación por persona en riesgo ha descendido a niveles inferiores a los de 2010, salvo en la Región de África de la OMS, donde la financiación se duplicó en 2021 en comparación con 2010. ■ De los 3.500 millones de dólares invertidos en 2021, más de tres cuartas partes (78%) se destinaron a la Región de África de la OMS, un 5% a la Región de Asia Sudoriental y a la Región del Mediterráneo Oriental, un 4% a la Región de las Américas y un 3% a la Región del Pacífico Occidental. El 5% restante de la financiación total en 2021 se asignó a regiones no especificadas. ■ Muchos países han experimentado cambios en su producto interno bruto (PIB) real debido a la pandemia de COVID-19 y a otras crisis; a su vez, esto ha afectado a la economía mundial, que se expandió un 5,5% en 2021 tras una contracción del 3,4% en 2020. En 2021, se produjo un crecimiento significativo entre los países de ingreso bajo y los países de ingreso mediano bajo, ya que solo el 11% de estos países experimentaron un impacto negativo en el crecimiento de su PIB real, en comparación con el 70% en 2020. A pesar del continuo aumento de la financiación para la malaria, el impacto real de la pandemia de COVID-19, la crisis económica asociada, otros factores globales y el aumento de la población en riesgo seguirán desarrollándose en los próximos años. ■ La financiación total para investigación y desarrollo (I+D) en malaria fue de 626 millones de dólares en 2021. ■ Este es el tercer año consecutivo de disminución de la financiación desde su pico de 2018, con la financiación de la I+D en malaria disminuyendo en casi todas las categorías de productos. En particular, la Fundación Bill y Melinda Gates redujo su inversión; sin embargo, siguió siendo el tercer mayor financiador general de I+D de vacunas contra la malaria, solo por detrás de los Institutos Nacionales de Salud (NIH) de los Estados Unidos y la industria. ■ La investigación básica pareció experimentar el mayor descenso absoluto de la financiación en 2021 (-12%) y la producción de control de vectores experimentó un descenso de algo menos del 20% desde 2020. La financiación del diagnóstico recibió el 2,5% del total de la financiación de la malaria en 2021, su porcentaje más bajo desde 2013. Solo la I+D para productos terapéuticos se libró de la tendencia general a la baja, ya que la I+D de medicamentos aumentó un 2,3% y la de productos biológicos un 7,0%. ■ Los Institutos Nacionales de Salud (NIH) de EE.UU. siguieron siendo el principal financiador de la I+D en malaria en 2021, con una financiación estable de 189 millones de dólares, seguidos por la industria y la Fundación Bill y Melinda Gates. DISTRIBUCIÓN Y COBERTURA DE LA PREVENCIÓN DE LA MALARIA ■ Los datos de entrega de los fabricantes para el periodo 2004-2021 muestran que se suministraron casi 2.500 millones de mosquiteros tratados con insecticidas (MTI) en todo el mundo en ese periodo, de los cuales 2.200 millones (el 87%) se suministraron al África subsahariana. ■ Los fabricantes entregaron unos 220 millones de MTI a los países donde la malaria es endémica en 2021. De ellos, el 46% fueron mosquiteros con piretroides y butóxido de piperonilo (PBO) y el 9% fueron mosquiteros con doble ingrediente activo. ■ En 2021, el 68% de los hogares del África subsahariana tenía al menos un MTI, lo que supone un aumento con respecto al 5% del año 2000. El porcentaje de hogares que poseen al menos un MTI por cada dos personas aumentó del 1% en 2000 al 38% en 2021. En el mismo periodo, el porcentaje de la población con acceso a un MTI en su hogar aumentó del 3% al 54%. ■ El porcentaje de la población que duerme bajo un mosquitero también aumentó considerablemente entre 2000 y 2021, para toda la población (del 2% al 47%), para los niños menores de 5 años (del 3% al 53%) y para las mujeres embarazadas (del 3% al 53%). ■ En general, el acceso y el uso de los mosquiteros sigue siendo inferior a los niveles observados en 2017. ■ A nivel mundial, el porcentaje de la población en riesgo protegida por el rociado residual intradomiciliar (RRI) en los países donde la malaria es endémica disminuyó del 5,5% en 2010 al 2,4% en 2021. El porcentaje de población protegida por el RRI se ha mantenido estable desde 2016, con menos del 6% de la población protegida en cada región de la OMS. lxv ■ El número de personas protegidas por el RRI a nivel mundial se redujo de 153 millones en 2010 a 112 millones en 2015, y siguió disminuyendo hasta 80 millones en 2021. ■ El promedio de niños tratados por ciclo de quimio prevención de la malaria estacional (QME) aumentó de unos 0,2 millones en 2012 a casi 45 millones en 2021. ■ El número total de dosis de tratamiento administradas en los 15 países que aplican la QME en 2021 fue de unos 180 millones. ■ Utilizando los datos de 33 países de la Región de África de la OMS, se calculó el porcentaje de uso del tratamiento preventivo intermitente (TPI) por dosis. En 2021, el 72% de las mujeres embarazadas utilizaron los servicios de control prenatal al menos una vez durante su embarazo. Alrededor del 55% de las mujeres embarazadas recibió una dosis de TPI, el 45% recibió dos dosis y el 35% recibió tres dosis. DISTRIBUCIÓN Y COBERTURA DEL DIAGNÓSTICO Y TRATAMIENTO DE LA MALARIA ■ A nivel mundial, los fabricantes vendieron 3.500 millones de pruebas de diagnóstico rápido (PDR) para la malaria en 2010-2021, y casi el 82% de estas ventas se realizaron en países del África subsahariana. En el mismo periodo, los programas nacionales de malaria (PNM) distribuyeron 2.400 millones de PDR, el 88% en el África subsahariana. ■ En 2021, los fabricantes vendieron 413 millones de PDR y los PNM distribuyeron 262 millones. ■ Los fabricantes suministraron casi 3.800 millones de tratamientos combinados con artemisinina (TCA) en todo el mundo entre 2010 y 2021. Alrededor de 2.600 millones de estas entregas fueron para el sector público en los países donde la malaria es endémica; el resto fueron de mecanismos de copago del Fondo Mundial o del Affordable Medicines Facility-malaria (AMFm) del sector público o privado, o entregas al sector privado fuera del mecanismo de copago del Fondo Mundial para esos años. ■ Los datos nacionales notificados por los PNM en 2010-2021 muestran que se entregaron 2.400 millones de TCA a los proveedores de servicios de salud para tratar a los pacientes de malaria en el sector público. ■ En 2021, los fabricantes entregaron unos 225 millones de TCA al sector público; en ese mismo año, los PNM distribuyeron 242 millones de TCA a este sector, de los cuales el 97% fueron en el África subsahariana. ■ Para analizar la cobertura de la búsqueda de tratamiento, diagnóstico y el uso de TCA en niños menores de 5 años, se utilizaron datos agregados de las encuestas de hogares realizadas en el África subsahariana entre 2005 y 2021 en 20 países con al menos dos encuestas (la de referencia 2005-2011 y la más reciente 2015-2021). ■ Comparando las encuestas de referencia y las más recientes, hubo pocos cambios en la prevalencia de fiebre en las dos semanas anteriores a las encuestas (mediana del 25% frente al 20%) o en la búsqueda de tratamiento para la fiebre (mediana del 65% frente al 67%). ■ Las comparaciones de la fuente de tratamiento entre las encuestas de referencia y las más recientes muestran que la proporción que recibió atención en centros de salud públicos aumentó de una mediana del 58% al 69%, la proporción que recibió atención del sector privado disminuyó de una mediana del 40% al 28%, y el uso de trabajadores sanitarios de la comunidad siguió siendo bajo, con medianas del 2% y el 1%, respectivamente. ■ La tasa de diagnóstico entre los niños menores de 5 años con fiebre y para los que se buscó atención aumentó considerablemente, de una mediana del 30% en la línea de base al 57% en las últimas encuestas de hogares. ■ El uso de TCA entre los que buscaron atención también aumentó, del 14% al inicio al 24% en las últimas encuestas. ■ Entre las personas que solicitaron atención y que recibieron un pinchazo en el dedo o en el talón, el uso de TCA fue del 29% en la encuesta más reciente, en comparación con el 21% al inicio. lxvi W O RL D M AL AR IA R EP O RT 2 02 2 PROGRESO HACIA LOS HITOS DE LA ETM DE 2020 ■ El objetivo de la Estrategia Técnica Mundial (ETM) es reducir la incidencia y la tasa de mortalidad de la malaria en al menos un 75% para 2025 y en un 90% para 2030 con respecto a la base de referencia de 2015. Para 2020, el objetivo de la ETM era una reducción del 40%. ■ El número de países que alcanzaron los objetivos de la ETM para 2021 se derivó de las estimaciones oficiales de la carga, en lugar de las proyecciones (como se hizo en el Informe Mundial sobre la Malaria 2020). ■ En 2020 y 2021, las estimaciones incluyeron el efecto de las interrupciones de los servicios de malaria durante la pandemia y se basaron en un nuevo método para cuantificar la fracción de causa de muerte de la malaria. ■ A pesar de los considerables progresos realizados desde el año 2000, los hitos de la ETM 2020 en materia de morbilidad y mortalidad no se han alcanzado a nivel mundial. Si se mantienen las tendencias actuales, los objetivos de la ETM 2025 tampoco se alcanzarán a nivel mundial. ■ La incidencia de casos de malaria de 59 casos por cada 1.000 habitantes en riesgo en 2021, en lugar de los 31 casos por 1.000 esperados si el mundo estuviera en camino de alcanzar el hito de morbilidad de la ETM 2021, significa que, a nivel mundial, estamos desviados del objetivo en un 48%. ■ Aunque el progreso relativo en la tasa de mortalidad es mayor que el de la incidencia de casos, el objetivo de la ETM de 7,8 muertes por malaria por cada 100.000 habitantes en riesgo en 2021 era un 48% inferior a la tasa de mortalidad de 14,8 observada en el mismo año. ■ De los 93 países que eran endémicos de malaria (incluido el territorio de la Guayana Francesa) en todo el mundo en 2015, 39 (42%) cumplieron el hito de morbilidad de la ETM para 2021, al haber logrado una reducción del 40% o más en la incidencia de casos o haber notificado cero casos de malaria. ■ Diecinueve países (20%) también redujeron la incidencia de casos de malaria, pero por debajo del objetivo previsto. ■ Veintisiete países (29%) aumentaron la incidencia de casos y 14 países (15%) observaron un aumento del 40% o más en la incidencia de casos de malaria en 2021 en comparación con 2015. ■ En ocho países (8,6%), la incidencia de casos de malaria en 2021 fue similar a la de 2015. ■ Cuarenta y tres países (46%) que eran endémicos de malaria en 2015 alcanzaron el hito de mortalidad de la ETM para 2021, y 28 de ellos informaron de cero muertes por malaria. ■ Veintidós países (24%) lograron reducir las tasas de mortalidad por malaria, pero por debajo del objetivo del 40%. ■ Las tasas de mortalidad por malaria se mantuvieron al mismo nivel en 2021 que en 2015 en nueve países (9,7%), mientras que las tasas de mortalidad aumentaron en 19 países (20%), 11 de los cuales tuvieron aumentos del 40% o más. ■ La Región de Asia Sudoriental de la OMS cumplió los hitos de la ETM 2020 tanto para la mortalidad como para la morbilidad. Todos los países de la región, excepto Indonesia, redujeron la incidencia de casos y la mortalidad en un 40% o más. AMENAZAS BIOLÓGICAS Y DE OTRO TIPO PARA LAS HERRAMIENTAS DE INTERVENCIÓN CONTRA LA MALARIA Deleción de los genes pfhrp2/3 del parásito ■ Los parásitos que no pueden expresar la proteína rica en histidina 2 (HRP2) pueden no ser detectables por las pruebas de diagnóstico rápido (PDR) basadas en la HRP2, y los que ya no expresan tanto la HRP2 como la proteína rica en histidina 3 (HRP3) son completamente indetectables por dichas PDR. ■ La OMS ha recomendado que los países con informes de deleciones de pfhrp2/3, y sus países vecinos, lleven a cabo estudios de línea de base representativos entre los casos sospechosos de malaria, para determinar si la prevalencia de deleciones de pfhrp2/3 que causan resultados de falsos negativos en las PDR ha alcanzado un umbral que requiere un cambio en la PDR (>5% de deleciones de pfhrp2 causan falsos negativos en las PDR). lxvii ■ Las opciones de PDR alternativas (por ejemplo, basadas en la detección de la deshidrogenasa láctica del parásito) son limitadas; en particular, actualmente no hay pruebas precalificadas por la OMS que no estén basadas en la detección combinada de HRP2, que puedan detectar y distinguir entre P. falciparum y P. vivax. ■ La OMS está haciendo un seguimiento de los informes publicados sobre deleciones de pfhrp2/3 utilizando la aplicación Mapa de los desafíos de la Malaria (Malaria Threats Map) y está fomentando un enfoque armonizado para el mapeo y la notificación de las deleciones de pfhrp2/3 a través de protocolos de encuesta disponibles públicamente. ■ En 2022, varios países de la Región de África de la OMS comenzaron a planificar o implementar encuestas representativas para las deleciones de pfhrp2/3, esperándose resultados en 2022 y 2023. ■ La OMS ha puesto en marcha un tablero de control para el seguimiento de las actividades de vigilancia a nivel mundial con el fin de informar sobre las prioridades y la asignación de recursos y la previsión de PDR, y para evitar la duplicación de esfuerzos. El tablero presenta las características clave de las actividades de vigilancia, así como los plazos. ■ Entre septiembre de 2021 y septiembre de 2022, se informó de investigaciones de deleciones de pfhrp2/3 en 17 publicaciones de 17 países: Benín, Brasil, Camerún, República Democrática del Congo, Yibuti, Ecuador, Guinea Ecuatorial, Eritrea, Etiopía, Gabón, Ghana, India, Kenia, Madagascar, Ruanda, Sierra Leona y República Unida de Tanzania. De ellos, sólo en Guinea Ecuatorial, Kenia y Ruanda no se identificó ninguna deleción de pfhrp2, aunque en estos tres países se han notificado deleciones en publicaciones anteriores. ■ Según los datos de las publicaciones incluidas en el Mapa de los desafíos de la Malaria, se ha realizado algún tipo de investigación en 47 países, confirmándose la presencia de deleciones en 40 de ellos. ■ El Plan de Respuesta Global de la OMS para las deleciones de pfhrp2/3 describe varias áreas de acción más allá de la ampliación de la vigilancia. Estas otras áreas de acción incluyen la identificación de nuevos biomarcadores, la mejora del rendimiento de las pruebas de diagnóstico rápido que no son de tipo HRP2, la realización de previsiones de mercado y el fortalecimiento de las redes de laboratorios para apoyar la demanda de uso de la caracterización molecular para determinar la presencia o ausencia de estas deleciones genéticas. Resistencia de los parásitos a los medicamentos antimaláricos ■ La eficacia de los medicamentos antimaláricos se vigila mediante estudios de eficacia terapéutica (EET), que hacen un seguimiento de los resultados clínicos y parasitológicos de los pacientes que reciben tratamiento antimalárico. Los EET se consideran la norma para que los países puedan determinar mejor sus políticas nacionales de tratamiento. ■ La resistencia a los medicamentos antimaláricos puede evaluarse mediante varias herramientas. Para algunos medicamentos, se han identificado cambios genéticos asociados a una menor sensibilidad a los mismos. La resistencia parcial a la artemisinina se vigila mediante una lista establecida de marcadores validados y candidatos de PfKelch13 asociados a un retraso en la eliminación parasitaria tras un tratamiento con artemisinina. ■ La OMS recopila los resultados de los estudios sobre la eficacia y la resistencia de los medicamentos antimaláricos y los pone a disposición en el Mapa de los desafíos de la Malaria. ■ Región de África de la OMS: En la Región Africana de la OMS se realizaron un total de 266 EET de P. falciparum de acuerdo con el protocolo estándar de la OMS entre 2015 y 2021, con la participación de al menos 20 pacientes. Entre estos EET, seis estudios han demostrado una tasa de falla superior al 10%: cuatro estudios con arteméter-lumefantrina (AL) en Burkina Faso y en Uganda, y dos estudios con dihidroartemisinina-piperaquina (DHA-PPQ) en Burkina Faso. Otros cinco estudios con AL y dos estudios con DHA-PPQ que utilizaron una metodología para distinguir entre reinfección y recrudescencia, que difiere de la metodología recomendada por la OMS, informaron de tasas de fallas terapéuticas superiores al 10%; estos incluyen estudios en Angola, la República Democrática del Congo y Uganda. Las mutaciones de PfKelch13 han aparecido en Eritrea, Ruanda y Uganda. Las tasas de falla terapéutica se mantienen por debajo del 10%, porque el medicamento asociado sigue siendo eficaz. Se necesitan más estudios para determinar el alcance de la propagación de las mutaciones de PfKelch13 y para investigar cualquier cambio en el tiempo de eliminación del parásito y la resistencia in vitro. ■ Región de las Américas de la OMS: Los datos de los EET de la Región de las Américas de la OMS son limitados. Los EET de AL realizadas entre 2015 y 2021 en Brasil y Colombia demostraron una alta eficacia. En Guyana, la mutación PfKelch13 C580Y asociada con la resistencia parcial a la artemisinina se observó esporádicamente entre 2010 y 2017, pero no se ha encontrado en ninguna de las muestras más recientes, lo que indica que la mutación probablemente ha desaparecido. lxviii W O RL D M AL AR IA R EP O RT 2 02 2 ■ Región del Mediterráneo Oriental de la OMS: De los 36 EET de P. falciparum realizados en la Región del Mediterráneo Oriental de la OMS en los que participaron al menos 20 pacientes, dos estudios con artesunato más SP (AS+SP) en Somalia y Sudán demostraron una tasa de falla superior al 10%. En estos países, el tratamiento de primera línea se cambió posteriormente a AL. ■ Región de Asia Sudoriental de la OMS: De los 67 EET de P. falciparum realizados en la región de Asia Sudoriental de la OMS que incluyeron al menos a 20 pacientes, ningún EET informó de una tasa de falla del tratamiento superior al 10%. Sin embargo, la presencia de mutaciones asociadas a la resistencia a la SP en la India central podría ser una señal de alerta temprana antes del fracaso de la AS+SP. En Tailandia, donde la eficacia de los fármacos se evalúa con la vigilancia integrada de la eficacia de los medicamentos, se descubrió que las tasas de fallas del tratamiento con DHA-PPQ más primaquina eran elevadas en la provincia de Sisaket. Esto llevó a la provincia a cambiar su tratamiento de primera línea por el de artesunato-pironaridina (AS-PY) en 2020. En la subregión del Gran Mekong (SGM), las mutaciones de PfKelch13 asociadas a la resistencia parcial a la artemisinina tienen una alta prevalencia en Myanmar y Tailandia. ■ Región del Pacífico Occidental de la OMS: De los 63 EET de P. falciparum realizados en la Región del Pacífico Occidental de la OMS que incluyeron al menos 20 pacientes, 14 estudios demostraron una tasa de falla superior al 10%. Un estudio con AL en la República Democrática Popular Lao en 2017 encontró altas tasas de falla (17,2%), pero este estudio solo tenía 29 pacientes y los estudios posteriores encontraron una alta eficacia de AL. Dos estudios en Camboya encontraron altas tasas de falla con artesunato-amodiaquina (AS-AQ), lo que indica la presencia de resistencia a AQ en Camboya. Se detectaron altas tasas de falla del tratamiento con DHA-PPQ en Camboya, la República Democrática Popular Lao y Vietnam, lo que provocó el cambio del uso de este fármaco como tratamiento de primera línea. En la SGM, las mutaciones de PfKelch13 asociadas a la resistencia parcial a la artemisinina tienen una alta prevalencia en Camboya, la República Democrática Popular Lao y Vietnam. Además, en Papúa Nueva Guinea ha aparecido la mutación PfKelch13 C580Y y parece estar extendiéndose. Resistencia de los vectores a los insecticidas ■ Entre 2010 y 2020, 88 países enviaron datos a la OMS sobre la vigilancia de la resistencia a los insecticidas, incluidos 38 con datos sobre la intensidad de la resistencia a los piretroides, y 32 sobre la capacidad del PBO para restaurar la susceptibilidad a los piretroides. ■ En 2020, se dispuso de nuevas concentraciones discriminantes y procedimientos para la vigilancia de la resistencia en los vectores de la malaria a clorfenapir, clotianidina, transflutrina, flupiradifurona y piriproxifeno, y se revisaron las concentraciones discriminantes para pirimifos-metilo y alfa- cipermetrina. Los países deben ajustar la vigilancia de la resistencia a los insecticidas en los vectores de malaria a estos nuevos procedimientos. La OMS no ha recibido datos de vigilancia de la resistencia de los vectores a transflutrina, flupiradifurona y piriproxifeno. Aunque la OMS ha recibido algunos datos de vigilancia de la resistencia a clorfenapir y clotianidina, estos datos son insuficientes para evaluar la posible presencia de resistencia a cualquiera de estos dos insecticidas. ■ De los 88 países endémicos de malaria y que proporcionaron datos para 2010-2020, 78 han detectado resistencia a al menos una clase de insecticida en al menos un vector de la malaria y en un sitio de recolección; 29 ya han detectado resistencia a los piretroides, organoclorados, carbamatos y organofosforados en diferentes sitios; y 19 han confirmado la resistencia a todas estas cuatro clases en al menos un sitio y al menos un vector local. ■ A nivel mundial, la resistencia a los piretroides -la principal clase de insecticida utilizada actualmente en los mosquiteros tratados con insecticidasestá muy extendida, habiéndose detectado en al menos un vector de la malaria en el 68% de los lugares de los que se dispone de datos. La resistencia a los organoclorados se registró en el 64% de los lugares. La resistencia a los carbamatos y a los organofosforados es menos frecuente, ya que se detectó en el 34% y el 28% de los lugares que comunicaron datos de vigilancia, respectivamente. ■ De los 38 países que notificaron datos sobre la intensidad de la resistencia a los piretroides, se detectó una resistencia de alta intensidad en 27 países y 293 lugares. ■ Desde 2010, se ha observado que el PBO restablece totalmente la susceptibilidad en 283 lugares de 29 países. ■ Para orientar el manejo de la resistencia, los países deben elaborar e implementar planes nacionales de vigilancia y manejo de la resistencia a los insecticidas, basándose en el Marco de la OMS para un plan nacional de vigilancia y manejo de la resistencia a los insecticidas en los vectores de malaria. El número de países que informaron que tenían un plan de este tipo aumentó de 53 en 2019 a 67 en 2020. lxix ■ Se requiere de asistencia técnica y financiera para apoyar a los países en la vigilancia y manejo de la resistencia a los insecticidas. ■ Los datos de resistencia a los insecticidas notificados a la OMS se incluyen en la base de datos mundial de la OMS sobre la resistencia a los insecticidas en los vectores de malaria y pueden explorarse a través del Mapa de Amenazas de la Malaria. Efectividad de los MTI ■ Se considera que los mosquiteros tratados con insecticida (MTI) fueron los principales impulsores de la disminución de la transmisión y la carga de malaria en el periodo 2005-2015, especialmente en entornos con una transmisión moderada a alta. Los mosquiteros tratados con insecticidas de larga duración siguen siendo eficaces y la OMS recomienda su uso continuo para prevenir la malaria. ■ Dado que los mosquiteros tratados con insecticidas de larga duración son la principal herramienta de control de vectores, los factores que disminuyen su eficacia en la prevención de la malaria son relevantes en el avance de la lucha contra esta enfermedad. Estos factores incluyen la durabilidad física del mosquitero (es decir, la integridad del tejido) y su durabilidad química (es decir, la bioeficacia del insecticida, que es la disponibilidad del ingrediente activo en la superficie del mosquitero a lo largo del tiempo), las limitaciones operativas y del comportamiento humano (es decir, la entrega, el acceso, la cobertura y la aceptabilidad, el uso, el mantenimiento y la retención), y la dinámica de los vectores (comportamientos de picadura y reposo de las especies vectoras). ■ La aparición y la amplia expansión geográfica de la resistencia a los piretroides es la amenaza más reconocida para la eficacia de los mosquiteros tratados con piretroides de larga duración. ■ Un estudio multinacional comisionado por la OMS y publicado en 2018 mostró que los MTI seguían siendo altamente protectores contra la malaria, incluso en presencia de una alta resistencia de los vectores a los piretroides. ■ Por el contrario, datos de varios estudios en casas experimentales sugieren que, a medida que la susceptibilidad de los vectores a los piretroides disminuye, los efectos repelente y de mortalidad de los insecticidas sobre los mosquitos se reducen en gran medida. ■ Los análisis con modelos sugieren además que el impacto epidemiológico de los mosquiteros tratados con insecticidas se reduce en zonas con vectores con niveles elevados de resistencia a los piretroides. ■ La mayor eficacia observada en ensayos controlados aleatorizados de algunos MTI de la nueva generación (en comparación con los MTI que tienen sólo piretroides) sugiere que la resistencia a los insecticidas está teniendo un efecto en los resultados epidemiológicos. ■ En el África subsahariana, donde se distribuyen la mayoría de los MTI, se entregaron unos 590 millones de MTI a las comunidades en el período 2019-2021. Sin embargo, en 2021, el porcentaje estimado de la población con acceso a un MTI dentro de su hogar y el porcentaje de la población que duerme bajo un MTI fue del 54% y el 47%, respectivamente, debido a varios factores. ■ Los factores que afectan la tenencia y el uso universal de los MTI son la eficacia en la asignación, la retención y el uso. Una asignación equitativa de los MTI se debe apoyar en la identificación de las brechas de cobertura a nivel local y en la ampliación de la distribución a estas áreas. La retención de los mosquiteros está determinada por las actitudes de las personas hacia sus mosquiteros, los comportamientos de manipulación de estos, y otros riesgos, la durabilidad del tejido y la calidad en su elaboración. La vida media de los mosquiteros actuales, aunque varía según el entorno, es de aproximadamente 1,9 años. Por último, incluso cuando los mosquiteros están a disposición de los miembros del hogar, no se utilizan el 100% de las veces; la variación en el uso se ve afectada por la edad, la estación, el género y el riesgo de malaria. ■ Se llevó a cabo un modelo de análisis ilustrativo. El análisis partió de un MTI "ideal" (es decir, uno que se asigna perfectamente, que nunca se desecha, que siempre se utiliza, que es altamente insecticida y altamente duradero), y se exploraron escenarios en los que se reintrodujo secuencialmente cada una de las limitaciones enumeradas anteriormente. ■ El análisis mostró que los principales factores que reducen la eficacia de los mosquiteros son la disminución de la eficacia insecticida y la durabilidad física. Cuando se combinan con las barreras al acceso, la tasa de uso y la retención de los mosquiteros, el impacto de una campaña de mosquiteros disminuye drásticamente al final del tercer año. ■ Se necesitan soluciones para mejorar la durabilidad física y química de los MTI, eficacia en la asignación y en el mantenimiento y uso por parte de los miembros de la familia. Además, los mosquiteros de doble ingrediente activo o los mosquiteros tratados con no-piretroides son esenciales para mitigar el impacto de la resistencia generalizada a los insecticidas. lxx W O RL D M AL AR IA R EP O RT 2 02 2 Efectividad del RRI ■ El rociado residual intradomiciliar (RRI) es la segunda intervención de control de vectores más implementada por los PNM. Sin embargo, las pruebas de la eficacia de RRI son limitadas. ■ A partir de los datos observados, cuando el RRI se lleva a cabo correctamente es una intervención poderosa para reducir la densidad y la longevidad de los mosquitos adultos, y por tanto para reducir la transmisión de la malaria. ■ Algunos de los factores que influyen en la eficacia del RRI son similares a los que afectan a los MTI. Por ejemplo, la mayor parte de la población de vectores se alimenta y reposa en el interior de las viviendas, la gente duerme principalmente en el interior por la noche y hay altos niveles de aceptación en la comunidad. Los factores específicos del RRI incluyen el momento de la fumigación y el nivel de formación de los operarios fumigadores (que afecta la calidad de la fumigación). ■ La aplicación del RRI con la calidad requerida es logísticamente más difícil y considerablemente más costosa que la distribución de mosquiteros tratados con insecticida. ■ Un estudio reciente demostró que, en comparación con los mosquiteros tratados con insecticida, el RRI era unas cinco veces más caro por persona protegida al año, lo que significa que el RRI es considerablemente menos rentable. ■ Un estudio realizado en Etiopía sugiere que realizar el RRI con participación comunitaria (en la que los operarios fumigadores entrenados y los materiales están más cerca de los hogares y se cuenta con la participación de los promotores de salud locales) puede reducir los costos, mejorar el rendimiento y aumentar la cobertura sin reducir la calidad. ■ Incluso cuando se recurre a la participación comunitaria, los costes totales se mantienen, y cuando se añaden los costes de mantenimiento de los equipos y la supervisión regular de los fumigadores, está claro que la implementación a gran escala del RRI requiere de un gran presupuesto. ■ Es probable que los costes aumenten aún más a medida que surja la resistencia a los insecticidas utilizados en el RRI y se necesiten productos químicos más caros. Más allá del alcance de los MTI y del RRI: abordaje de la transmisión residual ■ Más allá de la adaptación genética de los mosquitos a los insecticidas (es decir, la resistencia a los insecticidas), es igualmente importante comprender la adaptación del comportamiento del mosquito, en términos de cambios en el comportamiento de picadura, alimentación y reposo. ■ Cada vez hay más pruebas de que los vectores intentan reducir el contacto con los insecticidas (es decir, pican antes de que las personas se acuesten, pasan más tiempo en reposo fuera de las viviendas o se alimentan del ganado en lugar de los humanos). ■ Los vectores con esta plasticidad de comportamiento se vuelven más dominantes que los que tienen cambios de comportamiento limitados. La adaptación del comportamiento por parte del vector requerirá inversiones en I+D de intervenciones dirigidas a la transmisión fuera de las viviendas. INVESTIGACIÓN Y DESARROLLO PARA LA MALARIA Y PRODUCTOS EN PREPARACIÓN Características preferidas de los productos y perfiles de productos objetivo ■ El Programa Mundial contra la Malaria de la OMS (WHO/GMP) ha participado en el desarrollo de las características preferidas de los productos (CPP) y los perfiles de los productos objetivo (PPO), como herramientas clave para incentivar y guiar el desarrollo de productos con un alto impacto en la salud pública y adecuados para su uso en los países de bajo y mediano ingreso. ■ La OMS está coordinando el desarrollo de PPO para las pruebas de glucosa-6-fosfato deshidrogenasa (G6PD) en los puntos de atención para satisfacer las necesidades de control de P. vivax. ■ Basándose en la experiencia de la vacuna contra la malaria RTS,S, la OMS publicó en 2022 unas CPPs actualizadas para vacunas contra la malaria. Los CPP incluyen un conjunto ampliado de objetivos estratégicos para la I+D de vacunas contra la malaria para la prevención de la infección en la fase lxxi sanguínea, la reducción de la morbilidad y la mortalidad, y la reducción de la transmisión a nivel comunitario. ■ La OMS también ha desarrollado varios CPPs para el control de vectores, incluidos los relativos a los mosquiteros tratados con insecticida para el control de la transmisión de la malaria en poblaciones de mosquitos resistentes a los insecticidas (publicados en 2021), para las intervenciones de control de vectores para controlar la malaria en emergencias humanitarias complejas y en respuesta a desastres naturales (publicados en 2021), para los productos endectocidas y ectocidas para el control de la transmisión de la malaria (publicados en 2022) y para el tratamiento residual de superficies en interiores para el control de la transmisión de la malaria en zonas con mosquitos resistentes a los insecticidas (publicados en 2022). ■ En cuanto a la quimio prevención, la OMS ha desarrollado PPCs para los medicamentos de quimio prevención de la malaria destinados a abordar varios tipos de uso: quimio prevención en niños (quimio prevención de la malaria estacional, quimio prevención de la malaria perenne en lactantes (anteriormente conocido como tratamiento preventivo intermitente en infantes, o TPIi), tratamiento preventivo intermitente en escolares y quimio prevención de la malaria posterior al alta), durante el embarazo (TPIe) y en viajeros no inmunes. ■ En noviembre de 2021, el Programa Mundial de Malaria de la OMS y el Departamento de Inmunización, Vacunas y Productos Biológicos (IVB) de la OMS convocaron un grupo de desarrollo científico para elaborar CPPs y abordar las consideraciones de desarrollo clínico de los anticuerpos monoclonales para la prevención de la malaria. La prioridad más inmediata en materia de salud pública es la reducción de la morbilidad y la mortalidad de los lactantes y los niños a causa del P. falciparum. ■ La OMS también ha trabajado para el desarrollo de productos en estrecha coordinación con socios, como la Foundation for Innovative New Diagnostics (FIND), la Medicines for Malaria Venture (MMV) y el Innovative Vector Control Consortium (IVCC), con el fin de crear una base de datos de productos antimaláricos en fase de I+D. Pruebas de diagnóstico y medicamentos ■ En lo que respecta a las pruebas de diagnóstico de la malaria, la propagación de parásitos de P. falciparum con deleciones del gen pfhrp2/3 representa una importante amenaza para un diagnóstico fiable; se necesita un panorama con diversas posibilidades de diagnóstico para hacer frente a este desafío. Los principales retos en materia de I+D son la sensibilidad inadecuada de las pruebas para las especies que no son P. falciparum y una gama más amplia de posibilidades para el diagnóstico (por ejemplo, una detección no invasiva y de alta sensibilidad para la prevención de la reintroducción). ■ El proyecto actual de I+D incluye productos para abordar estos vacíos mediante esfuerzos para mejorar las plataformas existentes en los puntos de atención (concretamente, la microscopía y las pruebas de detección de antígenos por inmunocromatografía de flujo lateral) y desarrollar alternativas que utilicen una serie de tipos de muestras y tecnologías. ■ La I+D de medicamentos contra la malaria se centra actualmente en el desarrollo de la nueva generación de medicamentos que salvan vidas para adultos, especialmente mujeres embarazadas, y niños que corren un riesgo elevado de sufrir las consecuencias de la infección por malaria. Otra gran prioridad es ofrecer una opción de tratamiento sin derivados de la artemisinina, para simplificar la terapia o como contingencia contra la intensificación de la resistencia a la artemisinina o al fármaco asociado. ■ Bajo la coordinación de MMV (Medicines for Malaria Venture), la I+D en medicamentos contra la malaria se ha centrado en dos áreas principales: las innovaciones con los tratamientos antimaláricos existentes y los tratamientos antimaláricos de nueva generación. En relación con los tratamientos existentes, se está trabajando en la triple TCA, la TCA (tratamiento combinado con artemisinina) más una dosis única de primaquina para el bloqueo de la transmisión y el arteméter-lumefantrina para los neonatos. ■ Hay varios medicamentos de nueva generación en diferentes fases de ensayo: ganaplacida- lumefantrina, M5717-pironaridina, ZY19489-ferroquina y cipargamina. Además, hay siete moléculas de nueva generación en fase de investigación. lxxii W O RL D M AL AR IA R EP O RT 2 02 2 Control de vectores ■ El Observatorio Mundial de Investigación y Desarrollo Sanitarios (GOHRD) de la OMS tiene actualmente una lista de 28 productos de control de vectores en fase de investigación y desarrollo. Once (39%) de estos productos son mosquiteros tratados con insecticidas (MTI) y siete (25%) son tratamientos para el rociado residual intradomiciliar (RRI). Trece productos (46%) están en la fase de generación de datos sobre la evaluación de la seguridad, la calidad y la eficacia entomológica. Siete productos (25%) están siendo sometidos a ensayos epidemiológicos, seis (21%) están siendo evaluados por la OMS para ser incluidos en el listado de precalificación o para ser recomendados por la OMS y dos (7%) están en la fase de desarrollo de prototipos. ■ El IVCC proporcionó una visión general de los productos de intervención contra la malaria en desarrollo, que incluye varios insecticidas nuevos y reutilizados con diferentes modos de acción, para su uso en los mosquiteros tratados con insecticida (MTI) y para el rociado residual intradomiciliar (RRI), como apoyo a las mejores prácticas en el manejo de la resistencia a los insecticidas. ■ Entre los productos que el IVCC ha ayudado a introducir en el mercado para el RRI se encuentran el pirimifos-metilo (incluido en la lista del Plan de Evaluación de Plaguicidas de la OMS [WHOPES] en enero de 2013), la clotianidina (precalificada por la OMS en 2017) y la clotianidina más deltametrina (precalificada por la OMS en 2018). ■ Además, el clorfenapir y la broflanilida se están revisando y se espera que se incluyan en la lista de precalificación en un futuro próximo. ■ Interceptor® G2, un nuevo MTI, combina un piretroide con un insecticida reutilizado, el clorfenapir, que tiene un modo de acción diferente. La mezcla de estos dos ingredientes activos es un paso adelante para el manejo de la resistencia a los insecticidas. Otros tres ingredientes activos se encuentran en la fase final de investigación o han avanzado hasta su pleno desarrollo y se espera que obtengan el ingreso a la lista de precalificación entre 2026 y 2030. ■ Se está desarrollando un conjunto ampliado de herramientas para el control de vectores que incluye larvicidas, trampas letales para viviendas (tubos de alero) y cebos tóxicos azucarados. Estas herramientas son fundamentales para el manejo de la resistencia a los insecticidas y reducir el contacto humano-vector fuera de las viviendas. Vacunas ■ Las vacunas contra la malaria actualmente en desarrollo clínico se dirigen a la fase preeritrocítica del parásito; es decir, se dirigen al esporozoito o fase hepática del parásito, a la fase sanguínea del parásito, a los gametocitos o al desarrollo del parásito en el mosquito. ■ Se puede encontrar información detallada en el tablero de vacunas del GOHRD. En resumen, de las vacunas candidatas en desarrollo, seis se dirigen al parásito en la fase preeritrocítica (P. falciparum), tres se dirigen a la fase sanguínea (P. falciparum) y seis se dirigen a la fase sexual o bloqueo de la transmisión (de ellas, cuatro se dirigen a P. vivax y dos a la malaria por P. falciparum en el embarazo). lxxiii 1 W O RL D M AL AR IA R EP O RT 2 02 2 lxxiv The World malaria report 2022 presents progress in several important health and development goals in the global efforts to reduce the burden of malaria overall and eliminate the disease where possible. These goals are outlined in the Sustainable Development Goals (SDGs) framework (1), the World Health Organization (WHO) Global technical strategy for malaria 2016–2030 (GTS) (2) and the RBM Partnership to End Malaria Action and investment to defeat malaria 2016–2030 (AIM) (3). For most indicators, the report covers the period 2000–2021. Specific analysis is implemented to track disruptions to essential malaria services since the start of the coronavirus (COVID-19) pandemic in 2020, and the impact of these disruptions on malaria cases and deaths. There are several changes in this year’s report. There are three new sections: one on new global and regional initiatives launched in 2021 and 2022, one on surveillance with case study systems assessments, and one on research and development. Also, the section on the threats faced by malaria tools has been expanded, with a focus on the effectiveness of insecticide- treated mosquito nets (ITNs). Section 2 tracks some of the key events that are relevant to the global state of malaria. Section 3 presents the global trends in malaria morbidity and mortality and estimates of the burden of malaria during pregnancy. Section 4 shows the progress towards elimination and the trends in the 11 highest burden countries. Section 5 provides updates on global malaria surveillance and country case studies on surveillance system assessments. Section 6 focuses on the total funding for malaria control and elimination, and for malaria research and development (R&D). The supply of key commodities to endemic countries and population-level coverage achieved through these investments are presented in Section 7. Section 8 summarizes global progress, by region and country, towards the GTS milestones for 2020 and the trajectory towards 2025 and 2030. Section 9 describes the threats posed by Plasmodium falciparum parasites that no longer express histidine-rich protein 2 (HRP2), which is detected by the most widely used malaria rapid diagnostic test (RDT); the threats posed by drug and insecticide resistance; and the spread of the invasive species, Anopheles stephensi. This section also presents additional analysis on ITN durability and insecticide resistance, and their collective impact on the effectiveness of ITNs. Section 10 presents an overview of malaria R&D, with a focus on preferred product profiles and products in the development pipeline. Section 11 summarizes the findings of the report and presents concluding remarks. The main text is followed by annexes that contain data sources and methods, regional profiles and data tables. Country profiles can be found online (4). 1 INTRODUCTION 1 W O RL D M AL AR IA R EP O RT 2 02 2 2 This section presents the key events relevant to the global malaria response that occurred in 2021–2022. 2.1 GLOBAL FUND SEVENTH REPLENISHMENT The Global Fund to Fight AIDS, Tuberculosis and Malaria (Global Fund) invests US$ 4 billion a year in HIV, tuberculosis (TB) and malaria programmes (5). In 2021, more than 40% of the investment in malaria globally was channelled through the Global Fund (5). In September 2022, the Global Fund’s Seventh Replenishment was hosted by the government of the United States of America (USA) during the United Nations (UN) General Assembly meeting (6) (Section 6). Although falling short of the original target of US$ 18 billion, countries and partners managed to raise a record US$ 15.7 billion (7). With the cost of most health commodities rising during the COVID-19 pandemic, the current replenishment will be under considerable strain to maintain and expand levels of malaria intervention coverage. To optimize the impact of the limited resources, there will be an even greater need to maximize the efficient, effective and equitable use of malaria and other health system resources. Countries need to be able to identify the optimal mix of interventions suited to their local context and the best means of delivering them to all people in need. 2.2 NEW WHO RECOMMENDATIONS Clear, evidence-informed WHO recommendations guide managers of national malaria programmes (NMPs) as they develop policies and strategic plans to combat the disease; such recommendations support decisions around “what to do”. WHO also develops implementation guidance, such as operational and field manuals, to advise countries on “how to” deliver the recommended tools and strategies. In June 2022, WHO published consolidated guidelines for malaria that contained a package of new and updated recommendations across a number of technical areas – from case management, vector control, vaccines, malaria chemoprevention and mass drug administration (MDA) to elimination (8). The guidelines encourage countries to tailor the recommendations to local disease settings for maximum impact. The updated guidelines contain recommendations on intermittent preventive treatment of malaria in pregnancy (IPTp), perennial malaria chemoprevention (PMC) and seasonal malaria chemoprevention (SMC), intermittent preventive treatment of malaria in school-aged children (IPTsc), post-discharge malaria chemoprevention (PDMC), MDA and elimination recommendations, as summarized below. 2.2.1 Case management In November 2022, WHO released the following treatment recommendations for case management (8): artesunate-pyronaridine is recommended as an artemisinin-based combination therapy (ACT) option for the treatment of uncomplicated P. falciparum malaria “(strong recommendation, low certainty of evidence)”; pregnant women with uncomplicated P. falciparum malaria should be treated during the first trimester with artemether-lumefantrine “(strong recommendation for, low-certainty evidence)”; with regards to primaquine treatment to prevent relapse, 0.5 mg/kg per day for 7 days is recommended to treat P. vivax or P. ovale malaria in children and adults 2OVERVIEW OF KEY EVENTS IN 2021–2022 3(except pregnant women, infants aged <6 months, women breastfeeding infants aged <6 months, women breastfeeding older infants unless they are known not to be deficient in glucose-6-phosphate dehydrogenase [G6PD], and people with G6PD deficiency; “(strong recommendation, very low-certainty evidence)”; however, WHO recommends against using primaquine 1.0 mg/kg per day for 7 days to treat P. vivax or P. ovale. 2.2.2 Vector control In March 2022, WHO released an update to the vector control guidelines (8). The conditional recommen- dation for the deployment of pyrethroid-PBO nets was updated based on a recently completed revision of an earlier systematic review. A recommendation for the deployment of pyrethroid-only long-lasting insecticidal nets (LLINs) or pyrethroid-piperonyl butoxide (PBO) nets and a separate conditional recommendation for the deployment of indoor residual spraying (IRS) in areas affected by humanitarian emergencies were formulated based on evidence on vector control interventions from a recent systematic review. The sections on insecticide selection for IRS were updated to provide further detail about the risks of using dichloro-diphenyl-trichloroethane (DDT) and the importance of considering alternative insecticides. Further information on resource considerations, cost and cost–effectiveness for WHO-recommended interventions was added to inform local costing studies and guide the selection of intervention packages. Areas where evidence gaps remain and research is needed to inform further revisions of the guidance for malaria vector control were updated. 2.2.3 Vaccines WHO guidance is available to countries as they consider how to adopt the vaccine as an additional tool to reduce child illness and deaths from malaria. In March 2022, WHO’s recommendation for the vaccine was added to the WHO guidelines for malaria (8). 2.2.4 Intermittent preventive treatment of malaria in pregnancy Malaria infection during pregnancy poses substantial risks not only to the mother, but also to her fetus and the newborn. Evidence continues to show that IPTp with sulfadoxine-pyrimethamine (SP) is a safe and highly cost-effective strategy for reducing the disease burden in pregnancy and for reducing adverse pregnancy and birth outcomes (8). In the updated guidance, WHO has reaffirmed its strong recommendation for the use of IPTp-SP in areas of moderate to high P. falciparum malaria transmission. The recommendation does not limit the delivery of IPT-SP to antenatal care (ANC) settings; where inequities in access to ANC services exist, other delivery methods, such as the use of community health workers, may be explored. IPT-SP is now recommended for all pregnant women, regardless of the number of pregnancies; previously, it was recommended only during a woman’s first and second pregnancies. 2.2.5 Seasonal malaria chemoprevention and perennial malaria chemoprevention WHO has also updated its recommendations for two key malaria chemoprevention strategies: SMC and PMC (previously known as intermittent preventive treatment of malaria in infants, or IPTi). When given to young children, malaria chemoprevention has been shown to be a safe, effective and cost-effective strategy for reducing the disease burden and saving lives (8). The updated WHO recommendations on SMC and PMC are less restrictive than the original recommendations; they do not specify strict age groups, transmission intensity thresholds, numbers of doses or cycles, or specific drugs. The new recommendations will support the broader use of chemoprevention among young children at high risk of severe malaria in areas with both seasonal and year- round transmission. 2.2.6 Intermittent preventive treatment of malaria in school-aged children WHO is also issuing a new recommendation for the use of IPTsc in settings with moderate to high perennial or seasonal malaria transmission. The strategy and dosing schedule for IPTsc should cover children aged 5–15 years, and its introduction should not compromise chemoprevention interventions for children aged under 5 years, who are at highest risk of severe malaria. 2.2.7 Post-discharge malaria chemoprevention WHO is now recommending PDMC – a strategy aimed at preventing malaria among children with severe anaemia living in areas of moderate to high transmission after they are discharged from a hospital, when they are at high risk of re-admission or death. Through PDMC, children are given a full antimalarial treatment course at regular intervals. 2.2.8 Mass drug administration MDA is another chemoprevention strategy. Through MDA, all individuals in a target population are given a treatment course of antimalarial drugs, regardless of whether they are infected with malaria. The medication treats any existing malaria infections as well as new infections for a specific period. The new recommendations on malaria MDA provide guidance on rapidly reducing the malaria disease burden in emergency settings and in areas of moderate to high transmission. They also provide guidance on the use of MDA to reduce P. falciparum malaria in settings with very low to low transmission, and to reduce P. vivax 4W O RL D M AL AR IA R EP O RT 2 02 2 transmission. The full set of MDA recommendations and supporting evidence can be found in the consolidated guidelines (9). 2.2.9 Elimination recommendations In settings approaching elimination, interventions will be most effective at reducing transmission if they are tailored to detect and treat the residual foci of malaria transmission. WHO has issued a new set of recommendations for the final phase of malaria elimination. Some of these recommendations are also relevant to areas that have achieved elimination and are working to prevent re-establishment of transmission. Some of these evidence-based recommendations are positive (i.e. they favour specific interventions) and others are negative (i.e. they are against specific interventions). The recommendations are divided into three categories: ■ “mass” strategies applied to the entire population of a delimited geographical area, whether it be a village, township or district; these strategies include MDA (described above), mass testing and treatment (MTaT) and mass relapse prevention (MRP); ■ “targeted” strategies applied to people at greater risk of infection than the general population; these strategies include targeted drug administration (TDA); targeted testing and treatment (TTaT); routine testing and treatment at points of entry (border screening); and malaria testing of organized or identifiable groups arriving or returning from malaria endemic areas; and ■ “reactive” strategies triggered in response to individual cases; these strategies include reactive drug administration (RDA), reactive case detection and treatment to reduce transmission of malaria (RACDT) and reactive IRS. 2.3 THE STRATEGY TO RESPOND TO ANTIMALARIAL DRUG RESISTANCE IN AFRICA Recent studies have confirmed the emergence of artemisinin partial resistance in several areas of Africa, notably in Eritrea, Rwanda and Uganda (Section 9.2). Although resistance to the ACT partner drugs currently in use has not been confirmed, there are some worrying signals. Data are lacking from several countries and areas, meaning that resistance may be present in other areas. Artemisinin partial resistance can be defined as delayed parasite clearance after treatment with a drug containing an artemisinin derivative. Significant reduction of treatment efficacy has not been observed in association with delayed parasite clearance after treatment with a drug containing an artemisinin derivative. However, increases in the proportion of parasites carrying P. falciparum Kelch13 (PfKelch13) mutations indicate that parasites with this mutation have an advantage under current treatment strategies and transmission dynamics. Given the heavy reliance on ACTs in Africa, the threat of artemisinin partial resistance and partner drug resistance must be monitored and addressed urgently. The apparent rapid spread of some mutations associated with artemisinin partial resistance means that vigorous measures must be taken before ACTs start to fail in Africa. With no alternative drugs likely to become available soon, it is essential to preserve the therapeutic lifespan of ACTs. In November 2022, WHO launched the Strategy to respond to antimalarial drug resistance in Africa (10). This technical and advocacy document was developed to provide guidance to key stakeholders in the malaria community. The strategy builds on lessons learned from past global plans and complements existing strategies, including broader efforts to respond to antimicrobial resistance. The goal of the strategy is to minimize the threat and impact of antimalarial drug resistance of P. falciparum in Africa. The strategy has three objectives that are instrumental to achieving the goal: ■ improve the detection of resistance to ensure a timely response; ■ delay the emergence of resistance to artemisinin and ACT partner drugs; and ■ limit the selection and spread of drug-resistant parasites where resistance has been confirmed. The strategy addresses the threat of antimalarial drug resistance in Africa through four pillars: ■ Pillar I – Strengthen surveillance of antimalarial drug efficacy and resistance. ■ Pillar II – Optimize and better regulate the use of diagnostics and therapeutics to limit drug pressure through pre-emptive measures. ■ Pillar III – React to resistance by limiting the spread of antimalarial drug-resistant parasites. ■ Pillar IV – Stimulate research and innovation to better leverage existing tools and develop new tools against resistance. Each pillar consists of a set of interventions (Fig. 2.1) that can be implemented at the local, regional and global levels. Implementing countries need to tailor the interventions in the strategy to their local context. 52 | Overview of key events in 2021–2022 Strengthen surveillance of antimalarial drug efficacy and resistance Optimize and better regulate the use of diagnostics and therapeutics to limit drug pressure through pre-emptive measures React to resistance by limiting the spread of antimalarial drug-resistant parasites Stimulate research and innovation to better leverage existing tools and to develop new tools against resistance Enhance capacity and capabilities to generate better quality and standardized data on antimalarial drug efficacy and on parasite resistance Develop national treatment policies that promote deliberate use of existing treatments to prevent and react to the emergence and spread of resistance Ensure optimal malaria vector control intervention coverage in priority areas Identify innovative approaches using currently available drugs to delay the development and spread of resistance I II III IV 1 1 1 1 Increase coverage of surveillance systems on efficacy and resistance Promote the availability of a diversified drug portfolio in countries Leverage preventive measures to reduce transmission of antimalarial drug-resistant parasites Identify areas and populations where drug resistance is deemed more likely to develop and spread 2 2 2 2 Increase collection of additional, more detailed data at select sites Prevent exposure to subtherapeutic drug levels driven by substandard and falsified ACTs by promoting drug quality Limit the risk of increased transmission of drug-resistant parasites Develop new treatments and diagnostics with the objective of delaying the emergence and the spread of resistance 3 3 3 3 Improve data dissemination systems to facilitate reactive and coordinated response to resistance data Remove nonrecommended monotherapies and ensure that other monotherapies are used in accordance with guidelines Strengthen cross-border collaboration on malaria activities to ensure coordinated resistance management Identify and develop innovative tools to limit malaria infection and transmission 4 4 4 4 Promote equitable access to quality drugs Conduct research and modelling to better understand and track resistance 5 5 Promote equitable distribution of and access to high-quality diagnostics to reduce drug pressure 6 Empower patients, health care workers and other stakeholders to make informed decisions and provide appropriate treatment 7 FIG. 2.1. Response interventions clustered into the strategy’s four pillars to address antimalarial drug resistance in Africa Source: WHO (2022) (10). ACT: artemisinin-based combination therapy; WHO: World Health Organization. 6W O RL D M AL AR IA R EP O RT 2 02 2 2.4 INITIATIVE TO STOP THE SPREAD OF AN. STEPHENSI An. stephensi is a mosquito species that can transmit both P. falciparum and P. vivax malaria parasites. Originally, An. stephensi was native to south Asia and parts of the Arabian Peninsula but it has been expanding its range over the past decade, with detections reported in Djibouti (2012), Ethiopia and the Sudan (2016), Somalia (2019) and Nigeria (2020) (11-14). It is probable that An. stephensi has spread to other African countries but has not yet been detected because systematic, large-scale surveillance of the vector is still in its infancy. An. stephensi can thrive in urban environments, setting it apart from the other main mosquito vectors of malaria that primarily breed in rural areas. Where An. stephensi has been reported in the WHO African Region, it has been found to be resistant to many of the insecticides used in public health, posing an added challenge to its control. The invasion of An. stephensi in sub-Saharan Africa – where the burden of malaria is highest and over 40% of the population lives in urban environments – is particularly worrying (15). Since 2012, An. stephensi is thought to have contributed to a resurgence of malaria in Djibouti City and at least one outbreak of the disease in Ethiopia (11, 16). Although the overall contribution of An. stephensi to malaria transmission in the region is unclear, the rapid growth of many African cities, coupled with the invasion and spread of this highly efficient and adaptable malaria vector, could undermine the gains made in reducing the burden of the disease. In a 2019 vector alert (17), WHO identified the spread of An. stephensi as a significant threat to malaria control and elimination – particularly in the WHO African Region. A new WHO initiative, launched in September 2022, aims to stop the further spread of An. stephensi on the African continent and to determine whether it can be eliminated from areas that have already been invaded. The initiative has five key aims, outlined in Fig. 2.2 (18). The initiative calls for the national response to An. stephensi to be part of a comprehensive response to malaria vectors, guided by the WHO GTS (2). Where feasible, integration with efforts to control other vector- borne diseases should be explored; for example, in the area of breeding-site surveillance in urban and periurban areas. The WHO Global vector control response 2017–2030 (19) and the Global framework for the response to malaria in urban areas (20) provide additional guidance. 2.5 THE GLOBAL FRAMEWORK FOR THE RESPONSE TO MALARIA IN URBAN AREAS By 2050, almost seven in 10 people globally will live in cities and other urban settings (21). In the 10 highest burden countries in the WHO African Region, over 40% of the population are already considered to live in urban areas (20). Although many people will benefit from their urban status, rapid and unplanned urbanization will have negative social and environmental health impacts, particularly on the poorest and most vulnerable. Overall, urbanization will reduce malaria transmission. However, unplanned urbanization will lead to focal transmission, resulting in a disease burden that is disproportionately high among the urban poor. In October 2022, WHO and the UN Human Settlements Programme (UN-Habitat) jointly launched the Global framework for the response to malaria in urban areas (20), which is targeted at policy-makers and relevant stakeholders. The framework has been developed through wide and multidisciplinary consultations, and is based on published evidence and best practices. The framework is a response to malaria in towns and cities, and is expected to have many benefits in improving health and well-being. The framework provides guidance to countries on undertaking a comprehensive malaria response in urban areas, recognizing that: ■ in a few years, most people living in malaria endemic countries will reside in urban areas; ■ urban areas and rural areas can differ in the dynamics of the transmission and burden of malaria and other vector-borne diseases; ■ invasion by vectors that are adapted to breeding in urban environments, such as the recent spread of An. stephensi in the WHO African Region, may be putting urban populations at increased risk; ■ approaches that work to reduce disease burden in rural areas may not work in urban settings, or may need to occur at a smaller, more targeted scale; ■ the malaria response in urban areas requires data on the determinants that are unique to urban ecosystems and that lead to a focal malaria transmission and disease burden; ■ leadership from government departments, industry and finance, research, academia and other sectors tends to be concentrated in urban areas; this provides a greater opportunity for integrated, multisectoral policies, strategies and actions; and ■ as urbanization rapidly increases, there is greater focus on healthier cities, including global political will to address the threats posed by climate change. The framework has a leadership element, two strategic elements, three response pillars and one enabling element (Fig. 2.3). 72 | Overview of key events in 2021–2022 An. stephensi: Anopheles stephensi; WHO: World Health Organization. FIG. 2.2. Aims of the WHO initiative to support an effective response to An. stephensi on the African continent Source: WHO (2022) (18). Innovation, research and development Urban leadership spearheading the response Community engagement and multisectoral response Integrated strategic and response planning Malaria prevention built on environmental control of mosquitoes and targeted use of insecticides and larvicides Surveillance systems to respond to focal transmission Equitable quality of care for malaria provided to all residents WHO: World Health Organization. i Increasing COLLABORATION National malaria control programmes, researchers, funders, and other actors conducting surveillance, research and control of An. stephensi must collaborate effectively to ensure that knowledge is shared, resources are used optimally, and key activities are prioritized. As An. stephensi has the potential to spread quickly, cross-border collaboration is essential, and countries should work together to ensure an effective regional approach. Strengthening SURVEILLANCE Entomological surveillance can determine the extent of the spread of An. stephensi and its role in transmission; it is essential to target specific control measures and assess their impact. Human malaria case surveillance should be used to investigate the potential impact of the vector’s presence on malaria, particularly in urban areas. Such surveillance might provide an indication of the presence of An. stephensi in areas where it has not yet been detected. Improving INFORMATION EXCHANGE Information on the presence of An. stephensi, as well as on successes and failures in attempts to control the vector, needs to be documented and shared widely and rapidly – at both national and international levels – to determine best practices and inform the response across invaded areas. Developing GUIDANCE National malaria control programmes need evidence-based guidance on the appropriate ways to conduct surveillance, implement control measures, develop by-laws, and devote resources to their response to An. stephensi. WHO provided an initial set of recommendations in its 2019 vector alert (17). This guidance will be reviewed and, where appropriate, updated based on best practices and other evidence identified as part of the regional initiative. Prioritizing RESEARCH It will be important to evaluate the impact of vector control interventions, and particularly new tools, against An. stephensi. Conducting research focused on An. stephensi will enable programmes to find better ways of responding to this invasive vector and of integrating control efforts with those targeted at other mosquito vectors. FIG. 2.3. Building blocks of the Global framework for the response to malaria in urban areas Source: WHO (2022) (20). 8W O RL D M AL AR IA R EP O RT 2 02 2 2.6 ROLLOUT OF THE RTS,S/AS01 MALARIA VACCINE IN AREAS OF MODERATE TO HIGH MALARIA TRANSMISSION In October 2021, WHO recommended the RTS,S/AS01 malaria vaccine for the prevention of P. falciparum malaria in children living in regions with moderate to high transmission, as defined by WHO (22, 23). In July 2022, WHO issued prequalification approval for the RTS,S vaccine (24). This is an important milestone for the vaccine rollout and country regulatory approval processes. The WHO Regional Office for Africa’s African Vaccine Regulatory Forum (AVAREF) is committed to using its platform to facilitate the review and registration of the RTS,S vaccine in African countries. Gavi, the Vaccine Alliance (Gavi) and WHO are supporting the rollout of the malaria vaccine. Following the WHO recommendation, in December 2021, the Gavi board approved an initial investment of almost US$ 160 million (2022–2025) to support broader rollout of the malaria vaccine in Gavi-eligible countries (25). Demand for the vaccine is expected to outpace supply in the initial years of rollout. Gavi has estimated that steady-state demand for the vaccine will exceed 80–100 million doses per year (26). In anticipation of this gap in supply and demand, WHO coordinated the development of a framework for allocation of limited malaria vaccine supply, to guide where limited doses should be allocated in a fair and transparent way, based on ethical principles and the best available evidence. The main principle of the framework is to allocate the malaria vaccine across countries in areas of greatest need; that is, areas where the malaria disease burden in children and the risk of death are highest. A primary implication is that all countries will have to consider a phased subnational approach to vaccine implementation, starting in areas of highest need, until supply meets demand. The framework was published in July 2022 following a series of key stakeholder engagements (27). Gavi has published its guidelines for the malaria vaccine application (28); it recommends a phased approach to vaccine introduction, aligned with this allocation framework and within the context of national malaria control strategies. Countries that have expressed interest in introducing the vaccine (including the pilot countries) have been supported through technical workshops to use the best available local data and evidence to guide the development of high-quality applications and national vaccine introduction plans. The workshops are facilitated by Gavi, the WHO Global Malaria Programme (WHO/ GMP), the WHO Department of Immunization, Vaccines and Biologicals (IVB) and the WHO Regional Office for Africa, in collaboration with WHO country offices and global malaria partners. As of October 2022, about 20 countries had participated in the technical assistance workshops. In August 2022, the United Nations Children’s Fund (UNICEF) announced that it had secured supply of the GSK-produced vaccine (29, 30). The contract is for an available malaria vaccine supply of 18 million doses over the next 3 years (2023–2025), with an expected price of €9.30 per dose. This initial price reflects the low supply volumes. Ongoing market-shaping efforts by WHO, Gavi, UNICEF and partners aim to increase vaccine supply and reduce costs over time, to ensure that more children can receive the malaria vaccine as rapidly as possible. Two ways to increase supply are increasing the supply of the current RTS,S/AS01 vaccine (by completing the ongoing product transfer to Bharat Biotech, Ltd) and having other malaria vaccines available. 2.7 HUMANITARIAN AND HEALTH EMERGENCIES Section 2.8 presents an update of the malaria response during the COVID-19 pandemic and reported disruptions to provision of malaria services. However, many malaria endemic countries have been dealing with health and humanitarian emergencies unrelated to the COVID-19 pandemic. In 2019, there were an estimated 148 million people in 37 malaria endemic countries who needed assistance due to health and humanitarian emergencies, not including the COVID-19 pandemic (31). This number increased to 301 million in 2020 and 268 million in 2021 (Fig. 2.4). Conflicts, famine and flooding were the major contributors to these humanitarian emergencies, sometimes compounded by disease outbreaks. Among the countries with the highest need in 2021 in terms of people affected were Afghanistan, Colombia, the Democratic Republic of the Congo, Ethiopia, Mali, Myanmar, Nigeria, Somalia, South Sudan, the Sudan, Uganda and Yemen. In each of these countries, increases in malaria cases occurred above what could be attributed to the COVID-19 pandemic (Section 3). FIG. 2.4. People in humanitarian need in malaria endemic countries as of December 2021 Source: Global humanitarian overview 2022 (31). W H O : W or ld H ea lth O rg an iz at io n. ■ 6 79 9 00 –2 0 00 0 00 ■ 2 0 00 0 01 –4 0 00 0 00 ■ 4 0 00 0 01 –8 0 00 0 00 ■ 8 0 00 0 01 –1 0 00 0 00 0 ■ > 10 0 00 0 00 ■ M al ar ia e nd em ic ■ N o m al ar ia ■ N ot a pp lic ab le 0 20 00 40 00 10 00 km 9 2 | Overview of key events in 2021–2022 10 W O RL D M AL AR IA R EP O RT 2 02 2 2.8 MALARIA SERVICE DISRUPTIONS DURING THE COVID-19 PANDEMIC Disruptions to essential malaria services during the COVID-19 pandemic were monitored using various sources of data: ■ country data assembled by the Alliance for Malaria Prevention (AMP) and the RBM Partnership to End Malaria; ■ qualitative survey data from WHO essential health services (EHS) disruption surveys; and ■ reports by NMPs to WHO on the number of malaria tests performed in each country. Country data assembled by the AMP and the RBM Partnership to End Malaria show that 46 countries had ITN campaigns planned in 2020, to distribute about 272 million nets (Annex 2). By the end of 2020, 74% of all ITNs planned for distribution in 2020 had been distributed (Annex 2, Fig. 2.5). Six countries (Côte d'Ivoire, the Democratic Republic of the Congo, Eritrea, Eswatini, India and Kenya) had distributed less than 60% of their ITNs by the end of 2020 (Annex 2). Of concern, Kenya distributed less than 5% of their ITNs in 2020, while Côte d'Ivoire and Eswatini did not distribute any of their planned ITNs. In 2021, there were 43 countries that had ITN campaigns planned, including 14 countries that had planned to distribute only ITNs carried over from 2020 campaigns (Annex 2). There were 171 million ITNs planned for distribution in 2021, and 70 million of these ITNs were carried over FIG. 2.5. ITNs planned and distributed during mass campaigns in 52 malaria endemic countries, 2020–2022a Source: AMP, RBM Partnership to End Malaria and NMP reports. AMP: Alliance for Malaria Prevention; ITN: insecticide-treated mosquito net; NMP: national malaria programme. a Mass campaigns were not scheduled in 3 countries in 2020 and 1 country in 2021. Data presented here do not include ITNs distributed through continuous distribution channels. 300 250 200 150 100 50 0 Nu m be r o f I TN s ( m illi on ) ITNs planned for distribution in 2020a ITNs distributed in 2020 ITNs remaining for distribution in 2021 ITNs distributed in 2021 from 2020 campaigns ITNs planned for distribution in 2021a ITNs distributed in 2021 ITNs remaining for distribution in 2022 11 2 | Overview of key events in 2021–2022 from 2020 (Fig. 2.5). Overall, 128 million ITNs (75%) were distributed in 2021. Eight countries – Benin, Eritrea, Indonesia, Nigeria, Solomon Islands, Thailand, Uganda and Vanuatu – had distributed less than 60% of their ITNs in 2021. Seven countries – Botswana, the Central African Republic, Chad, Haiti, India, Pakistan and Sierra Leone – did not distribute any of the planned ITNs. India, Nigeria and Uganda – countries supported under the high burden high impact (HBHI) approach – had distributed 0%, 53% and 26%, respectively, of their ITNs planned for distribution in 2021. Since 2020, WHO has implemented three rounds of EHS pulse surveys (32-34). Round 1 (the 2020 survey) was implemented from mid-May to the end of May 2020, except in the countries of the WHO Region of the Americas, where responses were received in September 2020. Round 2 (the second survey) was implemented from December 2020 to March 2021. Round 3 (the third survey) was implemented in November to December 2021. Findings on disruptions to malaria diagnosis and treatment are summarized in Fig. 2.6. They suggest that, in the WHO African Region, disruptions in diagnosis and treatment had eased considerably in the latter part of 2021, with seven countries reporting disruptions, compared with 16 in 2020 and nine in the first quarter of 2021. The number of countries undertaking EHS surveys reduced in most of the other WHO regions in Round 3 of the EHS survey; however, overall, fewer countries reported disruptions to malaria diagnosis and treatment in Round 3. FIG. 2.6. Results from WHO surveys on disruptions to malaria diagnosis and treatment services during the COVID-19 pandemic from three rounds of surveys in 2020–2021 No disruption (<5%); partial disruption (<50%); severe disruption (≥50%). Surveys were conducted between May 2020 and December 2021. Source: WHO Integrated Health Services. ■ No disruption ■ Partial disruption ■ Severe disruption 302520 Number of countries 151050 35 W HO re gi on AFR Round 1 AFR Round 2 AFR Round 3 11 16 15 6 26 7 3 AMR Round 1 AMR Round 2 AMR Round 3 7 8 3 3 2 3 3 1 EMR Round 1 EMR Round 2 EMR Round 3 1 4 3 2 4 1 WPR Round 1 WPR Round 2 WPR Round 3 1 2 5 1 4 4 SEAR Round 1 SEAR Round 2 SEAR Round 3 3 5 1 1 4 1 1 AFR: WHO African Region; AMR: WHO Region of the Americas; EMR: WHO Eastern Mediterranean Region; SEAR: WHO South-East Asia Region; WHO: World Health Organization; WPR: WHO Western Pacific Region. 12 W O RL D M AL AR IA R EP O RT 2 02 2 An additional indicator for tracking disruptions in malaria diagnosis and a proxy for overall disruption in case management was the trend in the number of tests performed by year, comparing the 2 years preceding the pandemic (2018 and 2019) with the 2 peak years of the pandemic (2020 and 2021). The results (which are based on reports to WHO by NMPs) are summarized, by region, in Fig. 2.7. In total, 392 million and 450 million tests were performed in malaria endemic countries in 2018 and 2019, respectively. In 2020 and 2021, 398 million and 435 million malaria tests were performed, respectively. The drop in tests in 2020, compared with 2019, was mainly because fewer tests were performed in the WHO African Region (10.5 million fewer tests) and the South-East Asia Region (38 million fewer tests), the latter driven primarily by reductions in testing in India (a reduction of 37 million tests in 2020 compared with 2019). By 2021, testing in the WHO South-East Asia Region had increased by 15 million, mainly owing to recovery in India. FIG. 2.7. Number of malaria tests performed, by WHO region, in 86 malaria endemic countries Source: NMP reports. 300 250 200 150 100 50 0 ■ 2018 ■ 2019 ■ 2020 ■ 2021 Nu m be r o f m al ar ia te st s ( m illi on ) WPREMRAMRAFR SEAR AFR: WHO African Region; AMR: WHO Region of the Americas; EMR: WHO Eastern Mediterranean Region; NMP: national malaria programme; SEAR: WHO South-East Asia Region; WHO: World Health Organization; WPR: WHO Western Pacific Region. 13 W O RL D M AL AR IA R EP O RT 2 02 2 14 This section presents the number of malaria cases and deaths estimated to have occurred between 2000 and 2021, and the malaria case incidence and mortality rates for the same period. These estimates were used to compute the number of cases and deaths averted, globally and by WHO region, since 2000. This section also presents estimates of the prevalence of exposure to malaria during pregnancy and the burden of low birthweights averted under different scenarios of IPTp coverage. The methods used to estimate the burden of malaria cases and deaths depend on the quality of the national surveillance systems and the availability of data over time (Annex 1). Most of the global malaria burden is accounted for by countries with moderate to high transmission in sub-Saharan Africa; however, these countries generally have weak surveillance systems. Case estimates for these countries are calculated using an approach that transforms modelled community parasite prevalence into case incidence within a geospatial framework. Each year, population estimates are updated in line with UN population growth projections (35). As these population estimates change, the number of malaria cases estimated for a given level of incidence may also change. Malaria deaths for these countries are also estimated from a cause of death (CoD) fraction (36) for malaria that is applied to the trends in all-cause mortality in children aged under 5 years (37), and to which a factor for malaria deaths among those aged 5 years and over is applied. For countries with stronger surveillance systems, either reported data are used or cases are estimated by adjusting national data for rates of treatment seeking, testing and reporting. Where adjustments are applied to national case data, malaria deaths are estimated by applying a species-specific case fatality rate to these data. Estimates for both cases and deaths now include the impact of disruptions to essential malaria services during the COVID-19 pandemic. For both 2020 and 2021, new country epidemiological and service disruption data have affected the estimation of cases and deaths and the percentage attributable to the COVID-19 pandemic. 3.1 GLOBAL ESTIMATES OF MALARIA CASES AND DEATHS, 2000–2021 Globally in 2021, there were an estimated 247 million malaria cases (Table 3.1) in 84 malaria endemic countries (including the territory of French Guiana) (Fig. 3.1), an increase of 2 million cases compared with 2020. Between 2000 and 2015, case numbers steadily decreased from 245 million to 230 million across the 108 countries that were malaria endemic in 2000. Since 2016, malaria cases have increased; the largest annual increase of 13 million cases was observed between 2019 and 2020 during the first year of the 3GLOBAL TRENDS IN THE BURDEN OF MALARIA 15 WHO: World Health Organization. FIG. 3.1. Countries with indigenous cases in 2000 and their status by 2021 Countries with zero indigenous cases for at least 3 consecutive years are considered to have eliminated malaria. In 2021, the Islamic Republic of Iran and Malaysia reported zero indigenous cases for the fourth consecutive year; also, Belize and Cabo Verde reported zero indigenous cases for the third time. China and El Salvador were certified malaria free in 2021, following 4 years of zero malaria cases. Source: WHO database. ■ One or more indigenous cases ■ Zero indigenous cases in 2019–2021 ■ Zero indigenous cases in 2021 ■ Zero indigenous cases (>3 years) in 2021 ■ Certified malaria free after 2000 ■ No malaria ■ Not applicable TABLE 3.1. Global estimated malaria cases and deaths, 2000–2021 Estimated cases and deaths are shown with 95% upper and lower confidence intervals. Source: WHO estimates. Year Number of cases (000) Number of deaths Point Lower bound Upper bound % P. vivax Point Lower bound Upper bound 2000 245 000 229 000 266 000 8.4% 897 000 866 000 938 000 2001 250 000 231 000 273 000 8.3% 888 000 855 000 933 000 2002 246 000 228 000 268 000 7.7% 847 000 816 000 890 000 2003 249 000 232 000 272 000 8.1% 825 000 794 000 870 000 2004 251 000 232 000 278 000 7.9% 807 000 773 000 866 000 2005 249 000 231 000 272 000 8.0% 783 000 751 000 832 000 2006 244 000 226 000 268 000 7.0% 771 000 739 000 820 000 2007 241 000 224 000 263 000 6.6% 751 000 721 000 794 000 2008 241 000 224 000 262 000 6.4% 731 000 700 000 773 000 2009 246 000 228 000 269 000 6.3% 721 000 685 000 771 000 2010 248 000 229 000 272 000 6.6% 704 000 667 000 757 000 2011 242 000 225 000 262 000 7.0% 660 000 629 000 704 000 2012 238 000 222 000 258 000 6.9% 622 000 593 000 664 000 2013 233 000 217 000 253 000 5.9% 603 000 572 000 648 000 2014 231 000 211 000 253 000 5.5% 584 000 549 000 639 000 2015 230 000 211 000 253 000 4.9% 577 000 540 000 635 000 2016 232 000 214 000 253 000 4.6% 580 000 545 000 642 000 2017 237 000 219 000 257 000 3.7% 587 000 553 000 654 000 2018 231 000 214 000 252 000 3.0% 567 000 532 000 642 000 2019 232 000 213 000 255 000 2.7% 568 000 532 000 654 000 2020 245 000 222 000 273 000 1.8% 625 000 583 000 747 000 2021 247 000 224 000 276 000 2.0% 619 000 577 000 754 000 P. vivax: Plasmodium vivax; WHO: World Health Organization. 16 W O RL D M AL AR IA R EP O RT 2 02 2 COVID-19 pandemic. The increase in cases between 2020 and 2021 was considerably smaller, with 2 million additional cases. Overall, an estimated additional 13.4 million cases were attributed to disruptions during the COVID-19 pandemic. Most of the increase in case numbers over the past 5 years occurred in countries in the WHO African Region (Section 3.2). Malaria case incidence declined from 82.3 per 1000 population at risk in 2000 to 57.2 in 2019, before increasing by 4% to 59.4 in 2020. There was no change in case incidence between 2020 and 2021 (Fig. 3.2a). Despite the increase in cases, the results suggest that efforts by countries and partners averted the worst-case scenario projected at the start of the pandemic (38). Since 2000, malaria deaths declined steadily from 897 000 to 577 000 in 2015, and to 568 000 in 2019. The malaria mortality rate halved between 2000 and 2015, from 30.1 per 100 000 population at risk to 15.0 per 100 000; it then continued to decline, reaching 14.0 per 100 000 in 2019 (Fig. 3.2b). In 2020, the mortality rate increased to about 15.1 per 100 000 population at risk before decreasing slightly to 14.8 in 2021. FIG. 3.2. Global trends in a) malaria case incidence (cases per 1000 population at risk) and b) mortality rate (deaths per 100 000 population at risk), 2000–2021; and c) distribution of malaria cases and d) deaths by country, 2021 Source: WHO estimates. a) 100 80 60 40 20 0 M al ar ia c as es p er 10 00 p op ul at io n at ri sk 2000 2005 20152010 20212020 59.259.460.1 82.3 50 40 30 20 10 0M al ar ia d ea th s p er 10 0 00 0 po pu la tio n at ri sk 2000 2005 20152010 20212020 14.815.115.0 30.1 b) 17 3 | Global trends in the burden of malaria However, in 2020, malaria deaths increased to an estimated 625 000, an increase of 57 000 deaths from 2019 (Table 3.1). The estimated deaths in 2021 were 619 000, a slight decline compared with 2020. Between 2019 and 2021 there were 63 000 deaths that were due to disruptions to essential malaria services during the COVID-19 pandemic (Annex 1, Table 3.1). The percentage of total malaria deaths among children aged under 5 years declined over the past 20 years, from 87.3% in 2000 to 76.8% in 2015, but since then it has remained unchanged. In 2021, 29 of the 84 countries that were malaria endemic (including the territory of French Guiana) accounted for about 96% of malaria cases and deaths globally (Fig. 3.2c and Fig. 3.2d). Four countries accounted for almost half of all cases: Nigeria (26.6%), the Democratic Republic of the Congo (12.3%), Uganda (5.1%) and Mozambique (4.1%) (Fig. 3.2c). Also, four countries accounted for just over half of all malaria deaths globally: Nigeria (31.3%), the Democratic Republic of the Congo (12.6%), the United Republic of Tanzania (4.1%) and the Niger (3.9%) (Fig. 3.2d). Nigeria accounted for 38.4% of global malaria deaths in children aged under 5 years. Nigeria, 26.6% Uganda, 5.1% Democratic Republic of the Congo, 12.3% Niger, 3.2% Mozambique, 4.1% Angola, 3.4% United Republic of Tanzania, 3.1% Burkina Faso, 3.3% Benin, 2.0% Ghana, 2.2% Guinea, 1.8% Mali, 3.1% Côte d’Ivoire, 3.0% Burundi, 1.5% Madagascar, 1.6% Ethiopia, 1.7% Others, 4.4% Cameroon, 2.7% Kenya, 1.3% Sudan, 1.3% South Sudan, 1.2% Chad, 1.4% Zambia, 1.4% Sierra Leone, 1.1% Togo, 0.8% Liberia, 0.8% Central African Republic, 0.7% Rwanda, 1.2% India, 1.7% India, 1.2% Malawi, 1.7% Sierra Leone, 1.4% Nigeria, 31.3% Burkina Faso, 3.4% Democratic Republic of the Congo, 12.6% Côte d’Ivoire, 2.4% Mozambique, 3.8% Angola, 2.4% Cameroon, 2.3% Kenya, 1.9% Mali, 3.3% Benin, 1.7% Guinea, 1.6% Chad, 2.0% Ethiopia, 1.5% Uganda, 3.2% Liberia, 0.6% Madagascar, 1.6% Zambia, 1.4% Niger, 3.9% United Republic of Tanzania, 4.1% Burundi, 0.9% Malawi, 1.2% Sudan, 1.2% Togo, 0.6% Central African Republic, 0.8% Senegal, 0.7% South Sudan, 1.2% Others, 3.7% Ghana, 2.0% c) d) WHO: World Health Organization. 18 W O RL D M AL AR IA R EP O RT 2 02 2 3.2 ESTIMATED MALARIA CASES AND DEATHS IN THE WHO AFRICAN REGION, 2000–2021 Between 2019 and 2020, estimated malaria cases increased from 218 million to 232 million, and deaths from 544 000 to 599 000 in the WHO African Region (Table 3.2). In 2021, while cases increased to 234 million, deaths decreased to 593 000. This region accounted for about 95% of cases and 96% of deaths globally; 78.9% of all deaths in this region were among children aged under 5 years in 2021 compared with 91.0% in 2000. Since 2000, malaria case incidence reduced from 372.6 to 225.5 cases per 1000 population at risk in 2019. As a result of service disruptions owing to the COVID-19 pandemic, malaria case incidence increased to 233.6 per 1000 population at risk in 2020, but then declined to 229.4 in 2021 (Fig. 3.3a). Between 2000 and 2019, the malaria mortality rate reduced by 62%, from 148.4 to 56.3 per 100 000 population at risk (Fig. 3.3b). In TABLE 3.2. Estimated malaria cases and deaths in the WHO African Region, 2000–2021 Estimated cases and deaths are shown with 95% upper and lower confidence intervals. Source: WHO estimates. Year Number of cases (000) Number of deaths Point Lower bound Upper bound % P. vivax Point Lower bound Upper bound 2000 211 000 195 000 229 000 2.9% 841 000 815 000 875 000 2001 216 000 197 000 237 000 2.6% 834 000 806 000 873 000 2002 214 000 196 000 234 000 2.3% 796 000 769 000 831 000 2003 216 000 199 000 237 000 2.3% 774 000 748 000 810 000 2004 216 000 198 000 242 000 1.8% 754 000 725 000 807 000 2005 213 000 196 000 235 000 1.3% 729 000 704 000 771 000 2006 212 000 195 000 234 000 1.3% 722 000 696 000 764 000 2007 212 000 195 000 232 000 1.3% 705 000 679 000 742 000 2008 212 000 196 000 230 000 1.2% 685 000 660 000 717 000 2009 216 000 199 000 237 000 1.3% 671 000 642 000 715 000 2010 217 000 199 000 239 000 1.6% 653 000 623 000 700 000 2011 214 000 198 000 234 000 2.3% 617 000 590 000 656 000 2012 214 000 199 000 233 000 2.7% 584 000 556 000 622 000 2013 214 000 197 000 232 000 2.6% 570 000 542 000 613 000 2014 211 000 191 000 233 000 2.8% 549 000 516 000 601 000 2015 211 000 192 000 232 000 2.2% 542 000 509 000 598 000 2016 211 000 193 000 231 000 1.5% 542 000 511 000 600 000 2017 218 000 201 000 238 000 1.0% 555 000 522 000 619 000 2018 215 000 198 000 236 000 0.2% 541 000 508 000 614 000 2019 218 000 199 000 240 000 0.3% 544 000 509 000 628 000 2020 232 000 208 000 260 000 0.3% 599 000 559 000 722 000 2021 234 000 210 000 262 000 0.3% 593 000 553 000 728 000 P. vivax: Plasmodium vivax; WHO: World Health Organization. 19 3 | Global trends in the burden of malaria 2020, the mortality rate increased to 60.4 per 100 000 population before decreasing in 2021 to 58.2. Cabo Verde has reported zero malaria deaths since 2018. In 2021, Botswana, the Comoros, Eritrea, Eswatini and Sao Tome and Principe all reported fewer than 10 deaths. Since 2015, the rate of progress in both cases and deaths has stalled in several countries with moderate or high transmission; the situation was made worse, especially in sub-Saharan Africa, by disruptions during the COVID-19 pandemic and other humanitarian emergencies (Fig. 3.3a-b). Between 2019 and 2021 there were substantial increases in estimated case numbers in Nigeria (4.0 million), Madagascar (2.8 million), Uganda (1.7 million), the Democratic Republic of the Congo (1.6 million) and Angola (1.4 million). Over the same period, Rwanda saw a decrease of more than 3.6 million cases. The distribution of cases by country in 2021 is shown in Fig. 3.3c. WHO: World Health Organization. FIG. 3.3. Trends in a) malaria case incidence (cases per 1000 population at risk) and b) mortality rate (deaths per 100 000 population at risk), 2000–2021; and c) malaria cases by country in the WHO African Region, 2021 Source: WHO estimates. 375 300 225 150 75 0M al ar ia c as es p er 10 00 p op ul at io n at ri sk 20 00 20 05 20 15 20 10 20 21 20 20 229.4 233.6 243.5 372.6 150 120 90 60 30 0 M al ar ia d ea th s p er 10 0 00 0 po pu la tio n at ri sk 20 00 20 05 20 15 20 10 20 21 20 20 58.2 60.462.5 148.4 a) D em oc ra tic R ep ub lic o f t he C on go Ni ge ria Es w at in i Bo ts w an a Sa o To m e an d Pr in ci pe So ut h Af ric a Na m ib ia Co m or os G ui ne a- Bi ss au Er itr ea M au rit an ia G am bi a Zi m ba bw e Eq ua to ria l G ui ne a G ab on Se ne ga l Co ng o Rw an da Ce nt ra l A fri ca n Re pu bl ic Lib er ia Et hi op ia To go Ke ny a Za m bi a Ch ad G ui ne a Bu ru nd i M al aw i Si er ra L eo ne M ad ag as ca r Be ni n So ut h Su da n G ha na Ca m er oo n M al i Cô te d ’Iv oi re An go la Bu rk in a Fa so Ni ge r M oz am bi qu e Un ite d Re pu bl ic o f T an za ni a Ug an da Ca bo V er de 50 000 25 000 10 000 5 000 0 0 75 000 Nu m be r o f m al ar ia c as es (0 00 ) 15 000 c) b) 20 W O RL D M AL AR IA R EP O RT 2 02 2 3.3 ESTIMATED MALARIA CASES AND DEATHS IN THE WHO SOUTH-EAST ASIA REGION, 2000–2021 The WHO South-East Asia Region had nine malaria endemic countries in 2021, accounting for 5.4 million cases and contributing 2% of the burden of malaria cases globally (Table 3.3). In 2021, India accounted for about 79% of all malaria cases (Fig. 3.4c). About 40% of all cases in the region were due to P. vivax. Over the past 20 years, malaria cases have reduced by 76%, from 22.8 million in 2000 to 5.4 million in 2021, and incidence has reduced by 82%, from 17.9 to 3.2 per 1000 population at risk (Fig. 3.4a). Sri Lanka was certified malaria free in 2016. Timor-Leste reported zero indigenous cases in 2021. TABLE 3.3. Estimated malaria cases and deaths in the WHO South-East Asia Region, 2000–2021 Estimated cases and deaths are shown with 95% upper and lower confidence intervals. Source: WHO estimates. Year Number of cases (000) Number of deaths Point Lower bound Upper bound % P. vivax Point Lower bound Upper bound 2000 22 800 18 400 28 800 47.6% 35 000 19 000 54 000 2001 23 100 18 800 29 100 50.5% 34 000 19 000 52 000 2002 21 900 17 800 27 700 49.9% 32 000 18 000 50 000 2003 23 100 18 600 29 100 52.3% 33 000 18 000 50 000 2004 25 400 20 300 32 300 51.9% 36 000 20 000 57 000 2005 27 200 21 100 35 900 53.7% 38 000 21 000 61 000 2006 22 500 17 400 30 500 51.4% 32 000 18 000 52 000 2007 22 000 16 900 29 900 49.5% 33 000 18 000 53 000 2008 23 200 17 600 31 900 47.4% 35 000 19 000 58 000 2009 23 700 17 800 33 200 45.2% 37 000 20 000 63 000 2010 24 000 18 700 32 100 44.8% 38 000 21 000 60 000 2011 20 600 16 000 27 700 46.0% 32 000 18 000 51 000 2012 17 700 13 900 23 600 47.7% 27 000 16 000 42 000 2013 13 200 10 400 17 500 46.1% 20 000 11 000 32 000 2014 12 800 10 000 17 000 35.0% 23 000 12 000 38 000 2015 13 200 10 400 17 600 34.3% 24 000 12 000 39 000 2016 13 700 10 100 19 500 34.9% 25 000 12 000 42 000 2017 10 200 7 700 14 100 37.3% 18 000 9 000 30 000 2018 7 500 5 400 10 300 50.6% 11 000 6 000 18 000 2019 6 400 4 600 8 800 51.3% 9 000 5 000 15 000 2020 5 000 3 600 6 800 36.3% 9 000 5 000 15 000 2021 5 400 4 000 7 300 39.7% 9 000 5 000 15 000 P. vivax: Plasmodium vivax; WHO: World Health Organization. 21 3 | Global trends in the burden of malaria Between 2020 and 2021 there was an increase of 400 000 cases in the region, and over half of these cases were in Myanmar. Increases in cases and incidence were also seen in Bangladesh, the Democratic People’s Republic of Korea, India and Indonesia. Malaria deaths reduced by 74%, from about 35 000 in 2000 to 9000 in 2019. The number of estimated deaths has remained the same for the past 3 years. Between 2000 and 2021, the malaria mortality rate reduced by 81%, from 2.7 to 0.5 per 100 000 population at risk (Fig. 3.4b). India accounted for about 83% of all malaria deaths in this region in 2021. Between 2020 and 2021, all countries in this region in which malaria deaths occurred had reported either a reduction or no change in the malaria mortality rate except for Myanmar, where the mortality rate increased more than three times, from 0.2 to 0.74 per 100 000 population at risk. Bhutan and Timor-Leste have reported zero malaria deaths since 2013 and 2015, respectively. In 2021, Thailand reported zero indigenous deaths for the first time and Nepal reported one death. WHO: World Health Organization. Democratic People’s Republic of Korea India BhutanNepalThailandBangladeshMyanmarIndonesia Timor-Leste 1 800 3 600 600 300 0 0 5 400 Nu m be r o f m al ar ia c as es (0 00 ) 900 FIG. 3.4. Trends in a) malaria case incidence (cases per 1000 population at risk) and b) mortality rate (deaths per 100 000 population at risk), 2000–2021; and c) malaria cases by country in the WHO South-East Asia Region, 2021 Source: WHO estimates. 25 20 15 10 5 0M al ar ia c as es p er 10 00 p op ul at io n at ri sk 20 00 20 05 20 15 20 10 20 21 20 20 3.2 3.0 8.4 17.9 5 4 3 2 1 0 M al ar ia d ea th s p er 10 0 00 0 po pu la tio n at ri sk 20 00 20 05 20 15 20 10 20 21 20 20 0.5 0.5 1.5 2.7 a) c) b) TABLE 3.4. Estimated malaria cases and deaths in the WHO Eastern Mediterranean Region, 2000–2021 Estimated cases and deaths are shown with 95% upper and lower confidence intervals. Source: WHO estimates. Year Number of cases (000) Number of deaths Point Lower bound Upper bound % P. vivax Point Lower bound Upper bound 2000 6 900 5 500 11 100 27.3% 13 600 8 500 25 600 2001 7 200 5 600 11 900 27.2% 14 100 8 900 25 700 2002 6 800 5 200 11 900 28.3% 13 100 8 400 25 900 2003 6 400 5 000 11 100 29.2% 12 200 7 700 24 300 2004 5 200 4 200 8 900 25.0% 10 500 6 600 20 600 2005 5 400 4 200 9 400 22.3% 11 200 7 000 22 200 2006 5 400 4 100 10 100 20.5% 11 300 7 000 24 500 2007 4 700 3 800 6 500 24.4% 9 600 6 100 14 700 2008 3 700 2 900 5 200 28.8% 7 100 4 500 10 800 2009 3 600 2 800 5 300 29.7% 6 900 4 400 10 800 2010 4 500 3 400 6 400 28.8% 8 600 5 500 13 300 2011 4 600 3 500 6 500 39.1% 7 800 5 100 11 500 2012 4 300 3 300 6 100 33.1% 8 000 5 100 11 500 2013 4 200 3 300 5 600 34.3% 7 500 4 900 10 700 2014 4 000 3 300 5 100 31.1% 7 500 4 800 10 900 2015 4 300 3 500 5 700 30.0% 8 200 5 200 12 100 2016 5 400 4 300 6 900 36.1% 9 500 5 900 14 500 2017 5 400 4 100 7 300 30.1% 10 200 6 000 16 800 2018 5 600 4 200 8 000 26.7% 11 100 6 400 18 700 2019 5 700 4 200 8 200 22.0% 11 900 6 700 20 000 2020 5 900 4 200 8 500 17.7% 12 700 7 000 21 800 2021 6 200 4 400 9 000 18.0% 13 400 7 500 23 000 P. vivax: Plasmodium vivax; WHO: World Health Organization. 22 W O RL D M AL AR IA R EP O RT 2 02 2 3.4 ESTIMATED MALARIA CASES AND DEATHS IN THE WHO EASTERN MEDITERRANEAN REGION, 2000–2021 Estimated malaria cases in the WHO Eastern Mediterranean Region reduced by 38% between 2000 and 2015, from 6.9 million to 4.3 million, before increasing by 44% between 2015 and 2021 to reach 6.2 million cases (Table 3.4). Between 2020 and 2021, increases in estimated malaria cases were seen in Somalia, the Sudan and Yemen, with an additional 205 000, 64 000 and 180 000 cases, respectively. Estimated cases reduced in Afghanistan, Djibouti and Pakistan. In 2021, 18% of the cases in this region were due to P. vivax, mainly in Afghanistan and Pakistan. Due to instabilities in Afghanistan, Somalia, the Sudan and Yemen, it has been difficult to obtain sufficiently reliable data to estimate the recent trends of the burden of malaria in these countries. As such, current estimates should be interpreted with caution. Furthermore, recent floods in Pakistan suggest a malaria upsurge, which will probably lead to a considerably higher estimate in 2022. WHO will be supporting subnational burden estimation analyses in these countries to verify the trends in estimated cases. Estimated malaria deaths also reduced by about 45%, from 13 600 in 2000 to 7500 in 2014, and then increased by 79% between 2014 and 2021 to reach 23 3 | Global trends in the burden of malaria 13 400 deaths (Table 3.4). This increase in deaths was due to increases in Djibouti, Somalia, the Sudan and Yemen. Most of the estimated deaths were observed in the Sudan, where more than 90% of cases are due to P. falciparum, which is responsible for almost all malaria-related fatalities. About 58% of the deaths in the region were in the Sudan. Over the period 2000–2021, malaria case incidence declined from 20.3 to 11.6 cases per 1000 population at risk and the mortality rate declined from 4.0 to 2.5 deaths per 100 000 population at risk (Fig. 3.5a-b). Although case incidence and mortality rates reduced overall between 2000 and 2021, the largest reductions (of 58% and 60%, respectively), were seen between 2000 and 2009. Apart from two small increases in incidence rates in 2010 and 2016, there has been little change over the past 10 years. Mortality rates have gradually increased since 2016 by a total of 28%. In 2021, the Sudan accounted for most of the estimated malaria cases in this region (54%), followed by Somalia, Yemen, Pakistan, Afghanistan and Djibouti (Fig. 3.5c). In 2021, Saudi Arabia reported zero indigenous malaria cases for the first time and the Islamic Republic of Iran reported no indigenous malaria cases for a fourth consecutive year. Iraq, Morocco, Oman and the Syrian Arab Republic last reported indigenous malaria cases in 2008, 2004, 2007 and 2004, respectively (Annex 4-I). In 2021, all countries in the region reported zero malaria deaths apart from Djibouti, the Sudan and Yemen. WHO: World Health Organization. Sudan Saudi ArabiaDjiboutiAfghanistanYemen PakistanSomalia Iran (Islamic Republic of) 2 000 2 500 1 500 1 000 500 0 3 000 3 500 Nu m be r o f m al ar ia c as es (0 00 ) 00 FIG. 3.5. Trends in a) malaria case incidence (cases per 1000 population at risk) and b) mortality rate (deaths per 100 000 population at risk), 2000–2021; and c) malaria cases by country in the WHO Eastern Mediterranean Region, 2021 Source: WHO estimates. 25 20 15 10 5 0M al ar ia c as es p er 10 00 p op ul at io n at ri sk 11.6 11.2 9.0 20.3 20 00 20 05 20 15 20 10 20 21 20 20 5 4 3 2 1 0 M al ar ia d ea th s p er 10 0 00 0 po pu la tio n at ri sk 2.5 2.41.7 4.0 20 00 20 05 20 15 20 10 20 21 20 20 a) c) b) TABLE 3.5. Estimated malaria cases and deaths in the WHO Western Pacific Region, 2000–2021 Estimated cases and deaths are shown with 95% upper and lower confidence intervals. Source: WHO estimates. Year Number of cases (000) Number of deaths Point Lower bound Upper bound % P. vivax Point Lower bound Upper bound 2000 2 805 1 778 4 049 17.4% 6 200 3 400 10 100 2001 2 477 1 481 3 653 20.3% 5 300 2 800 8 900 2002 2 192 1 329 3 223 20.6% 4 700 2 500 7 900 2003 2 359 1 446 3 501 20.1% 5 000 2 700 8 600 2004 2 702 1 557 4 067 22.5% 5 600 2 900 9 800 2005 2 301 1 350 3 499 28.7% 4 500 2 300 7 900 2006 2 463 1 485 3 650 27.0% 4 900 2 600 8 300 2007 1 847 981 2 893 21.9% 3 900 1 800 7 200 2008 1 673 862 2 693 20.5% 3 600 1 600 6 600 2009 2 225 1 246 3 384 20.5% 4 700 2 300 8 800 2010 1 677 967 2 517 22.2% 3 500 1 700 6 300 2011 1 422 847 2 138 21.7% 3 000 1 500 5 400 2012 1 698 867 3 015 23.2% 3 500 1 400 7 200 2013 1 756 1 121 2 566 13.6% 4 000 1 800 7 200 2014 2 014 1 340 2 925 30.9% 3 800 1 800 6 700 2015 1 247 931 1 622 27.0% 2 500 1 300 4 000 2016 1 472 1 073 1 921 25.1% 3 000 1 400 5 000 2017 1 576 1 145 2 101 28.5% 3 000 1 500 5 100 2018 1 693 1 242 2 239 35.9% 3 000 1 500 5 100 2019 1 434 1 091 1 811 35.3% 2 600 1 300 4 300 2020 1 650 1 196 2 150 29.5% 3 200 1 400 5 400 2021 1 426 1 046 1 839 31.9% 2 600 1 200 4 600 P. vivax: Plasmodium vivax; WHO: World Health Organization. 24 W O RL D M AL AR IA R EP O RT 2 02 2 3.5 ESTIMATED MALARIA CASES AND DEATHS IN THE WHO WESTERN PACIFIC REGION, 2000–2021 Malaria cases decreased by 49% in the WHO Western Pacific Region, from 2.8 million cases in 2000 to an estimated 1.4 million cases in 2021. An increase of 15% was observed between 2019 and 2020; however, in 2021, cases decreased again by 14% (Table 3.5). Malaria deaths also decreased significantly, by 58%, from about 6200 deaths in 2000 to 2600 deaths in 2021, with an increase between 2019 and 2020 (from 2600 to 3200 deaths). Increases in cases and deaths between 2019 and 2020 were mainly due to increases in Papua New Guinea. Between 2020 and 2021, increases in cases were observed in the Lao People’s Democratic Republic and Solomon Islands. The proportion of cases in the region due to P. vivax has increased over time, from about 17% in 2000 to almost a third of all cases in 2021, with effective malaria prevention and treatment contributing to reductions in the burden of P. falciparum. 25 3 | Global trends in the burden of malaria In the period 2000–2021, malaria case incidence reduced from 4.3 to 1.9 cases per 1000 population at risk (Fig. 3.6a), and the malaria mortality rate reduced from 0.9 to 0.3 deaths per 100 000 population at risk (Fig. 3.6b). Papua New Guinea accounted for 87% of all cases in this region in 2021, followed by Solomon Islands, Cambodia and the Philippines (Fig. 3.6c). China was certified malaria free in 2021. Malaysia has had no cases due to the four human malaria parasites for 4 consecutive years, but for the past 5 years there has been an increase in the number of zoonotic P. knowlesi malaria cases, with 3575 cases reported in 2021. Four countries had fewer than 10 000 estimated cases in 2021: the Lao People’s Democratic Republic (6403), the Republic of Korea (274), Vanuatu (576) and Viet Nam (453). Papua New Guinea accounted for 94% of all deaths in the region. There have been zero reported malaria deaths in the Republic of Korea and Vanuatu since 2012, Cambodia since 2018 and Viet Nam since 2019. No deaths due to human malaria have been reported in Malaysia since 2018; however, a small number of P. knowlesi malaria deaths have been reported every year since then, with 13 deaths occurring in 2021. WHO: World Health Organization. FIG. 3.6. Trends in a) malaria case incidence (cases per 1000 population at risk) and b) mortality rate (deaths per 100 000 population at risk), 2000–2021; and c) malaria cases by country in the WHO Western Pacific Region, 2021 Source: WHO estimates. 5 4 3 2 1 0M al ar ia c as es p er 10 00 p op ul at io n at ri sk 1.9 2.2 1.7 4.3 20 00 20 05 20 15 20 10 20 21 20 20 1.0 0.8 0.6 0.4 0.2 0 M al ar ia d ea th s p er 10 0 00 0 po pu la tio n at ri sk 0.3 0.3 0.4 0.9 20 00 20 05 20 15 20 10 20 21 20 20 a) Papua New Guinea Vanuatu Viet NamLao People’s Democratic Republic PhilippinesCambodiaSolomon Islands Malaysia 180 120 60 0 1 250 1 000 750 Nu m be r o f m al ar ia c as es (0 00 ) 0 Republic of Korea c) b) TABLE 3.6. Estimated malaria cases and deaths in the WHO Region of the Americas, 2000–2021 Estimated cases and deaths are shown with 95% upper and lower confidence intervals. Source: WHO estimates. Year Number of cases (000) Number of deaths Point Lower bound Upper bound % P. vivax Point Lower bound Upper bound 2000 1 549 1 400 1 710 71.6% 919 776 1 124 2001 1 297 1 169 1 431 67.1% 832 699 1 039 2002 1 183 1 078 1 297 67.7% 765 622 976 2003 1 159 1 067 1 262 68.6% 725 581 938 2004 1 147 1 069 1 235 69.5% 712 567 919 2005 1 273 1 201 1 359 70.3% 688 539 907 2006 1 097 1 033 1 175 68.3% 584 445 791 2007 989 907 1 073 70.2% 504 381 696 2008 696 643 761 71.1% 470 322 699 2009 688 635 753 70.5% 464 322 686 2010 818 746 901 70.9% 502 354 728 2011 615 568 671 68.9% 464 321 674 2012 585 545 634 68.9% 430 308 607 2013 576 530 630 64.1% 470 337 647 2014 475 444 510 69.4% 348 259 449 2015 573 531 620 70.1% 390 288 505 2016 688 637 749 67.3% 530 379 693 2017 946 878 1 032 73.9% 665 452 902 2018 929 861 1 014 78.1% 572 391 773 2019 894 824 979 77.2% 510 337 703 2020 646 599 700 68.2% 418 296 556 2021 597 554 646 71.5% 334 247 436 P. vivax: Plasmodium vivax; WHO: World Health Organization. 26 W O RL D M AL AR IA R EP O RT 2 02 2 3.6 ESTIMATED MALARIA CASES AND DEATHS IN THE WHO REGION OF THE AMERICAS, 2000–2021 Between 2000 and 2021, in the WHO Region of the Americas, malaria cases and incidence declined by 60% (from 1.5 million to 0.6 million) and 70% (from 14.1 to 4.2 cases per 1000 population at risk), respectively (Table 3.6, Fig. 3.7a). Over the same period, malaria deaths and the mortality rate reduced by 64% (from 919 to 334) and 73% (from 0.8 to 0.2 deaths per 100 000 population at risk), respectively (Table 3.6, Fig. 3.7b). The Bolivarian Republic of Venezuela, Brazil and Colombia accounted for 79% of all cases in this region (Fig. 3.7c). Most of the cases in this region are due to P. vivax (71.5% in 2021). Progress in this region suffered in recent years because of a major increase in malaria in the Bolivarian Republic of Venezuela, which had about 35 500 cases in 2000, rising to over 482 000 by 2017. In 2020, however, cases reduced by more than half compared with 2019, from 467 000 to 223 000 cases, and then reduced further in 2021, to 205 000 cases. 27 3 | Global trends in the burden of malaria In 2021, the following countries saw substantial reductions in case burden compared with 2019: the Bolivarian Republic of Venezuela (–263 000), Brazil (–17 000), Colombia (–17 000) and Peru (–22 000), with more modest reductions seen in the Dominican Republic, French Guiana, Guatemala, Guyana and Mexico. During the same period, estimated cases increased slightly in Ecuador and the Plurinational State of Bolivia, and more than doubled in Honduras and Panama. Following an increase in estimated cases between 2019 and 2020, Haiti had a considerable reduction in cases in 2021, by about 18 000 compared with 2020; smaller reductions in cases were also seen in Nicaragua and Suriname. Three countries – Argentina, El Salvador and Paraguay – were certified malaria free in 2019, 2021 and 2018, respectively. Belize reported zero indigenous malaria cases for the third consecutive year. There are few malaria-related deaths in the region; for example, there were an estimated 334 deaths in 2021, most being in adults (78%). WHO: World Health Organization. FIG. 3.7. Trends in a) malaria case incidence (cases per 1000 population at risk) and b) mortality rate (deaths per 100 000 population at risk), 2000–2021; and c) malaria cases by country in the WHO Region of the Americas, 2021 Source: WHO estimates. 15 12 9 6 3 0M al ar ia c as es p er 10 00 p op ul at io n at ri sk 4.2 4.6 4.3 14.1 20 00 20 05 20 15 20 10 20 21 20 20 1.0 0.8 0.6 0.4 0.2 0 M al ar ia d ea th s p er 10 0 00 0 po pu la tio n at ri sk 0.2 0.30.3 0.8 20 00 20 05 20 15 20 10 20 21 20 20 a) Su rin am e Co st a Ri ca Ho nd ur as Fr en ch G ui an a M ex ic o Pa na m a Do m in ic an R ep ub lic Ec ua do r G ua te m al a Bo liv ia (P lu rin at io na l St at e of ) Ha iti Ni ca ra gu a G uy an a Pe ru Co lo m bi a Br az il Ve ne zu el a (B ol iv ar ia n Re pu bl ic o f) 100 50 0 250 Nu m be r o f m al ar ia c as es (0 00 ) 150 200 Be liz e 0 c) b) 28 W O RL D M AL AR IA R EP O RT 2 02 2 AFR: WHO African Region; AMR: WHO Region of the Americas; EMR: WHO Eastern Mediterranean Region; EUR: WHO European Region; SEAR: WHO South-East Asia Region; WHO: World Health Organization; WPR: WHO Western Pacific Region. 3.7 ESTIMATED MALARIA CASES AND DEATHS IN THE WHO EUROPEAN REGION, 2000–2021 Since 2015, the WHO European Region has been free of malaria. The last country to report an indigenous malaria case was Tajikistan in 2014. Throughout the period 2000–2021, no malaria deaths were reported in the WHO European Region. 3.8 CASES AND DEATHS AVERTED SINCE 2000, GLOBALLY AND BY WHO REGION Cases and deaths averted over the period 2000–2021 were calculated by comparing the current annual estimated burden of malaria with the malaria case incidence and mortality rates from 2000, assuming that, as a comparison, they remained constant throughout the same period (Annex 1). The analysis showed that Cases (million) Deaths (000) 2 000 1 600 1 200 800 400 0 250 200 150 100 50 0 100 80 60 40 20 0 40 32 24 16 8 0 20 16 12 8 4 0 2 000 1 600 1 200 800 400 0 0.75 0.60 0.45 0.30 0.15 0 12 500 10 000 7 500 5 000 2 500 0 375 300 225 150 75 0 200 160 120 80 40 0 100 80 60 40 20 0 12.5 10.0 7.5 5.0 2.5 0 12 500 10 000 7 500 5 000 2 500 0 AFR SEAR EMR WPR AMR World EUR 20 18 20 19 20 20 20 21 20 17 20 16 20 15 20 14 20 13 20 12 20 11 20 10 20 09 20 08 20 07 20 06 20 05 20 04 20 03 20 02 20 01 20 00 AFR SEAR EMR WPR AMR World 20 18 20 19 20 20 20 21 20 17 20 16 20 15 20 14 20 13 20 12 20 11 20 10 20 09 20 08 20 07 20 06 20 05 20 04 20 03 20 02 20 01 20 00 FIG. 3.8. Cumulative number of cases and deaths averted, globally and by WHO region, 2000–2021 Source: WHO estimates. 29 3 | Global trends in the burden of malaria 2 billion malaria cases and 11.7 million malaria deaths were averted globally in the period 2000–2021. Most of the cases (82%) and deaths (95%) averted were in the WHO African Region, followed by the South-East Asia Region (cases 10% and deaths averted 3%) (Fig. 3.8 and Fig. 3.9). In addition to malaria interventions, cases and deaths could also have been averted by other factors that modify malaria transmission or disease, such as improvements in socioeconomic status, malnutrition, infrastructure, housing and urbanization. Despite considerable disruptions to malaria services during the COVID-19 pandemic, it is estimated that 177 million cases and 949 000 deaths were averted in 2020, and a further 185 million cases and 997 000 deaths in 2021, compared with the estimated burden if case incidence and mortality rates had remained at the levels of 2000. b) AFR: WHO African Region; AMR: WHO Region of the Americas; EMR: WHO Eastern Mediterranean Region; EUR: WHO European Region; SEAR: WHO South-East Asia Region; WHO: World Health Organization; WPR: WHO Western Pacific Region. AFR, 94.9% SEAR, 2.6% EMR, 1.6% WPR, 0.8% AMR, 0.1% AFR, 82.1% SEAR, 10.2% EMR, 4.7% AMR, 1.0% WPR, 1.9% EUR, 0.0% FIG. 3.9. Percentage of a) cases and b) deaths averted, by WHO region, 2000–2021 Source: WHO estimates. a) 30 W O RL D M AL AR IA R EP O RT 2 02 2 3.9 BURDEN OF MALARIA IN PREGNANCY 1 Angola, Benin, Burkina Faso, Burundi, Cameroon, the Central African Republic, Chad, Côte d’Ivoire, the Democratic Republic of the Congo, Equatorial Guinea, Eritrea, Ethiopia, Gabon, the Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Liberia, Madagascar, Malawi, Mali, Mauritania, Mozambique, the Niger, Nigeria, the Republic of the Congo, Rwanda, Senegal, Sierra Leone, Somalia, South Sudan, the Sudan, Togo, Uganda, the United Republic of Tanzania, Zambia and Zimbabwe. Malaria infection during pregnancy has substantial risks for the pregnant woman, her fetus and the newborn child. For the pregnant woman, malaria infection can lead to severe disease and death, and placental sequestration of the parasite, which can lead to maternal anaemia; it also puts the mother at increased risk of death before and after childbirth, and is an important contributor to stillbirth and preterm birth. Placental infection can also lead to poor fetal growth and low birthweight, which in turn can lead to retardation of child growth and poor cognitive outcomes; it can also be a major risk factor in perinatal, neonatal and infant mortality (39-41). To avert the consequences to women and children of malaria infection, WHO recommends – in combination with vector control, and prompt diagnosis and effective treatment of malaria – the use of IPTp with SP as part of ANC (Section 7.4) in malaria endemic areas. The analysis in this section is restricted to moderate to high transmission countries in the WHO African Region, where the burden of malaria in pregnancy is most pronounced. The analysis of the burden of malaria in pregnancy accounts for the increased levels of malaria risk due to disruptions during the COVID-19 pandemic (Section 2.6, Section 3 and Section 7.4). 3.9.1 Prevalence of exposure to malaria infection during pregnancy The methods used to estimate country-specific malaria infection exposure during pregnancy (measured as cumulative prevalence over 40 weeks) are presented in Annex 1. In 2021, in 38 moderate to high transmission countries1 in the WHO African Region, there were an estimated FIG. 3.10. Estimated prevalence of exposure to malaria infection during pregnancy, overall and by subregion in 2021, in moderate to high transmission countries in the WHO African Region Sources: Imperial College and WHO estimates. 50 000 40 000 30 000 20 000 10 000 0 West Africa Total ■ Pregnancies without malaria infection ■ Pregnancies with malaria infection Nu m be r o f p re gn an ci es (0 00 ) Central Africa 2020 2021 2020 2021 2020 2021 2020 2021 East and southern Africa 4 756 3 059 5 074 3 357 14 210 2 801 9 298 6232 9 471 6 497 28 264 12 092 28 700 13 324 14 155 3 470 WHO: World Health Organization. 31 3 | Global trends in the burden of malaria 40 million pregnancies (Annex 1 and Fig. 7.5), of which 13.3 million (32%) were exposed to malaria infection (Fig. 3.10). This represented an additional 1.2 million women exposed to malaria in 2021 compared with 2020, as malaria risk in several countries increased in this period (Fig. 3.10). By WHO subregion, west Africa had the highest prevalence of exposure to malaria during pregnancy, where about 6.5 million (40.7%) of an estimated 16 million pregnant women had malaria infections. Of the 8.4 million pregnant women in central Africa in 2021, 3.4 million (39.8%) had malaria infection. At 20%, the prevalence of malaria infection in pregnant women was lower in east and southern Africa than in other subregions in 2021 (40% in central Africa and 41% in west Africa); however, the number of women infected was higher in east and southern Africa (3.5 million) than in central Africa (3.4 million). 3.9.2 Prevalence of low birthweight in neonates due to malaria infection during pregnancy There has been an increase in exposure to malaria in pregnancy in the WHO African Region, as outlined in Section 3.9.1. Given this increased exposure, without a pregnancy-specific intervention it is estimated that exposure to malaria infection would have resulted in 961 000 neonates with low birthweight compared with an estimated 505 000 neonates with low birthweight in the three subregions in 2020, with 48.5% of the children at risk being in the subregion of west Africa (Fig. 3.11). Low birthweight is a strong risk factor for neonatal and childhood mortality, and averting a substantial number of low birthweights will have a considerable impact on all-cause mortality in children. Of the 38 countries for which the prevalence of malaria infection during FIG. 3.11. Estimated number of LBWs due to exposure to malaria infection during pregnancy (without IPTp versus at estimated levels of IPTp coverage), overall and by subregion in 2021, in moderate to high transmission countries in sub-Saharan Africa Sources: Imperial College and WHO estimates. 1 000 000 800 000 600 000 400 000 200 000 0 East and southern AfricaCentral Africa West Africa Total Es tim at ed n um be r o f c hi ld re n w ith lo w b irt hw ei gh t 215 000 91 000 280 000 466 000 263 000 961 000 505 000 151 000 ■ Without IPTp ■ With estimated IPTp coverage IPTp: intermittent preventive treatment of malaria in pregnancy; LBW: low birthweight; WHO: World Health Organization. 32 W O RL D M AL AR IA R EP O RT 2 02 2 pregnancy was estimated, five countries (Eritrea, Ethiopia, Rwanda, Somalia and the Sudan) had no information on IPTp coverage. This was because the countries were not implementing the intervention, the intervention had been stopped in recent years or the intervention was in use in only a few areas within the country. In the 33 countries in which IPTp was implemented, an estimated 457 000 low birthweights were averted from the 961 000 neonates at risk of low birthweight in 2021, compared with an estimated 408 000 low birthweights averted in 2020. This increase was driven by a combination of increases in neonates at risk of low birthweight and increases in IPTp uptake in 2021. Accounting for the current impact of IPTp, there are still 505 000 low birthweights that need to be averted across the three subregions. If all of the pregnant women visiting ANC clinics at least once during pregnancy received a single dose of IPTp – assuming they were all eligible, and the levels of IPTp2 and IPTp3 coverage remained the same – an additional 55 000 low birthweights would be averted (Fig. 3.12), reducing the remaining residual low birthweight burden of malaria in pregnancy to 450 000. If IPTp3 coverage was raised to the same levels of ANC1 coverage, assuming that subsequent ANC visits were just as high, then an additional 162 000 low birthweights would be averted (Fig. 3.13), reducing the remaining residual low birthweight burden of malaria in pregnancy to 343 000. If IPTp3 coverage was optimized to 90% of all pregnant women, an additional 265 000 low birthweights would be averted (Fig. 3.14), reducing the remaining residual low birthweight burden of malaria in pregnancy to 240 000. FIG. 3.12. Estimated number of LBWs averted if current levels of IPTp coverage are maintained, and additional number averted if coverage of IPTp1 was optimized to match levels of coverage of ANC1 in 2021 while maintaining IPTp2 and IPTp3 at current levels, in moderate to high transmission countries in the WHO African Region Sources: Imperial College and WHO estimates. 750 000 600 000 450 000 300 000 150 000 0 West Africa Total ■ Additional low birthweights averted if IPTp1 matches ANC1 coverage ■ Low birthweights averted with current level of IPTp coverage Es tim at ed n um be r o f c hi ld re n w ith lo w b irt hw ei gh t Central Africa East and southern Africa 116 000 6 000 15 000 138 000 34 000 203 000 55 000 457 000 ANC: antenatal care; ANC1: first ANC visit; IPTp: intermittent preventive treatment of malaria in pregnancy; IPTp1: first dose of IPTp; IPTp2: second dose of IPTp; IPTp3: third dose of IPTp; LBW: low birthweight; WHO: World Health Organization. 33 3 | Global trends in the burden of malaria FIG. 3.13. Estimated number of LBWs averted if levels of IPTp3 coverage were optimized to match levels of coverage of ANC1 in 2021, in moderate to high transmission countries in the WHO African Region Sources: Imperial College and WHO estimates. 750 000 600 000 450 000 300 000 150 000 0 West Africa Total ■ Additional low birthweights averted if IPTp3 matches ANC1 coverage ■ Low birthweights averted with current level of IPTp coverage Es tim at ed n um be r o f c hi ld re n w ith lo w b irt hw ei gh t Central Africa East and southern Africa 116 000 28 000 45 000 138 000 89 000 203 000 162 000 457 000 ANC: antenatal care; ANC1: first ANC visit; IPTp: intermittent preventive treatment of malaria in pregnancy; IPTp3: third dose of IPTp; LBW: low birthweight; WHO: World Health Organization. FIG. 3.14. Estimated number of LBWs averted if levels of IPTp3 were optimized to achieve 90% coverage in 2021, in moderate to high transmission countries in the WHO African Region Sources: Imperial College and WHO estimates. 750 000 600 000 450 000 300 000 150 000 0 West Africa Total ■ Additional low birthweights averted if IPTp3 coverage was 90% ■ Low birthweights averted with current level of IPTp coverage Es tim at ed n um be r o f c hi ld re n w ith lo w b irt hw ei gh t Central Africa East and southern Africa 116 000 38 000 80 000 138 000 147 000 203 000 265 000 457 000 IPTp: intermittent preventive treatment of malaria in pregnancy; IPTp3: third dose of IPTp; LBW: low birthweight; WHO: World Health Organization. W O RL D M AL AR IA R EP O RT 2 02 2 34 This section presents the latest results on progress towards elimination as well as the status of the 11 highest burden countries globally. 4.1 NEARING ELIMINATION Progress towards malaria elimination is increasing as several countries near the goal of zero indigenous malaria cases. In 2021, there were 84 malaria endemic countries compared with 108 in 2000. The number of malaria endemic countries with fewer than 10 000 indigenous malaria cases increased from 27 in 2000 to 46 in 2021 (Fig. 4.1). During the same period, the number of countries that reported fewer than 100 indigenous cases increased from six to 27, and the number of countries that reported fewer than 10 indigenous cases increased from four to 25. Two additional countries – Bhutan and Saudi Arabia – reported fewer than 10 indigenous cases in 2021 compared with 2020. 4.2 MALARIA ELIMINATION CERTIFICATION Certification of malaria elimination is granted once a country has proven and shown evidence that local malaria transmission by Anopheles mosquitoes has been interrupted in the country, resulting in zero indigenous malaria cases for at least the past 3 consecutive years, and a programme for the prevention of re-establishment of transmission is in place (42). In 2017 and 2018, WHO established two malaria elimination advisory committees: the Malaria Elimination Oversight Committee (MEOC) and the Malaria Elimination Certification Panel (MECP), to help countries reach their elimination goals and advise WHO on whether a country could be certified malaria free based on WHO criteria. The preparation for certification starts when countries embark on elimination by achieving zero indigenous transmission and providing supporting documentation of the malaria programme. In 2022, the functions of the MEOC and MECP were merged into one committee and the Technical Advisory Group on Malaria Elimination and Certification (TAG- MEC) was established. Certification requires the elimination of the four main human parasite species: P. falciparum, P. vivax, P. ovale and P. malariae. Until now, certification has only been granted to countries where only the four species of Plasmodium were transmitted. However, there has been a notable rise in P. knowlesi infecting humans in some countries in the WHO South-East Asia Region, especially in Malaysia. Although there have been no indigenous malaria cases or deaths due to the four human malaria parasites in Malaysia for the past 4 years, since 2017 a total of 17 125 P. knowlesi cases and 48 deaths have been reported. In 2021 alone, 3575 cases were reported, resulting in 13 deaths. Over the same period, an additional 435 P. knowlesi cases were reported in the WHO South-East Asia Region, in Indonesia, the Philippines and Thailand. WHO has convened two technical consultations on P. knowlesi (43) and will continue working closely with affected countries to tackle this emerging challenge for malaria elimination. In addition, a careful analysis of currently available data and evidence was discussed by the MECP and subsequently by the Malaria Policy Advisory Group (MPAG) (43). Those groups concluded that for countries where the elimination of the four main human malaria species was achieved but human malaria cases of other Plasmodium species continue to occur, certification might be granted if the risk of human infection was reduced to the extent that it was negligible. 4 ELIMINATION AND THE HIGH BURDEN TO HIGH IMPACT APPROACH NMP: national malaria programme; WHO: World Health Organization. a Plasmodium knowlesi and introduced cases are not included. FIG. 4.1. Number of countries that were malaria endemic in 2000 and had fewer than 10, 100, 1000 and 10 000 indigenous malaria cases between 2000 and 2021a Sources: NMP reports and WHO estimates. 50 40 30 20 10 0 Nu m be r o f c ou nt rie s 2005 20102000 2015 2020 2021 ■ Fewer than 10 000 ■ Fewer than 1000 ■ Fewer than 100 ■ Fewer than 10 27 46 35 6 9 17 27 35 40 13 16 25 25 24 46 36 20 4 6 11 TABLE 4.1. Countries eliminating malaria since 2000 Countries are shown by the year that they attained 3 consecutive years of zero indigenous cases; countries that have been certified malaria free are shown in green (with the year of certification in parentheses). Sources: Country reports and WHO. 2000 Egypt United Arab Emirates (2007) 2001 2002 2003 2004 Kazakhstan 2005 2006 2007 Morocco (2010) Syrian Arab Republic Turkmenistan (2010) 2008 Armenia (2011) 2009 2010 2011 Iraq 2012 Georgia Türkiye 2013 Argentina (2019) Kyrgyzstan (2016) Oman Uzbekistan (2018) 2014 Paraguay (2018) 2015 Azerbaijan Sri Lanka (2016) 2016 Algeria (2019) 2017 Tajikistan 2018 2019 China (2021) El Salvador (2021) 2020 Islamic Republic of Iran Malaysia 2021 Cabo Verde Belize WHO: World Health Organization. Note: Maldives was certified in 2015; however, it was already malaria free before 2000, and thus is not listed here. 35 Based on evidence that indigenous transmission has been interrupted throughout the entire country, 25 countries that were malaria endemic in 2000 have achieved 3 consecutive years of zero indigenous malaria cases; 12 of these countries were certified malaria free by WHO between 2000 and 2021. In 2021, Belize and Cabo Verde reported zero indigenous cases and had achieved this for 3 consecutive years (Table 4.1). 36 W O RL D M AL AR IA R EP O RT 2 02 2 Altogether, five countries – Azerbaijan, Belize, Cabo Verde, Iran (Islamic Republic of) and Tajikistan – have submitted an official request for certification. In 2021, El Salvador became the first central American country to achieve malaria free status. This certification followed more than 50 years of political commitment by the government and the people of El Salvador (44). During the same year, China became the first country in the WHO Western Pacific Region to be certified malaria free in more than 30 years, following 70 years of investment in the fight against malaria (45). Algeria, which was certified malaria free in 2019, remains the only country in the WHO African Region to be certified malaria free since 1973 (46). 4.3 E-2025 INITIATIVE In 2021, the E-2025 initiative was launched, building on the foundation and success of the E-2020 initiative. Countries from the E-2020 cohort (i.e. countries identified as having the capacity to eliminate malaria by 2020) that had not yet requested malaria free certification were automatically nominated to participate in the E-2025 initiative. Eight additional countries – the Democratic People’s Republic of Korea, the Dominican Republic, Guatemala, Honduras, Panama, Sao Tome and Principe, Thailand and Vanuatu – and one territory, French Guiana, were added based on the following: having a set goal for malaria elimination by 2025 backed by a government- endorsed elimination plan; meeting a defined threshold of malaria case reductions in recent years; meeting the programme requirements; and expert opinions, including from WHO staff (47). In the period 2010–2021, the number of malaria cases in the 25 countries and one territory in the E-2025 decreased by 82.8%. Progress in the reduction of malaria cases by country since 2010 is shown in Table 4.2. In 2021, the E-2025 countries reported 33 064 indigenous cases, a 30.4% increase from 2020. Belize and Cabo Verde reported zero malaria cases for the third consecutive year, with Iran (Islamic Republic of) and Malaysia reporting zero indigenous cases for the fourth consecutive year. In 2021, Timor-Leste, following an outbreak in the previous year, reported zero indigenous malaria cases, and Saudi Arabia reported zero indigenous cases for the first time. Despite the negative impact of the COVID-19 pandemic, 61.5% of E-2025 countries reporting cases continued making progress towards elimination and reduction of the malaria burden. Between 2020 and 2021, case reductions were observed in the following countries: Bhutan (59.1%), Botswana (20.5%), the Dominican Republic (65.6%), Mexico (32.0%), Nepal (56.2%), the Republic of Korea (23.0%), Saudi Arabia (100%), South Africa (33.7%), Suriname (85.9%), Thailand (22.3%), Timor-Leste (100%) and Vanuatu (36.7%) (Table 4.2). Owing to COVID-19 disruptions, South Africa saw a drastic decrease in testing rates. Restrictions to movement because of lockdowns, which occurred during peak malaria transmission periods, resulted in mobile clinics being unable to carry out testing and case investigations at the community level. This, as well as limited human resources, also led to a notable increase over the past 3 years in unclassified cases, with the country reporting one third of its cases as unclassified in 2021. Nepal continues to report presumed cases, because of treatment of patients based on clinical symptoms despite diagnostic tests being negative, which remains a concern. The country is working to rectify this by updating treatment guidelines and training clinical staff. With the disruption of the COVID-19 pandemic and pressure on already fragile health systems, there has been a concerning increase in malaria cases in some countries. The following countries, when compared with 2020, have shown increases in malaria cases: the Comoros (56.9%), Costa Rica (52.4%), the Democratic People’s Republic of Korea (22.8%), Ecuador (11.1%), Eswatini (53.9%), French Guiana (2.1%), Guatemala (16.9%), Honduras (47.4%), Panama (55.3%) and Sao Tome and Principe (28.9%). Ecuador, for the second consecutive year, reported increases in the number of indigenous cases. Eswatini also saw an increase in local cases, most of which came from areas where malaria had not been reported for years. The Comoros made up 32.0% of all indigenous cases reported in the E-2025 countries, followed by Panama, contributing 13.2%, and Sao Tome and Principe and South Africa, both accounting for 8.0% of the total case burden (Table 4.2). Despite the setbacks, many countries continue to make impressive progress – between 2011 and 2021, the classification rates within the E-2025 countries increased from 7.6% to 90.8%, demonstrating continued efforts by countries in pursuit of their elimination goals. During the same period, there was also an increase in the number of countries that have made private sector malaria reporting mandatory. In 2011, only four countries reported that the private sector was mandated to report; this increased to 18 countries in 2021. TABLE 4.2. Number of indigenous malaria cases in E-2020 and E-2025 countries, 2010–2021 Source: NMP reports. Country/ area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 Belize 150 72 33 20 19 9 4 7 3 0 0 0 Bhutan 436 194 82 15 19 34 15 11 6 2 22 9 Botswana 1 046 432 193 456 1 346 284 659 1 847 534 169 884 703 Cabo Verde 47 7 1 22 26 7 49 423 2 0 0 0 Comoros 36 538 24 856 49 840 53 156 2 203 1 884 1 467 3 896 15 613 17 599 4 546 10 537 Costa Rica 110 10 6 0 0 0 4 12 70 95 90 189 Democratic People’s Republic of Koreaa 13 520 16 760 21 850 14 407 10 535 7 022 5 033 4 603 3 698 1 869 1 819 2 357 Dominican Republic 2 482 1 616 952 473 459 631 690 341 433 1 291 826 284 Ecuador 1 888 1 219 544 368 242 618 1 191 1 275 1 653 1 803 1 934 2 175 Eswatini 268 379 409 728 389 318 250 440 686 235 233 505 French Guiana 1 632 1 209 900 875 448 374 217 554 546 212 140 143 Guatemala 7 384 6 817 5 346 6 214 4 929 5 538 5 000 4 121 3 018 2 069 1 058 1 273 Honduras 9 745 7 618 6 439 5 364 3 378 3 555 4 094 1 273 632 330 815 1 550 Iran (Islamic Republic of) 1 847 1 632 756 480 358 167 81 57 0 0 0 0 Malaysia 5 194 3 954 3 662 1 028 596 242 266 85 0 0 0 0 Mexico 1 226 1 124 833 495 656 517 551 736 803 618 356 242 Nepal 3 894 2 335 3 230 1 974 832 591 507 623 493 127 73 32 Panama 418 354 844 696 864 546 769 649 684 1 756 1 946 4 354 Republic of Korea 1 267 505 394 383 557 627 602 436 501 485 356 274 Sao Tome and Principe 3 146 8 442 12 550 9 243 1 754 2 056 2 238 2 239 2 937 2 732 1 933 2 719 Saudi Arabia 29 69 82 34 30 83 272 177 61 38 83 0 South Africa 8 060 9 866 6 621 8 645 11 705 4 959 4 323 23 381 9 540 3 096 4 463 2 958 Suriname 1 771 795 569 525 401 81 78 137 37 104 156 22 Thailand 32 480 24 897 46 895 41 602 41 218 14 265 12 076 7 416 51 104 4 065 3 123 2 426 Timor-Leste 48 137 19 739 5 208 1 025 3 424 80 81 16 0 0 3 0 Vanuatu 9 817 6 179 4 532 2 883 1 314 571 2 243 1 227 632 567 493 312 Total 192 532 141 080 172 771 151 111 87 702 45 059 42 760 55 982 93 686 39 262 25 352 33 064 E-2020: malaria eliminating countries for 2020; E-2025: malaria eliminating countries for 2025; NMP: national malaria programme. Note: When cases in E-2025 countries were higher in 2021 than in 2020, they are shown in red (including countries reporting a continued increase from 2019). 37 4 | Elimination and the high burden to high impact approach GMS: Greater Mekong subregion; P. falciparum: Plasmodium falciparum; WHO: World Health Organization. Notes: Data for P. falciparum come from the GMS Malaria Elimination Database for Cambodia 2014, 2015 and 2017, Thailand 2012 and 2013, and Viet Nam 2018. For the following countries and years, species breakdown does not add up to the total confirmed cases and therefore the numbers of P. falciparum cases are likely to be underreported: Cambodia 2000–2006 and 2010–2013; Myanmar 2000–2006 and 2010–2011; Thailand 2005, 2006 and 2010–2011; and Viet Nam 2000 and 2007–2009. Before the implementation of case investigation and classification in countries, all confirmed cases are considered to be indigenous. This uncertainty should be noted in the interpretation of the trends. The year of implementation of these activities varies by country. For further details of species breakdown from 2010 onwards, see Annex 4-I. FIG. 4.2. Total indigenous malaria and P. falciparum indigenous cases in the GMS, 2000–2021 Source: WHO database. 500 600 400 300 200 100 0 700 Nu m be r o f m al ar ia c as es (0 00 ) ■ Total indigenous malaria cases ■ P. falciparum indigenous malaria cases 2017 2018 2019 2020 202120162015201420132012201120102009200820072006200520042003200220012000 38 W O RL D M AL AR IA R EP O RT 2 02 2 4.4 THE GREATER MEKONG SUBREGION Eliminating P. falciparum in the Greater Mekong subregion (GMS) is critical because of the repeated development of drug resistance in P. falciparum parasites. Although antimalarial drug resistance remains a concern, countries in the eastern GMS continue to progress towards the elimination of P. falciparum, with rapid decline in cases. Between 2000 and 2021, the GMS countries – Cambodia, China (Yunnan Province), the Lao People’s Democratic Republic, Myanmar, Thailand and Viet Nam – reported a 76.5% decrease in indigenous malaria cases and a 94.1% decline in indigenous P. falciparum malaria cases. China remains the only country within the subregion to consecutively report zero indigenous cases and be certified malaria free. In 2013 the WHO Emergency Response to Artemisinin Resistance in the GMS initiative was launched, following a peak of malaria cases in the region in 2012 (48); in 2017, this transitioned into the Mekong Malaria Elimination Programme (49). Between 2012 and 2021, the region saw a staggering decrease in indigenous malaria cases (86.4%) and in indigenous P. falciparum malaria cases (95.7%). FIG. 4.3. Total indigenous malaria and P. falciparum cases in the GMS, by country, 2012–2021 Countries are shown from the highest number of total indigenous malaria cases in 2021 to the lowest. Source: WHO database. GMS: Greater Mekong subregion; P. falciparum: Plasmodium falciparum; WHO: World Health Organization. 800 600 400 200 0 202020192018201720162015201420132012 2021 Nu m be r o f m al ar ia c as es (0 00 ) Myanmar 80 60 40 20 0 202020192018201720162015201420132012 2021 Nu m be r o f m al ar ia c as es (0 00 ) Cambodia 80 60 40 20 0 202020192018201720162015201420132012 2021 Nu m be r o f m al ar ia c as es (0 00 ) Thailand 80 60 40 20 0 202020192018201720162015201420132012 2021 Nu m be r o f m al ar ia c as es (0 00 ) Lao People’s Democratic Republic 80 60 40 20 0 202020192018201720162015201420132012 2021 Nu m be r o f m al ar ia c as es (0 00 ) Viet Nam 400 300 200 100 0 202020192018201720162015201420132012 2021 Nu m be r o f m al ar ia c as es China 0 0 0 0 0 0 0 0 0 0 0 ■ Total indigenous malaria cases ■ P. falciparum indigenous malaria cases 39 4 | Elimination and the high burden to high impact approach In 2021, a total of 90 082 indigenous cases and 16 484 indigenous P. falciparum cases were reported, a 17.3% increase in indigenous malaria cases and a 12.2% decline in indigenous P. falciparum cases compared with 2020. However, in recent years Myanmar has had some setbacks, with an increase in numbers of both P. falciparum and P. vivax cases because of the continued political instability in the area. In 2021, Myanmar continued to account for most of the indigenous malaria cases (87.7%) and indigenous P. falciparum malaria cases (80.9%) in the subregion, followed by the Lao People’s Democratic Republic (accounting for 4.3% of indigenous cases and 8.4% of indigenous P. falciparum cases) and Cambodia (accounting for 4.8% of indigenous cases and 5.7% of indigenous P. falciparum cases) (Fig. 4.2). As P. falciparum cases continue to decline, P. vivax has emerged as the dominant species within the subregion. In 2021, Cambodia reported relapses for the first time; the country reported a total of 1978 relapse cases, making up 48% of the country’s total indigenous cases. To further accelerate the gains made towards malaria elimination within the region, it is critical to administer primaquine for radical cure for P. vivax, in line with WHO guidelines (50). China’s malaria free status provides important lessons for the subregion. All GMS countries other than Myanmar have increasingly adopted subnational verification in low-burden settings, in preparation for malaria certification. 40 W O RL D M AL AR IA R EP O RT 2 02 2 4.5 PREVENTION OF RE-ESTABLISHMENT Once countries have eliminated malaria, they should sustain the minimum activities necessary to prevent re-establishment in areas with malariogenic potential (i.e. risk of importation in areas receptive to transmission). All countries that are malaria free should maintain vigilance to rapidly detect imported cases that might occur at any time and anywhere. Detection of introduced or indigenous cases should trigger a rapid response and a thorough investigation, to prevent further transmission. Given that malaria elimination is often first achieved in parts of the territories within a country, prevention of re-establishment should start at the subnational level while countries are working on full interruption of transmission in all their territories. Subnational verification is a process that is recommended to large countries or countries with a subnational elimination goal. It is a country-owned process and WHO does not verify malaria free status in subnational regions. Subnational verification can help to strengthen FIG. 4.4. Regional map of malaria incidence in the GMS, by area, 2012–2021 Source: NMP reports to GMS MEDB. GMS: Greater Mekong subregion; MEDB: Malaria Elimination Database; NMP: national malaria programme. Incidence per 1000 population ■ 0 ■ 0–0.1 ■ 0.1–1 ■ 1–5 ■ 5–10 ■ 10–20 ■ 20–50 ■ >50 ■ Not available 41 4 | Elimination and the high burden to high impact approach surveillance and response systems and the programme to prevent re-establishment; ultimately, it can prepare the country for national certification, although it is not a prerequisite for national certification (51). Countries in the GMS are leveraging the subnational verification exercise to strengthen their programme to prevent re-establishment and to prepare for their country’s certification by WHO. China is the first country that has conducted a nationwide subnational verification before being certified malaria free by WHO. The subnational verification process and its results assisted with the final certification. Transmission of malaria is considered re-established when at least three indigenous cases of malaria of the same species have been found in the same focus of transmission for 3 consecutive years. Between 2000 and 2020, no country that was certified malaria free was found to have malaria transmission re-established. 42 W O RL D M AL AR IA R EP O RT 2 02 2 4.6 THE HIGH BURDEN TO HIGH IMPACT APPROACH In November 2018, WHO and the RBM Partnership to End Malaria launched the HBHI country-led approach (52) as a mechanism to support the 11 highest burden countries to get back on track to achieve the GTS 2025 milestones (2). These 11 countries (Burkina Faso, Cameroon, the Democratic Republic of the Congo, Ghana, India, Mali, Mozambique, the Niger, Nigeria, Uganda and the United Republic of Tanzania) accounted for 70% of the global estimated case burden and 71% of global estimated deaths in 2017. Several countries with smaller populations but with high malaria incidence have also adopted the HBHI approach. This section presents the progress made in the burden of malaria and the consequences of disruptions during the COVID-19 pandemic in the HBHI countries. 4.6.1 Malaria burden in HBHI countries During the period 2000–2015, malaria cases in the HBHI countries reduced from 155 million to 150 million, and deaths from 641 000 to 398 000. However, by FIG. 4.5. Estimated malaria a) cases and b) deaths in HBHI countries, 2000–2021 Sources: WHO estimates. 2009200620032000 201820152012 2021 2009200620032000 201820152012 2021 2009200620032000 201820152012 2021 2009200620032000 201820152012 2021 2009200620032000 201820152012 2021 2009200620032000 201820152012 2021 2009200620032000 201820152012 2021 2009200620032000 201820152012 2021 2009200620032000 201820152012 2021 2009200620032000 201820152012 2021 2009200620032000 201820152012 2021 2009200620032000 201820152012 2021 Ca se s ( m illi on ) Ca se s ( m illi on ) Ca se s ( m illi on ) Ca se s ( m illi on ) Ca se s ( m illi on ) Ca se s ( m illi on ) 10 8 6 4 2 0 50 40 30 20 10 0 25 20 15 10 5 0 De at hs (0 00 ) De at hs (0 00 ) De at hs (0 00 ) De at hs (0 00 ) De at hs (0 00 ) De at hs (0 00 ) 60 45 30 15 0 120 90 60 30 0 60 45 30 15 0 10 8 6 4 2 0 10 8 6 4 2 0 60 45 30 15 0 60 45 30 15 0 60 45 30 15 0 10 8 6 4 2 0 Burkina Faso a) b) a) b) Democratic Republic of the Congo India Cameroon Ghana Mali 43 4 | Elimination and the high burden to high impact approach Ca se s ( m illi on ) Ca se s ( m illi on ) 15 12 9 6 3 0 De at hs (0 00 ) De at hs (0 00 ) 60 45 30 15 0 15 12 9 6 3 0 60 45 30 15 0 a) b) a) b) Mozambique Niger Ca se s ( m illi on ) Ca se s ( m illi on ) Ca se s ( m illi on ) 75 60 45 30 15 0 De at hs (0 00 ) De at hs (0 00 ) 300 225 150 75 0 Nigeria 15 12 9 6 3 0 60 45 30 15 0 Uganda De at hs (0 00 ) 15 12 9 6 3 0 60 45 30 15 0 United Republic of Tanzania 2009200620032000 201820152012 2021 2009200620032000 201820152012 2021 2009200620032000 201820152012 2021 2009200620032000 201820152012 2021 2009200620032000 201820152012 2021 2009200620032000 201820152012 2021 2009200620032000 201820152012 2021 2009200620032000 201820152012 2021 2009200620032000 201820152012 2021 2009200620032000 201820152012 2021 HBHI: high burden to high impact; WHO: World Health Organization. I II III IV 2020 there had been an increase to 165 million cases and 444 600 deaths, and by 2021 a further increase, to 168 million malaria cases, although deaths declined to 427 854 (Fig. 4.5). In 2021, with the ongoing increase in malaria cases, HBHI countries accounted for 68% of all cases and 70% of deaths globally. When looking at the case contribution within the HBHI countries, the following countries accounted for most of the malaria cases: Nigeria (39.0%), the Democratic Republic of the Congo (18.2%), Uganda (7.8%) and Mozambique (6.1%). Despite the rise in estimated global cases, global malaria deaths declined for the first time since 2018. Five of the 11 HBHI countries – the Democratic Republic of the Congo, Ghana, India, the Niger and the United Republic of Tanzania – also experienced a decline in deaths, although their contributions to the malaria burden within HBHI countries is still substantial. The following countries account for most malaria deaths within HBHI countries: Nigeria (45.3%), the Democratic Republic of the Congo (18.5%), the United Republic of Tanzania (5.9%) and Uganda (4.6%). Trends in malaria cases and deaths from 2000 to 2021 in HBHI countries are presented in Fig. 4.5. W O RL D M AL AR IA R EP O RT 2 02 2 44 Accelerating the elimination of malaria, and eventually interrupting its transmission, requires data and information from surveillance systems, to inform decisions on the optimal deployment and impact of interventions. Effective surveillance of malaria cases and deaths, and of key entomological and efficacy indicators, is essential for identifying which areas or population groups are at risk of malaria or are vulnerable to reducing the efficacy of interventions. In turn, this allows for effective programmatic planning, including response to epidemics and intensification of control when necessary. To systematically track national progress towards achieving Pillar 3 of the GTS (2), which is to transform malaria surveillance into a core intervention, WHO recommends the regular monitoring and evaluation of surveillance systems (53). This involves assessing the structure, core and support functions and the quality of the data across both passive and active case detection systems – information that is critical to the continuous improvement of surveillance systems. Such information is also useful for better understanding the data captured through these systems, which in turn makes it possible to make well-informed decisions about programmes. A key component of these assessments is a review of the quality of data, including case detection, recording and reporting. Cases and deaths detected through the surveillance system reported by countries are often from the public health sector, predominantly from passive case detection systems. In elimination settings, data on cases may also be recorded during active case detection. Often, data from the private sector remain sparse and, in countries with moderate to high transmission, it is possible that a substantial percentage of patients who do not seek care remain undocumented by the surveillance system. Hence, a strong surveillance system requires high levels of access to information on care and case detection, and complete reporting by all health sectors. WHO/GMP, in collaboration with partners, has developed several digital solutions and other types of tools to support the strengthening of country surveillance systems. This section provides a summary of these tools and presents country case studies on surveillance system assessment. 5.1 COUNTRY IMPLEMENTATION OF DIGITAL MALARIA MODULES Over 40 countries are using malaria surveillance packages designed for the District Health Information System 2 (DHIS2) (54), the world’s largest Health Management Information System (HMIS). The malaria surveillance packages support Pillar 3 of the GTS (2) to transform surveillance into a core intervention; they also help countries to implement the recommendations for surveillance strengthening outlined in the WHO guidance materials (53). Each package includes standardized metadata, defining data elements, indicators, visualizations and curriculum materials that have been developed to help countries to strengthen national routine surveillance. The packages are readily available for download and installation in countries that use DHIS2 as their HMIS (55). Since its initial release 4 years ago, 34 countries have adopted the DHIS2 malaria module that is designed for countries whose routine information system focuses on collecting aggregate data (Fig. 5.1). 5SURVEILLANCE 45 FIG. 5.1. Implementation status of routine malaria surveillance modules Source: WHO/GMP as of 20 October 2022. 0 20 00 40 00 10 00 km D H IS 2: D ist ric t H ea lth In fo rm at io n Sy st em 2 ; G M P: G lo ba l M al ar ia P ro gr am m e; W H O : W or ld H ea lth O rg an iz at io n. ■ W HO s ta nd ar d ag gr eg at e D HI S2 m od ul e ■ W HO s ta nd ar d ca se b as ed D HI S2 m od ul e ■ B ot h W HO s ta nd ar d m od ul es ■ A gg re ga te m od ul e (lo ca l v er sio n) ■ C as e ba se d m od ul e (lo ca l v er sio n) ■ B ot h ag gr eg at ed a nd c as e ba se d (lo ca l v er sio n) ■ N o da ta ■ M al ar ia fr ee ■ N ot a pp lic ab le 46 W O RL D M AL AR IA R EP O RT 2 02 2 The most recent version of this module includes additional malaria dashboards for routine monitoring of data quality, stock management and district-level surveillance. Standard modules specific for entomology and vector control have also been developed to support countries to improve the collection and use of entomological and vector control intervention data. The entomological and vector control modules are integrated with epidemiological modules and other malaria-related modules to form malaria repositories, which have been installed in a few countries. 5.1.1 Malaria case-based reporting and foci investigation module As more countries progress towards malaria elimination, a robust case-based surveillance system becomes essential, and it becomes necessary to shift from monthly aggregate reporting to timely individual case reporting. The aim of surveillance in low-burden settings is to rapidly detect and treat all malaria infections, investigate every malaria case and identify the likely source of an infection, to then appropriately direct public health action to prevent transmission. This calls for effective surveillance strategies and rapid response from NMPs. A suite of tools has been developed or enhanced to support malaria elimination surveillance using the DHIS2 web platform (54) and DHIS2 capture application (app) for Android (55). These tools can be used for case notification, investigation and response, and for focus investigation and response (Fig. 5.2). The various tools make it easy for health workers to enter data on new malaria cases, and to register and monitor foci. As the data are captured, investigative teams are notified – they can also use the module to record case data and investigation data during their surveillance activities. Simultaneously, the quality of the data can be validated and corrected, with duplicates being detected and removed as data are captured. Data can be visualized in graphs, charts, tables and maps, to guide an effective response. 1 Non-DHIS2 implementation. 2 Established a separate instance for entomology initially and is now integrating a link to the routine HMIS. Designed to be accessed through the free open- source DHIS2 platform (55), the case-based and foci investigation modules can be used on a smartphone, tablet or computer, both online and offline in the field. Data can be collected in real time, and historical data can be uploaded retrospectively. As with all WHO malaria modules, these new additions are fully customizable to each country’s context and can be integrated into existing DHIS2 implementation, saving time and minimizing maintenance costs. 5.1.2 Integrated malaria data repositories The malaria repository is a data warehouse for all malaria-related data; it is built on the DHIS2 routine aggregate module but includes other data that are not collected through the HMIS. It provides NMPs with the comprehensive data required for evidence-based strategic and operational planning. In collaboration with countries and partners, the implementation of the repository is undertaken in five main phases: ■ Phase 1 (planning phase): convene a national stakeholders meeting, establish a working group, develop a workplan and assign tasks to various stakeholders. Angola and the Niger are at this stage of the process. ■ Phase 2: establish a separate instance of the DHIS2, dedicated to non-routine and routine HMIS malaria-related data. ■ Phase 3: establish a live link with the national HMIS instance to extract routine malaria-related data. ■ Phase 4: install non-routine modules such as entomology, insecticide resistance, commodity distribution, climate and household surveys. Countries in this phase are Burkina Faso, Cameroon,1 Ghana,2 Guinea, Kenya, Uganda and the United Republic of Tanzania. ■ Phase 5: create a governance mechanism for the supervision of the development and maintenance of the repository as well as training at all levels. The steering committee members required to establish this process include staff from the NMP, ministries of health and supporting partners. Mozambique and Nigeria have established these structures. D H IS 2: D ist ric t H ea lth In fo rm at io n Sy st em 2 ; W H O : W or ld H ea lth O rg an iz at io n. 47 5 | Surveillance FIG. 5.2. Overview of WHO case-based malaria elimination surveillance modules: a) malaria case notification, investigation and response, and b) foci investigation and response workflow Source: Created using DHIS2 (54). D ia gn os is an d tre at m en t s ta ge C as e in ve st ig at io n an d cl as sifi ca tio n N ei gh bo ur in g ho us eh ol d in ve st ig at io n C as e ou tc om e Ro ut in e fo cu s in ve st ig at io n an d re sp on se Pa tie nt te st s po sit iv e fo r m al ar ia Pa tie nt e nr ol m en t ■ Id en tif yi ng in fo rm at io n ■ Pr eg na nc y st at us Fo cu s is id en tifi ed Fo cu s re gi st ra tio n N am e of fo cu s co or di na te s Fo cu s st at us , in ve st ig at io n an d cl as sifi ca tio n st ag e (R EP EA TA BL E) Fo cu s re sp on se (R EP EA TA BL E) Th es e th re e st ag es c ou ld h ap pe n in a d iff er en t o rd er o r i n pa ra lle l Li nk ed th ro ug h re la tio ns hi p a) b) In fo rm at io n ab ou t t he la te st re sp on se ev en t i s au to m at ic al ly a dd ed to th e in ve st ig at io n an d cl as sifi ca tio n st ag e TABLE 5.1. Contents of the malaria surveillance assessment toolkit Function Tools Description Define scope Assessment framework tool A set of key objectives, sub-objectives and indicators that can be used to quantify and qualify strengths and weaknesses in the surveillance system. This tool should be used as the starting point in an assessment to define the scope of the assessment and the approach. Concept note and protocol A template for the outline of a short concept note for refining the scope, methods, expected outputs and outcomes of an assessment, and a more detailed protocol outline for comprehensive assessments. Surveillance assessment planning tool A budgeting template to assist countries in developing a costed plan to undertake a comprehensive assessment. Collect and analyse data Desk review tool A set of questions, tables, graphs and diagrams used to collect information and summarize what is known about malaria surveillance through document and data review, and optional interviews with surveillance programme staff and other relevant supporting partners. Data quality assessment tools Tools and guidance for collecting and analysing data to specifically assess data quality at national, regional, district and service delivery levels. Question bank A library of questions that can be used to develop survey questionnaires for data collection at service delivery levels. Analysis tools A set of shell tables in Microsoft Excel® that are used to summarize the results of analysis from the survey. Develop and prioritize recommendations Technical brief and report outline A report template for organizing, visualizing and interpreting results from the assessment. A technical brief is used to highlight a subset of priority results, whereas the complete report includes all assessment results. 48 W O RL D M AL AR IA R EP O RT 2 02 2 5.2 MALARIA SURVEILLANCE ASSESSMENTS Surveillance systems need to be regularly assessed to identify key surveillance gaps and to evaluate the ability of the system to collect complete, timely and accurate data (53). The evaluation will enable understanding of the quality of the data generated by the system, how the data can be used to inform decision-making and what bottlenecks are likely to impede the efficiency and effectiveness of the system. The results of malaria surveillance assessments can be used to provide actionable and prioritized recommendations on how to strengthen surveillance systems; such recommendations can be used to develop national and subnational strategic and operational plans. In elimination settings, a surveillance assessment can help the country to prepare documentation and check the quality of data before certification. 5.2.1 Malaria surveillance assessment toolkit WHO, in collaboration with partners, has developed a malaria surveillance assessment toolkit (56). The toolkit comprises a set of standardized tools to assess the ability of the malaria surveillance system or integrated disease surveillance system to accurately capture malaria cases and deaths across all transmission settings (Table 5.1). The assessment framework comprises a minimum set of core indicators that are used to measure the performance of the surveillance TABLE 5.2. Results from the assessment of indicators for data quality and use (>80%=met [green], 50–80%=partially met [orange] and <50=not met [red]) Indicator Burkina Faso Democratic Republic of the Congo Ghana Completeness of reporting 91% 91% 98% Timeliness of reporting 85% 66% 94% Consistency between core variables 85% 68% 60% Completeness of core variables within registers 64% 74% 40% Concordance of core variables between registers and aggregated reports 32% 38% 30% Data used for strategic, policy and operational processes 83% 64% 72% Users with access to data 58% 96% 73% 49 5 | Surveillance system by assessing coverage, data quality and data use, and the determinants of the performance. The toolkit has been designed to enable countries to carry out comprehensive assessments, which include a desk review and a survey at all levels of the health system, or a shorter rapid assessment that includes only an assessment of priority indicators, primarily at the national level. Further details on the toolkit and a step-by-step guide on how to carry out a malaria surveillance assessment are shown in the implementation reference guide (57). 5.2.2 Country case studies: results from malaria surveillance assessments using the WHO toolkit To date, four countries have completed assessments using a pilot version of the WHO malaria surveillance assessment toolkit (56): Burkina Faso (2020), Cameroon (2021), the Democratic Republic of the Congo (2021) and Ghana (2021). The assessment had two main components: ■ a desk review of national documents and a data quality assessment on data extracted from HMIS or the national malaria surveillance system; and ■ a quantitative survey and a service delivery level data quality assessment on a randomly selected sample of health facilities across the country. Results from the assessment of indicators for data quality and use are presented for Burkina Faso, the Democratic Republic of the Congo and Ghana (Table 5.2). These data are not yet in the public domain, but the countries have agreed to the publication of summary information in this report. 50 W O RL D M AL AR IA R EP O RT 2 02 2 Burkina Faso Burkina Faso does not have an integrated national electronic information system that combines data on malaria cases, interventions, commodities and entomology. Instead, malaria data are collected and recorded in nine systems: the monthly routine HMIS (ENDOS DHIS2), the weekly Integrated Disease Surveillance and Response (IDSR), the Logistics Management Information System (LMIS), the Electronic Consultation Register, the mHealth community platform, the databases for each of the three specific interventions – LLINs, SMC and IRS – and the entomological surveillance database. The desk-level data quality assessment showed that, between 2016 and 2020, completeness and timeliness of reporting to ENDOS DHIS2 were both high, at 91% and 85%, respectively; it also showed that 85% of reports passed data consistency checks (Table 5.2). The data quality assessment carried out at service delivery level (which included 171 public and private health facilities across 13 regions), found that 64% of registers had core variables complete, but that concordance between registers and aggregate reports was low, at 32%. Reasons given by staff for the poor completeness of variables included the high workload of staff (37%), the lack of staff (35%) and data not being available in the register (32%). The quantitative survey revealed that 66% of the district surveillance agents reported problems with internet connection, which made it difficult to enter the data into the ENDOS platform. Community health workers also expressed difficulties in reporting data owing to the lack of transport to bring the reports back to the health facility. Most district surveillance officers (83%) organize quarterly data validation meetings and 90% of supervisory visits included data verification, but only 55% of the health facilities reported participating in these meetings. These findings may help to explain the low concordance between variables in the register and in ENDOS DHIS2. Although 83% of regions and districts reported using data for strategic, policy and operational processes, almost half of the health facilities and community workers were not using data to guide decision-making, despite 92% reporting that they routinely analyse their data. The ENDOS platform provides direct access to case surveillance data to users at central, regional and district levels, but not to health facility level. Overall, 62% of health facilities do not have access to the ENDOS national database. Priority recommendations to address current malaria surveillance system deficiencies include: ■ continuing to digitize data collection and reporting (supported by the availability of infrastructure and skilled human resources) to improve data collection and quality, and the timeliness of malaria reports; ■ integrating malaria data, including case surveillance, entomology and vector control, into a national data repository, and developing a dashboard to visualize and analyse the data for evidence-based decision- making; and ■ strengthening data validation, analysis and use by standardizing training and supervision, and institutionalizing data review meetings at all levels of the system. Democratic Republic of the Congo The Democratic Republic of the Congo uses a common DHIS2 platform managed by the HMIS division of the Ministry of Health to report malaria data at the point of service delivery. The HMIS DHIS2 includes data on monthly passive case surveillance, routine intervention data (LLINs and IPTp), the weekly IDSR, the LMIS and mass campaign data (LLINs). Entomological surveillance data are recorded and stored in a separate database. The desk-level data quality assessment showed that, between 2018 and 2020, completeness of reporting was high, at 91% (Table 5.2). Timeliness of reporting posed a challenge, with only 66% of reports being received within the expected time frame. About 68% of reports received passed all tests for consistency between core variables. This percentage was low owing to inconsistency between IPTp data variables; if these variables were removed, 91% of reports passed consistency checks between core malaria variables (e.g. RDT tested should not be less than RDT positive cases). The service delivery level data quality assessment was carried out in the provinces of Haut-Uele, Kinshasa, Kwilu and Tshopo at 239 health facilities for the period October to December 2020. Completeness of core variables within registers could be improved (74%) and concordance between registers and aggregate reports was low (38%). A quantitative survey was carried out in the same provinces and included health zone offices (n=80), health facilities – hospitals and reference centres (n=50), health centres and health posts (n=150) and private health facilities (n=50) – and community health workers (n=86). Results from this survey revealed that in the prior 3 months, 83% of all facility types held one to three data review meetings; nevertheless, 84% of facilities and 100% of health zones cited challenges with validating malaria data. Lack of financial motivation was the most common reason for this (cited by 48% of respondents at both levels), followed by time constraints as a result of insufficient staff, high workload and competing priorities. In addition, only 54% of interviewed health providers had received any kind of surveillance training. At the 51 5 | Surveillance health zone level, 94% of surveillance staff expressed difficulties with reporting owing to lack of equipment and services, lack of time and insufficient training. Only 10% of health facilities had monitoring and evaluation guidelines for malaria surveillance available, and only 30% of respondents were able to correctly define all three key malaria variables (suspected, presumed and confirmed malaria cases). These findings explain the poor concordance between source documents and national data as well as delayed reporting. Despite 91% of health facilities reporting that data were routinely analysed, only 64% of respondents said that they had used data for strategic, policy and operational processes. Data were mainly used to inform product distribution. Only 5% of facilities reported using some form of automated analysis tool; the others used manual calculations, primarily for preparing reports. A high proportion of health facilities (96%) had access to data, but this was mainly to paper copies of facility HMIS reports. The HMIS DHIS2 platform provides direct access to case surveillance data to users at central, provincial and health zone level, but not to users delivering malaria case management. Priority recommendations to address current malaria surveillance system deficiencies include: ■ ensuring availability of adequate resources such as equipment, guidelines and malaria supplies (e.g. RDTs and ACTs) to facilitate surveillance activities; ■ strengthening the integration of malaria surveillance data from different information systems, particularly entomological and survey data, into DHIS2 to allow for easy and adequate visualization of data; ■ strengthening the validation, access and use of data by strengthening the capacities of surveillance agents, the use of dashboards and the dissemination of data analysis reports such as the malaria epidemiological bulletins; and ■ improving data quality by applying validation rules at the data recording level, by setting up regular data review workshops at the different levels of the system and by conducting routine data quality audits. Ghana In Ghana, the primary HMIS for malaria epidemiological surveillance is the District Health Information Management System, an integrated disease surveillance platform developed in DHIS2. Malaria is reported into Ghana's DHIS2 by public sector facilities, including hospitals, clinics, health centres and community health post compounds, and by formal private sector facilities. The country has several different information systems for collecting data on interventions; however, these systems have not been integrated into DHIS2 and a data repository has not yet been established. Most health facilities (95.6%) still use paper forms for data recording and only 21.7% use the DHIS2 e-Tracker directly. The desk-level data quality assessment showed that, between 2018 and 2020, completeness and timeliness of reporting were high, at 98% and 94%, respectively (Table 5.2). Consistency between the core variables was 60% within reports received. The service delivery level data quality assessment was carried out at 203 health facilities, in 24 districts, across six regions (Ashanti, Central, Greater Accra, Oti, Upper West and Western North). There was poor completeness of core variables within reporting registers and poor concordance between data recorded in these registers and aggregate reports (≤40%). A quantitative survey carried out at the same facilities revealed that facilities cited staffing issues (64.5%), lack of availability of source documents (activity sheets, forms and registers) (31.5%), poorly designed and developed tools (21.2%), too high a workload (65.0%), not knowing how to fill out the form (19.7%) and data from community health post compounds not being received (5.4%) as reasons for missing data. Monthly data validation meetings occurred in half of the facilities surveyed but 67% of respondents indicated that their facility had never had an external data quality audit. Furthermore, 55% felt that they had not been adequately trained on malaria surveillance. More than 70% of users had used data for strategic, policy and operational processes, with nearly 100% of health facilities analysing malaria data and 88% doing so monthly. Most respondents (80%) had received training on data analysis and use, and 72% said that actions were triggered as a result of analysis of the surveillance system data. Some 73% of users had access to data, but only 51.2% could access DHIS2 directly and 32.5% relied on asking the district for malaria data. One major challenge was not having access to the internet (50%). Priority recommendations to address current malaria surveillance system deficiencies include: ■ developing a single malaria data repository that includes data validation rules and dashboards for all thematic areas; ■ ensuring that all care seeking points can report into the malaria data repository; ■ creating a comprehensive master health facility list for facilities providing malaria services, including private sector providers; ■ increasing data use at lower levels through improved access to dashboards, refresher trainings on data analysis and use, and improved standard operating procedures; and ■ improving frequency of data validation meetings and adding components for checking variable completeness. W O RL D M AL AR IA R EP O RT 2 02 2 52 In 2015, WHO launched the GTS (2). The 2021 GTS update (58) includes estimates for the funding required to achieve key milestones in 2025 and 2030. To reach over 80% coverage of currently available interventions, investment in malaria (both international and domestic) needs to increase substantially above the current annual spending (about US$ 3.1 billion per year for the past 5 years). Total annual resources needed were estimated at US$ 6.8 billion in 2020, increasing by US$ 0.5 billion per year to achieve the estimated US$ 9.3 billion required by 2025 and then by US$ 0.2 billion per year to reach US$ 10.3 billion by 2030 (58). Additionally, funding of US$ 8.5 billion is projected to be needed for R&D during the period 2021–2030, representing an average annual investment of US$ 851 million (58). In this section on investments towards malaria control, Section 6.1 presents the most up-to-date funding trends for malaria control and elimination, by source and channel of funding, for the period 2000–2021 or 2010–2021 (where data are available), both globally and for major country groupings, and Section 6.2 presents investments in malaria-related R&D for the period 2012–2021. 6.1 FUNDING TRENDS FOR MALARIA CONTROL AND ELIMINATION In 2021, 91 countries were included in the analysis of total funding for malaria control and elimination. This comprised 84 endemic countries and seven non- endemic countries, two of which have been declared malaria-free in the past 2 years. These seven non- endemic countries are included in this report because they contribute to important trends represented in this section, with some continuing to receive support for malaria. Throughout this section, these 91 countries are referred to as the “malaria endemic countries”. Total funding in 2021 was estimated at US$ 3.5 billion, a consistent increase from the US$ 3.3 billion in 2020 and US$ 3.0 billion in 2019. However, the amount invested in 2021 continues to fall short of the estimated US$ 7.3 billion required globally in 2021 to stay on track for the GTS milestones. Moreover, the funding gap between the amount invested and the resources needed has continued to widen significantly, particularly over the past 3 years, increasing from a gap of US$ 2.6 billion in 2019 to US$ 3.5 billion in 2020 and US$ 3.8 billion in 2021. Each year, the world malaria report describes funding that has been adjusted for inflation as well as potential changes to data in cases where countries or donors provide updates for previous years. Therefore, the figures presented in this chapter reflect 2021 constant dollar values and potential updates, which may vary from figures reported in previous years. The sources of funding for malaria control and elimination are summarized in Table 6.1. 6INVESTMENTS IN MALARIA PROGRAMMES AND RESEARCH TABLE 6.1. Sources of funding data International funding Domestic funding Multilateral Bilateral Donor contributions to the Global Fund 2010–2021 sourced from the Global Fund and WHO/GMP estimates Global Fund disbursements to malaria endemic countries 2003– 2021, sourced from the Global Fund Donor disbursements to multilateral funders 2011–2020 sourced from OECD members' total use of the multilateral system, and WHO/GMP estimates for 2010 and 2021 United Kingdom final aid spend 2017– 2020 from the Foreign, Commonwealth and Development Office and WHO/ GMP estimates for 2021 based on 2020 United Kingdom final aid spend data United States funding for malaria, by agency & recipient country sourced from the Kaiser Family Foundation Donor disbursement to malaria endemic countries from OECD CRS (2002–2021, except for United Kingdom 2007–2016) and WHO/GMP estimates for 2021 NMP reported domestic budget or expenditures when available, or estimates from WHO/GMP 2000–2021 Patient care delivery estimates, 2010–2021 from WHO/GMP CRS: creditor reporting system; Global Fund: Global Fund to Fight AIDS, Tuberculosis and Malaria; NMP: national malaria programme; OECD: Organisation for Economic Co-operation and Development; United Kingdom: United Kingdom of Great Britain and Northern Ireland; WHO/GMP: World Health Organization Global Malaria Programme. Note: The estimate of 2021 funding from the United Kingdom has been assumed to be the same as that of 2020 because 2021 data were not available at the time of publication of this report. 53 FIG. 6.1. Funding for malaria control and elimination, 2010–2021 (% of total funding), by source of funds (constant 2021 US$) Sources: ForeignAssistance.gov, Global Fund, NMP reports, OECD CRS database, United Kingdom Foreign, Commonwealth and Development Office, WHO estimates and World Bank DataBank. CRS: creditor reporting system; Global Fund: Global Fund to Fight AIDS, Tuberculosis and Malaria; NMP: national malaria programme; OECD: Organisation for Economic Co-operation and Development; United Kingdom: United Kingdom of Great Britain and Northern Ireland; WHO: World Health Organization. Note: The data sources, boundaries, accounting rules, and estimation methods used in this report are different from those of the System of Health Accounts 2011 (SHA2011). The malaria expenditure data reported here are thus not comparable with the disease expenditure data, including for malaria, that are reported in WHO's Global Health Expenditure Database. Governments of endemic countries 33% Other funders 3% The Netherlands 1% Norway 1% Australia 1% Sweden 1% European Commission 2% Canada 2% Bill & Melinda Gates Foundation 2% Japan 3% Germany 3% France 4% United Kingdom 8% United States of America 36% 54 W O RL D M AL AR IA R EP O RT 2 02 2 To assess the share of international funding by source of funds, malaria-related annual funding through multilateral agencies was estimated from donors’ contributions to the Global Fund for 2010–2021, and from contributions from the Organisation for Economic Co-operation and Development (OECD) creditor reporting system (CRS) for 2011–2020. Annual OECD CRS funding data for 2010 and 2021 were estimated using 2011 and 2020 reported estimates, respectively. In addition, contributions from malaria endemic countries to multilateral agencies were allocated to governments of endemic countries for the years 2010–2021. From 2010 through 2021, the share of international funding (67%) and domestic funding (33%) revealed an increasing trend in domestic funding compared with 2010 through 2020 (31%). In 2021, the share of international funding also corresponded to 67% of total funding, with governments of malaria endemic countries contributing 33% and domestic funding increasing slightly compared with 2020 (32%). The highest share of contributions since 2010 from international sources stemmed from the USA, the United Kingdom of Great Britain and Northern Ireland (United Kingdom), France, Germany and Japan, followed by other donors (Fig. 6.1). Fig. 6.2 shows the breakdown of total funding for each donor per year from 2010 through 2021. Most of the US$ 3.5 billion invested in 2021 (almost US$ 2.4 billion) came from international funders. The highest contribution stemmed from the government of the USA – over US$ 1.3 billion through planned bilateral funding and malaria-adjusted share of multilateral contributions. This was followed by bilateral and multilateral disbursements of about US$ 0.2 billion each from Germany and the United Kingdom, contributions of about US$ 0.1 billion from Canada, France and Japan, and a combined US$ 0.4 billion from other FIG. 6.2. Funding for malaria control and elimination, 2010–2021, by source of funds (constant 2021 US$) Sources: ForeignAssistance.gov, United Kingdom Foreign, Commonwealth and Development Office, Global Fund, NMP reports, OECD CRS database, the World Bank DataBank and WHO estimates. CRS: creditor reporting system; Global Fund: Global Fund to Fight AIDS, Tuberculosis and Malaria; NMP: national malaria programme; OECD: Organisation for Economic Co-operation and Development; United Kingdom: United Kingdom of Great Britain and Northern Ireland; WHO: World Health Organization. Note: The data sources, boundaries, accounting rules, and estimation methods used in this report are different from those of the System of Health Accounts 2011 (SHA2011). The malaria expenditure data reported here are thus not comparable with the disease expenditure data, including for malaria, that are reported in WHO’s Global Health Expenditure Database. 2.5 3.02.01.51.00.50 3.5 4.0 US$ (billion) ■ Governments of endemic countries ■ United States of America ■ United Kingdom ■ France ■ Germany ■ Japan ■ Canada ■ Bill & Melinda Gates Foundation ■ European Commission ■ Sweden ■ Australia ■ Norway ■ The Netherlands ■ Other funders 2017 2018 2019 2020 2021 2016 2015 2014 2013 2012 2011 2010 55 6 | Investments in malaria programmes and research countries that are members of the Development Assistance Committee (DAC) and from private sector contributors. Governments of malaria endemic countries contributed more than a third of total funding in 2021, with investments of over US$ 1.1 billion, of which over US$ 0.3 billion was spent on malaria case management in the public sector and US$ 0.8 billion on other malaria control activities, a US$ 0.1 billion increase from 2020. To analyse malaria investment since 2000, international bilateral funding data were obtained from several sources, although the availability of historical data varied, depending on the donor. From the USA, data on total annual planned funding from the US Centers for Disease Control and Prevention (US CDC), Department of Defense and United States Agency for International Development (USAID) are available from 2001 to 2021; planned country-level USAID data are available starting in 2006. Data on annual disbursements by the Global Fund to malaria endemic countries are available from 2003 to 2021. For the government of the United Kingdom, disbursement data were obtained through the OECD CRS on aid activity from 2007 to 2016; however, from 2017 to 2020, disbursement data were sourced from Statistics on international development: final UK aid spend 2020 (59) (2021 data were not yet finalized at the time of publishing this report, so disbursement data for 2021 were estimated using 2020 reported figures). The United Kingdom supports malaria control and elimination through a broad range of interventions that are not recorded as malaria-specific spend (e.g. support to overall health systems in malaria endemic countries and R&D), and are therefore not included in this estimate. FIG. 6.3. Funding for malaria control and elimination, 2000–2021, by channel (constant 2021 US$) Sources: ForeignAssistance.gov, Global Fund, NMP reports, OECD CRS database, United Kingdom Foreign, Commonwealth and Development Office, WHO estimates and World Bank DataBank. CRS: creditor reporting system; Global Fund: Global Fund to Fight AIDS, Tuberculosis and Malaria; NMP: national malaria programme; OECD: Organisation for Economic Co-operation and Development; United Kingdom: United Kingdom of Great Britain and Northern Ireland; USA: United States of America; WHO: World Health Organization. Note: The data sources, boundaries, accounting rules, and estimation methods used in this report are different from those of the System of Health Accounts 2011 (SHA2011). The malaria expenditure data reported here are thus not comparable with the disease expenditure data, including for malaria, that are reported in WHO’s Global Health Expenditure Database. 2018201720162015201420132012201120102009200820072006200520042003200220012000 2019 2020 2021 3.0 2.5 2.0 1.5 1.0 0.5 0 3.5 4.0 ■ Governments of endemic countries ■ Global Fund ■ USA bilateral ■ United Kingdom bilateral ■ World Bank ■ Other funders US $ (b illi on ) 56 W O RL D M AL AR IA R EP O RT 2 02 2 For all other donors, disbursement data were also obtained from the OECD CRS database for the period 2002–2020, with 2021 estimates being derived from 2020 figures. For all international bilateral funding data, the country recipient has been labelled as “unspecified” for all years where country-specific data are not available. For years where no data are available for a particular funder, no imputation was conducted; hence, the trends presented in Figs. 6.3–6.6 should be interpreted carefully, particularly for the years preceding 2010. Contributions from governments of endemic countries were estimated as the sum of contributions reported by NMPs for the relevant year plus the estimated costs of patient care delivery services at public health facilities. From 2000 to 2021, where available, government expenditures were used for their contributions (if such expenditures were unavailable, then government budgets or estimates were used). Patient care delivery costs were derived using unit cost estimates from WHO CHOosing Interventions that are Cost-Effective (WHO- CHOICE) (60). Where possible, patient care delivery costs per country were included for the years 2010 to 2021, because no unit cost estimates are available for the years before 2010. Of the US$ 3.5 billion invested in 2021, over US$ 1.1 billion (33%) was contributed from governments of endemic countries. Of the total investments, over US$ 1.5 billion (44%) was channelled through the Global Fund. Compared with previous years, the Global Fund’s disbursements to malaria endemic countries increased by about US$ 0.1 billion since 2020 and US$ 0.3 billion since 2019. Planned funding from the USA was FIG. 6.4. Funding for malaria control and elimination, 2000–2021, by World Bank 2021 income group and source of funding (constant 2021 US$) Sources: ForeignAssistance.gov, Global Fund, NMP reports, OECD CRS database, United Kingdom Foreign, Commonwealth and Development Office, WHO estimates and World Bank DataBank. US $ (b illi on ) ■ Domestica ■ International US $ (b illi on ) Low-income countries Lower-middle-income countries Upper-middle-income countries 20 20 20 15 20 10 20 05 20 00 20 21 20 20 20 15 20 10 20 05 20 00 20 21 20 20 20 15 20 10 20 05 20 00 20 21 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0 US $ (b illi on ) CRS: creditor reporting system; Global Fund: Global Fund to Fight AIDS, Tuberculosis and Malaria; NMP: national malaria programme; OECD: Organisation for Economic Co-operation and Development; United Kingdom: United Kingdom of Great Britain and Northern Ireland; WHO: World Health Organization. a Excludes out-of-pocket spending by households. Note: The data sources, boundaries, accounting rules, and estimation methods used in this report are different from those of the System of Health Accounts 2011 (SHA2011). The malaria expenditure data reported here are thus not comparable with the disease expenditure data, including for malaria, that are reported in WHO’s Global Health Expenditure Database. 57 6 | Investments in malaria programmes and research US$ 0.8 billion in 2021, essentially matching the 2017–2020 levels (Fig. 6.3). Fig. 6.4 shows the substantial variation across country income groups (as defined by the World Bank classifications published in July 2022) (61) in the share of funding received from domestic and international sources. The World Bank’s classifications by income group vary from year to year. In 2021, the low-income group category comprised 27 countries representing over 90% of global malaria cases and deaths. The low-income countries, which accounted for 47% of total malaria funding in 2021, experienced an overall increase in funding since 2000. These countries have received an increase in funding of over 50% since 2010, from both international and domestic sources. In this low-income group, 78% of the funding stemmed from international sources and 22% from domestic sources. The 39 low- and middle-income countries (LMIC) accounted for 41% of total funding in 2021, with international sources accounting for 61% of funding and domestic 39%. This figure continues to reflect an increase in domestic funding; in 2020, international sources accounted for 66% of funding and domestic 34%. In contrast, the upper-middle-income group, comprising 19 countries and accounting for 5% of total funding in 2021, received 4% of their malaria funding from international sources and 96% from domestic public funding (8% and 92% in 2020, respectively). Finally, the three high-income countries accounted for 1% of total malaria funding, with 100% stemming from domestic sources. Malaria funding to regions with no geographical information on recipients and one country that was not classified into an income group this year represent the remaining 6% of malaria funding in 2021. FIG. 6.5. Funding for malaria control and elimination per person at risk, 2010–2021, by WHO region (constant 2021 US$) Sources: ForeignAssistance.gov, United Kingdom Foreign, Commonwealth and Development Office, Global Fund, NMP reports, OECD CRS database, World Bank DataBank and WHO estimates. US $ (b illi on ) ■ Domestica ■ International ■ Total expenditure per capita US $ (b illi on ) AFR AMR 20 19 20 18 20 17 20 16 20 15 20 14 20 13 20 12 20 11 20 10 20 20 20 19 20 18 20 17 20 16 20 15 20 14 20 13 20 12 20 11 20 10 20 20 3.0 2.5 2.0 1.5 1.0 0.5 0 3.0 2.5 2.0 1.5 1.0 0.5 0 US $ (b illi on ) US $ (b illi on ) EMR SEAR WPR 20 19 20 18 20 17 20 16 20 15 20 14 20 13 20 12 20 11 20 10 20 20 20 19 20 18 20 17 20 16 20 15 20 14 20 13 20 12 20 11 20 10 20 20 20 19 20 18 20 17 20 16 20 15 20 14 20 13 20 12 20 11 20 10 20 20 20 21 20 21 20 21 20 21 20 21 1.2 1.0 0.8 0.6 0.4 0.2 0 0.6 0.5 0.4 0.3 0.2 0.1 0 0.6 0.5 0.4 0.3 0.2 0.1 0 US $ (b illi on ) AFR: WHO African Region; AMR: WHO Region of the Americas; CRS: creditor reporting system; EMR: WHO Eastern Mediterranean Region; Global Fund: Global Fund to Fight AIDS, Tuberculosis and Malaria; NMP: national malaria programme; OECD: Organisation for Economic Co-operation and Development; SEAR: WHO South-East Asia Region; United Kingdom: United Kingdom of Great Britain and Northern Ireland; WHO: World Health Organization; WPR: Western Pacific Region. a Excludes out-of-pocket spending by households. Note: The data sources, boundaries, accounting rules, and estimation methods used in this report are different from those of the System of Health Accounts 2011 (SHA2011). The malaria expenditure data reported here are thus not comparable with the disease expenditure data, including for malaria, that are reported in WHO’s Global Health Expenditure Database. 58 W O RL D M AL AR IA R EP O RT 2 02 2 The assessment of funding for malaria control and elimination per capita by domestic and international sources highlights the variation in funding per person at risk, on average, within each WHO region. The calculation for the population at risk is further explained in Annex 1. The five graphs in Fig. 6.5 depict the trends across the different regions in funding per person at risk. The WHO African Region has the highest funding per person at risk, with international expenditure per capita nearing US$ 2 and domestic expenditure per capita nearing US$ 1, having doubled from US$ 0.50 in 2010. The domestic funding trends should be interpreted with caution because the more cases a country has, the greater the estimated patient costs. However, the trends presented across the other WHO regions show sizeable changes in funding per person at risk over the past decade, with many regions either falling to levels seen in 2010 or decreasing significantly over time. The WHO African Region and the Eastern Mediterranean Region have experienced variations in funding per person at risk by domestic and international sources, with international funding being twice that of domestic funding in 2021. However, the WHO Eastern Mediterranean Region has seen overall funding per person at risk return to the same level in 2021 as it was in 2010, averaging US$ 0.50 per person. 59 6 | Investments in malaria programmes and research FIG. 6.6. Funding for malaria control and elimination, 2000–2021, by WHO region (constant 2021 US$) Sources: ForeignAssistance.gov, United Kingdom Foreign, Commonwealth and Development Office, Global Fund, NMP reports, OECD CRS database, World Bank DataBank and WHO estimates. AFR: WHO African Region; AMR: WHO Region of the Americas; CRS: creditor reporting system; EMR: WHO Eastern Mediterranean Region; Global Fund: Global Fund to Fight AIDS, Tuberculosis and Malaria; NMP: national malaria programme; OECD: Organisation for Economic Co-operation and Development; SEAR: WHO South-East Asia Region; United Kingdom: United Kingdom of Great Britain and Northern Ireland; WHO: World Health Organization; WPR: Western Pacific Region. a “Unspecified” refers to funding flows, with no information on the geographical localization of their recipients. Note: The data sources, boundaries, accounting rules, and estimation methods used in this report are different from those of the System of Health Accounts 2011 (SHA2011). The malaria expenditure data reported here are thus not comparable with the disease expenditure data, including for malaria, that are reported in WHO’s Global Health Expenditure Database. 2.5 3.0 2.0 1.5 1.0 0.5 0 3.5 4.0 US $ (b illi on ) ■ AFR ■ AMR ■ SEAR ■ EMR ■ WPR ■ Unspecifieda 2019 2020 20212017 20182016201520142013201220112001 2002 2003 2004 2005 2006 2007 2008 20092000 2010 In the WHO Region of the Americas, domestic funding in 2021 per person at risk was four times greater than international funding; however, the overall trend showed a nearly 50% decrease in total funding since 2010. This trend was more apparent in the WHO South- East Asia Region and Western Pacific Region, where funding per person at risk dropped to one third of the reported funding in 2010 – in both cases, from about US$ 0.30 to US$ 0.10 per person at risk, with domestic contributions being significantly higher than international contributions. In recent years, the WHO Western Pacific Region funding from domestic and international sources had converged and had been relatively stable since 2011; however, from 2020 to 2021 there was a 40% decline in total funding per person at risk. Most of the WHO regions, apart from the WHO African Region, have shown volatility in overall funding over the past decade and experienced lower total funding per person at risk in 2021 than in 2010. In the assessment of funding for malaria control and elimination by WHO region, more than three quarters (78%) of the US$ 3.5 billion invested in 2021 benefited the WHO African Region. Of the remaining funding, 5% each went to the WHO South-East Asia Region and Eastern Mediterranean Region, 4% to the Region of the Americas and 3% to the Western Pacific Region. The remaining 5% of total funding in 2021 was allocated to unspecified regions where no geographical information on recipients was available (Fig. 6.6). 60 W O RL D M AL AR IA R EP O RT 2 02 2 Many countries have experienced shocks in their real gross domestic product (GDP) because of the COVID-19 pandemic and other global crises, such as the increasing frequency of infectious diseases (e.g. monkeypox), climate events and conflict. This is reflected in the variation in real GDP across countries since the beginning of the pandemic. The UN predicted in 2021 that the global economy could shrink by about 0.9% during the pandemic (61–64). The 2022 World Economic Situation and Prospects (WESP) report by the UN Department of Economic and Social Affairs (65) stated that the global economy gained momentum in 2021, with an expansion of 5.5% (the highest growth rate in more than 4 decades), after a global contraction of 3.4% in 2020 (65). However, projections suggest that the economy will grow at a slower rate in the coming years. It is important to note that these are global outcomes and projections; therefore, they do not capture the differences across countries. In 2020, 70% of the malaria endemic countries categorized as lower income countries and LMIC experienced a shock in their annual real GDP (66). Of these countries, 34 shrunk by more than 1%, with half of these coming from the WHO African Region, as defined in the International Monetary Fund (IMF) data mapper on real GDP annual growth percentage (66). For 2021, the IMF data mapper and World Bank income classification categories show that, among the 65 countries defined as lower income countries and LMIC, only seven countries (11%) experienced a shock (66). Of those seven countries, five shrunk by more than 1%, with only one country experiencing a significant decline (of nearly 20%). Most of the 65 lower income countries and LMIC (as well as the upper-middle- income countries) experienced growth in their annual real GDP, ranging from 0.5% to 12.5% (and up to 19.9% for upper-middle-income countries) in 2021 (Fig. 6.7); however, a full recovery of GDP per capita since the pandemic will remain a challenge for many developing countries (65). FIG. 6.7. Real GDP growth annual per cent change, 2021, by World Bank income classification (constant 2021 US$) Source: IMF data mapper (66) and World Bank Data Bank. Rwanda Burkina Faso Ethiopia Democratic Republic of the Congo Gambia South Sudan Uganda Togo Liberia Guinea Guinea-Bissau Madagascar Sierra Leone Mali Eritrea Burundi Mozambique Malawi Somalia Niger Central African Republic Sudan Afghanistan Chad Yemen 10%8%6%4% Increase DJ Decrease Low-income countries 2%0%2%4% 12% GDP: gross domestic product; IMF: International Monetary Fund. 61 6 | Investments in malaria programmes and research Honduras Nicaragua El Salvador Tajikistan India Kenya Cabo Verde Benin Côte d’Ivoire Zimbabwe Senegal Bolivia (Plurinational State of) Philippines Pakistan Bangladesh United Republic of Tanzania Zambia Ghana Iran (Islamic Republic of) Djibouti Indonesia Nigeria Cameroon Eswatini Mauritania Nepal Viet Nam Comoros Cambodia Lao People’s Democratic Republic Timor-Leste Sao Tome and Principe Papua New Guinea Angola Vanuatu Solomon Islands Congo Haiti Bhutan Myanmar 5%0% Increase DJ Decrease Lower-middle-income countries 5%10%15%20% 10% 15% 62 W O RL D M AL AR IA R EP O RT 2 02 2 6.2 INVESTMENTS IN MALARIA-RELATED R&D 6.2.1 Overarching trends Global funding for malaria basic research and product development was US$ 626 million in 2021, a reduction of US$ 54 million (–7.9%) from 2020. This falls short of the estimated US$ 851 million projected to be required to stay on track towards the GTS milestones (2). This drop represented the third consecutive year of funding decline since its 2018 peak, taking headline investment to its lowest level since 2013 or, adjusting for long-term growth in survey participation, the lowest since 2007 when the G-FINDER survey began. The decrease was driven by large reductions in funding from industry (down US$ 18 million, –14%) and the United Kingdom's Foreign, Commonwealth and Development Office (FCDO) (down US$ 18 million, –51%); it fell most heavily on R&D targeting multiple and “other” malaria strains, which dropped by US$ 70 million to US$ 289 million (46% of total funding). In contrast, funding specifically targeting P. falciparum malaria rose slightly to US$ 282 million (45% of total funding) and funding for P. vivax malaria remained unchanged at US$ 55 million (8.8%). Malaria R&D funding declined across most product categories. Vaccine funding continued its downward trajectory for the fourth consecutive year, falling to US$ 118 million (down US$ 17 million, –13%), leaving it only slightly above its lowest recorded level. However, in a reversal of a trend seen over the past 2 years, this decrease was largely caused by a fall in funding for early-stage vaccine research, rather than clinical development. In particular, the Bill & Melinda Gates Foundation reduced its investment into early-stage research by almost three quarters (down US$ 10 million, –74%). Despite this, it remained the third-largest overall funder of malaria vaccine R&D, behind only the US National Institutes of Health (NIH) and industry. While continuing to receive more than a quarter of malaria funding (US$ 166 million), basic research appeared to experience the largest absolute decline in funding in 2021 (down US$ 22 million, –12%). However, this was largely due to the absence of 2021 funding data from the Indian Council of Medical Research, which had been the second largest funder in 2020, with contributions of more than US$ 16 million. Adjusting for differences in survey participation, malaria basic research still fell substantially – by US$ 12 million (–7.2%) – attributable mostly to reduced funding from the Australian National Health and Medical Research Council and tapered disbursements from Open Philanthropy, which had made frontloaded payments under its 2- and 3-year grants in 2020. After receiving a record 10% share of funding in 2020 (US$ 68 million), vector control product (VCP) R&D fell by just under a fifth to US$ 55 million in 2021. The Bill & Melinda Gates Foundation and the FCDO, which collectively accounted for 80% of VCP funding in 2020, saw their funding decrease by US$ 12 million (–27%) and US$ 3.6 million (–33%), respectively. However, this decrease was partially offset by a rise in funding from Open Philanthropy, which disbursed US$ 10 million to the University of California, Davis to progress a gene drive application designed to reduce or inhibit the ability of mosquitoes to transmit malaria. Diagnostics funding received only 2.5% of total malaria funding in 2021, its lowest share since 2013. Since its sustained peak at over US$ 30 million a year between 2017 and 2019, diagnostics funding has now declined by close to half, to US$ 16 million. The rate of decline slowed in 2021, thanks to a doubling of the Bill & Melinda Gates Foundation funding (up US$ 3.5 million, 110%) and a slight increase from the US NIH (up US$ 1.2 million, 21%). This was offset by a US$ 6.2 million fall in funding from the United Kingdom’s Department of Health and Social Care and FCDO, with the latter concluding 4 years of funding to PATH, amid substantial declines more broadly in its funding to a wide range of neglected diseases. Only R&D for therapeutics bucked the overall downward trend, with drug R&D rising by US$ 5.7 million (2.3%) and biologics by US$ 0.4 million (7.0%), leaving both with their largest share of overall malaria funding in the past decade. The increase in drug funding was mostly thanks to Unitaid, which more than doubled last year’s record contributions to reach a new peak of US$ 23 million (up US$ 13 million, 132%). In contrast to other product areas, industry remained the top funder of drug R&D, having contributed 38% (US$ 927 million) of all drug R&D funding over the past 10 years. However, its contributions fell slightly in 2021, to US$ 75 million (29% of total drug R&D funding). The largest decreases were in industry’s funding for clinical development, which trended downwards from a peak of US$ 85 million in 2018 to US$ 31 million in 2021. Despite a US$ 30 million drop in contributions from the public sector in high-income countries, they continued to provide 52% of global malaria R&D funding in 2021. This was largely thanks to the US NIH – the top funder of malaria R&D – whose funding remained stable at US$ 189 million. Philanthropic funding fell slightly, due to reduced funding from the Bill & Melinda Gates Foundation – still the second largest overall funder – followed by a US$ 18 million drop in aggregate industry funding, leaving it as the third largest. Record funding from Unitaid drove multilaterals’ share of funding to nearly 4%, its highest level ever, while – after adjusting for the absence of Indian funding data – LMIC public funding rose slightly. 63 6 | Investments in malaria programmes and research FIG. 6.8. Funding for malaria-related R&D, 2012–2021, by product type (constant 2021 US$) Sources: Policy Cures Research G-FINDER data portal (67). R&D: research and development. a “Unspecified” refers to funding flows, with no information on the product type. 600 500 400 300 200 100 0 700 800 ■ Drugs ■ Basic research ■ Vaccines ■ Vector control ■ Diagnostics ■ Biologics ■ Unspecifieda US $ (m illi on ) 2012 2018 2019 2020 202120172016201520142013 45 167 198 220 248 184 160 56 249 189 135 68 255 166 118 55 290 186 179 59 261 159 211 47 256 163 147 69 268 148 151 50 222 182 200 23 178 214 140 55 FIG. 6.9. Funding for malaria-related R&D, 2012–2021, by sector (constant 2021 US$) Sources: Policy Cures Research, G-FINDER data portal (67). HIC: high-income countries; LMIC: low- and middle-income countries; MNC: multinational corporation; R&D: research and development; SME: small and medium enterprise. 600 500 400 300 200 100 0 700 800 ■ Public (HIC) ■ Philanthropic ■ Private (MNCs) ■ Public (LMIC) ■ Public (multilaterals) ■ Private (SMEs) ■ Other US $ (m illi on ) 2018 2019 2020 2021201720162015201420132012 123 191 329 366 157 137 356 167 124 326 162 106 367 168 192 393 154 167 340 158 163 311 155 167 316 192 139 325 177 89 W O RL D M AL AR IA R EP O RT 2 02 2 64 This section summarizes data from manufacturers and NMPs on the distribution of malaria interventions. It also presents modelled estimates of access to and coverage of ITNs. 7.1 DISTRIBUTION AND COVERAGE OF ITNs 1 PBO is a synergist that disrupts the functions of the enzyme in the mosquito that makes them resistant to pyrethroids. Manufacturers delivered about 220 million ITNs to malaria endemic countries in 2021, 10 million fewer than in 2020 and 34 million fewer than in 2019 (Fig. 7.1). About 94% of all ITNs delivered by manufacturers went to countries in sub-Saharan Africa. Of these, 46% were pyrethroid–piperonyl butoxide (PBO)1 nets and 9% were dual active ingredient ITNs. About 56% of the ITNs delivered in sub-Saharan Africa were received in Nigeria (37 million), the Democratic Republic of the Congo (31.6 million), the United Republic of Tanzania (11.9 million), Kenya (11.8 million), Burkina Faso (11.5 million) and the Niger (10.0 million). Data from 2010–2021 are presented here; however, manufacturers’ delivery data show that, between 2004 and 2021, more than 2.5 billion ITNs were supplied globally, of which 2.2 billion (87%) were supplied to sub- Saharan Africa. In 2021, a total of 200 million ITNs were distributed globally by NMPs in malaria endemic countries. Of these ITNs, 176 million were distributed in sub- Saharan Africa. More than half of the total number of nets distributed in sub-Saharan Africa were distributed in five countries: the Democratic Republic of the Congo (22.6 million), Côte d’Ivoire (21.7 million), Kenya (17.9 million), Ghana (17.8 million) and Nigeria (16.4 million). Of the 25.1 million nets distributed outside sub-Saharan Africa, 90% were distributed in six countries: India (16.2 million), Yemen (1.8 million), Papua New Guinea (1.6 million), Viet Nam (1.5 million), Bangladesh (0.9 million) and Cambodia (0.6 million). Indicators of population-level coverage of ITNs were estimated for sub-Saharan African countries in which ITNs are the main method of vector control. Household surveys were used, together with manufacturer deliveries and NMP distributions, to estimate the following indicators: ■ ITN use (i.e. percentage of a given population group that slept under an ITN the night before the survey); ■ ITN ownership (i.e. percentage of households that owned at least one ITN); ■ percentage of households with at least one ITN for every two people; and ■ percentage of the population with access to an ITN within their household (i.e. percentage of the population that could be protected by an ITN, if each ITN in a household could be used by two people). 7DISTRIBUTION AND COVERAGE OF MALARIA PREVENTION, DIAGNOSIS AND TREATMENT 65 AMP: Alliance for Malaria Prevention; ITN: insecticide-treated mosquito net; NMP: national malaria programme; WHO: World Health Organization. a A lag between manufacturer deliveries to countries and NMP distributions of about 6–12 months is expected; thus, deliveries by manufacturers in a given year are often not reflected in distributions by NMPs in that year. Also, distributions of ITNs reported by NMPs do not always reflect all the nets that have been distributed to communities, depending on completeness of reporting. These issues should be considered when interpreting the relationship between manufacturer deliveries, NMP distributions and likely population coverage. Additional considerations include nets that are in storage in-country but have not yet been distributed by NMPs and those sold through the private sector that are not reported by programmes. FIG. 7.1. Number of ITNs delivered by manufacturers and distributeda by NMPs, 2010–2021 Sources: Milliner Global Associates and NMP reports to AMP and WHO. 300 250 200 150 100 50 0 2017 20182016 20172015 20162014 20152013 20142012 20132011 20122010 2011 2018 2019 2019 202120192020 2021 Nu m be r o f I TN s ( m illi on ) Manufacturer deliveries: ■ Sub-Saharan Africa ■ Outside sub-Saharan Africa NMP distributions:a ■ Sub-Saharan Africa ■ Outside sub-Saharan Africa 66 W O RL D M AL AR IA R EP O RT 2 02 2 By 2021, 68% of households in sub-Saharan Africa had at least one ITN, an increase from about 5% in 2000. The percentage of households owning at least one ITN for every two people increased from 1% in 2000 to 38% in 2021. In the same period, the percentage of the population with access to an ITN within their household increased from 3% to 54%. The percentage of the population sleeping under an ITN also increased considerably between 2000 and 2021 for the whole population (from 2% to 47%), for children aged under 5 years (from 3% to 53%) and for pregnant women (from 3% to 53%). Overall, access to and use of ITNs remains below the levels observed in 2017 (Fig. 7.2). Survey results on key ITN coverage indicators, by country, are shown in Annex 4-Ea. ITN: insecticide-treated mosquito net. FIG. 7.2. a) Indicators of population-level access to ITNs, sub-Saharan Africa, 2000–2021 and b) indicators of population-level use of ITNs, sub-Saharan Africa, 2000–2021 Sources: ITN coverage model by Malaria Atlas Project (68, 69). 60 40 20 0 20192018201720162015201420132012201120102009200820072006200520042003200220012000 2020 2021 80 Pe rc en ta ge ■ Households with at least one ITN ■ Households with one ITN for every two people ■ Population with access to an ITN ■ Population using an ITN ■ Children aged under 5 years using an ITN ■ Pregnant women sleeping under an ITN60 40 20 80 0 Pe rc en ta ge 20192018201720162015201420132012201120102009200820072006200520042003200220012000 2020 2021 a) b) 67 7 | Distribution and coverage of malaria prevention, diagnosis and treatment 7.2 POPULATION PROTECTED WITH IRS 1 The 53 countries that implemented IRS in 2021 are Angola, Benin, Bhutan, Botswana, Brazil, Burkina Faso, Burundi, Chad, Colombia, the Comoros, Costa Rica, Djibouti, the Dominican Republic, Ecuador, Eritrea, Eswatini, Ethiopia, the Gambia, Ghana, Guatemala, Honduras, India, Indonesia, Kenya, the Lao People’s Democratic Republic, Madagascar, Malawi, Mali, Mexico, Mozambique, Namibia, Nepal, Nicaragua, Pakistan, Panama, the Philippines, Rwanda, Sao Tome and Principe, Saudi Arabia, Senegal, Sierra Leone, Somalia, South Africa, South Sudan, the Sudan, Thailand, Timor-Leste, Uganda, the United Republic of Tanzania, Viet Nam, Yemen, Zambia and Zimbabwe. In 2021, 53 countries1 implemented IRS to prevent malaria. Globally, the percentage of the population at risk protected by IRS in countries that are currently malaria endemic continued to decline, from 5.5% in 2010 to 2.4% in 2021. The percentage of the population at risk protected by IRS has remained stable since 2016, with less than 6% of the population protected in each WHO region (Fig. 7.3). The number of people protected by IRS globally fell from 153 million in 2010 to 112 million in 2015, and further declined to 80 million in 2021. Between 2020 and 2021, the number of people protected by IRS decreased in India, Zambia and Chad, by 7.8 million, 1.7 million and 1.0 million, respectively. FIG. 7.3. Percentage of the population at risk protected by IRS, by WHO region, 2010–2021a Source: IVCC data and NMP reports. 0.5% 2.4% 1.0% 1.6% 5.3% 2.0% 2.2% 5.5% 3.9% 5.6% 10.7% ■ AFR ■ AMR ■ EMR ■ SEAR ■ WPR ■ World 12 10 8 6 4 2 0 Pe rc en ta ge o f p op ul at io n at ri sk 201820172016201520142013201220112010 2019 2020 2021 1.7% AFR: WHO African Region; AMR: WHO Region of the Americas; EMR: WHO Eastern Mediterranean Region; IRS: indoor residual spraying; IVCC: Innovative Vector Control Consortium; NMP: national malaria programme; SEAR: WHO South-East Asia Region; WHO: World Health Organization; WPR: WHO Western Pacific Region. a Among malaria endemic countries, 2021. 68 W O RL D M AL AR IA R EP O RT 2 02 2 7.3 SCALE-UP OF SMC SMC has been implemented in 15 countries in sub- Saharan Africa. In 2021, Uganda and Mozambique implemented SMC for the first time. The average number of children treated per cycle of SMC steadily increased from about 0.2 million in 2012 to almost 45 million in 2021 (Table 7.1). Between 2020 and 2021, there were an additional 11.6 million children treated with SMC per cycle. Nigeria contributed to 92% of this TABLE 7.1. Average number of children treated with SMC per cycle, by year, in countries implementing SMC, 2012–2021 Sources: LSHTM and MMV. Country 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 Benin 0 0 0 0 0 0 0 114 165 236 639 374 560 Burkina Faso 0 0 307 770 954 047 2 647 713 2 970 117 3 298 397 3 298 397 4 136 042 4 409 619 Cameroon 0 0 0 0 1 071 723 1 581 183 1 636 658 1 681 737 1 780 742 1 908 941 Chad 10 000 263 972 27 307 500 153 824 806 998 595 1 184 706 1 638 158 2 259 851 2 512 920 Gambia 0 0 65 271 76 450 73 710 76 601 112 841 110 870 121 834 76 045 Ghana 0 0 0 115 309 151 509 327 446 329 953 964 956 1 033 812 1 322 251 Guinea 0 0 0 201 283 442 177 575 927 840 120 841 090 1 088 194 1 122 434 Guinea-Bissaua 0 0 0 0 42 097 166 162 42 571 86 107 86 107 108 394 Mali 160 000 537 294 524 742 1 999 987 3 980 684 3 990 096 4 299 242 3 767 820 3 767 099 3 357 846 Mozambique 0 0 0 0 0 0 0 0 0 119 254 Niger 0 225 970 528 681 621 173 2 361 924 2 545 885 3 952 400 4 151 103 4 516 729 4 457 575 Nigeria 0 209 451 370 280 787 399 1 696 770 4 122 999 3 508 924 4 110 152 13 236 139 23 922 101 Senegal 0 55 709 595 745 614 581 621 503 631 897 0 879 652 687 959 748 116 Togo 0 119 222 170 165 0 411 811 420 451 434 161 453 907 486 716 475 997 Uganda 0 0 0 0 0 0 0 0 0 81 899 Total 170 000 1 411 618 2 589 960 5 870 382 14 326 425 18 407 359 19 639 973 22 098 113 33 437 862 44 997 950 TABLE 7.2. Number of treatment doses delivered, by year, in countries implementing SMC, 2014–2021 Sources: LSHTM and MMV. Country 2014 2015 2016 2017 2018 2019 2020 2021 Benin 0 0 0 0 0 456 661 856 491 1 498 240 Burkina Faso 1 231 081 3 816 187 10 590 851 11 799 603 13 193 588 13 193 588 16 544 168 18 603 883 Cameroon 0 0 4 286 893 6 324 731 6 546 632 6 726 947 7 122 967 7 635 762 Chad 109 226 1 850 623 3 299 222 3 658 347 4 738 823 5 967 618 9 039 406 10 142 392 Gambia 195 812 305 800 294 839 306 405 406 044 443 478 487 334 304 180 Ghana 0 461 236 606 037 1 309 782 1 319 813 3 859 822 4 135 249 4 803 223 Guinea 0 805 131 1 768 708 2 303 709 3 360 479 3 003 612 4 352 774 4 533 292 Guinea-Bissaua 0 0 146 718 664 647 170 284 344 429 344 429 433 574 Mali 2 098 969 7 999 948 15 398 687 15 960 382 17 113 604 15 068 821 14 956 952 12 906 775 Mozambique 0 0 0 0 0 0 0 477 016 Niger 2 072 438 2 484 692 7 977 379 10 183 541 15 243 535 16 604 412 18 066 916 17 830 299 Nigeria 1 481 118 3 149 597 6 316 916 9 298 163 13 842 931 16 440 609 52 944 556 96 002 997 Senegal 1 787 236 1 887 211 1 910 656 1 942 868 2 684 527 2 107 303 2 290 288 Togo 510 494 0 1 235 433 1 529 275 1 302 483 1 185 327 1 946 863 1 903 986 Uganda 0 0 0 0 0 0 0 409 495 Total 9 486 374 22 760 425 53 832 339 65 281 453 77 238 216 85 979 851 132 905 408 179 775 402 LSHTM: London School of Hygiene & Tropical Medicine; MMV: Medicines for Malaria Venture; SMC: seasonal malaria chemoprevention. a Values for 2020 were imputed from 2019. 69 7 | Distribution and coverage of malaria prevention, diagnosis and treatment increase, with 24 million children treated on average per cycle. In four countries, there were fewer children treated per SMC cycle in 2021 than in 2020: the Gambia (46 000), Mali (409 000), the Niger (59 000) and Togo (11 000). The total number of treatment doses delivered among countries implementing SMC in 2021 was about 180 million (Table 7.2). Subnational areas in each country where SMC was delivered in 2021, together with the number of cycles in each district, are shown in Fig. 7.4. LSHTM: London School of Hygiene & Tropical Medicine; SMC: seasonal malaria chemoprevention. Note: In one district in Mali where 4 cycles were planned, only 1 cycle was implemented, and in four districts where 3 cycles were planned, 2 cycles were implemented due to delays in receiving financial support. FIG. 7.4. Subnational areas where SMC was delivered, and number of treatment cycles per district, in implementing countries in sub-Saharan Africa, 2021 Source: LSHTM. SMC in 2021 Number of cycles ■ 1 ■ 2 ■ 3 ■ 4 ■ 5 ■ Not applicable 70 W O RL D M AL AR IA R EP O RT 2 02 2 7.4 COVERAGE OF IPTp USE BY DOSE 1 The 35 countries that implement IPTp nationally are Angola, Benin, Burkina Faso, Burundi, Cameroon, the Central African Republic, Chad, the Comoros, the Congo, Côte d’Ivoire, the Democratic Republic of the Congo, Equatorial Guinea, Gabon, the Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Liberia, To date, 35 African countries1 have adopted IPTp nationally to reduce the burden of malaria during pregnancy. Of these, 33 countries2 with moderate to high malaria transmission reported routine data from health facilities in the public sector on the number of women visiting antenatal clinics, and the number receiving the first, second, third and fourth doses of IPTp (i.e. IPTp1, IPTp2, IPTp3 and IPTp4). Using annual expected pregnancies as the denominator (adjusted for fetal loss and stillbirths) (Annex 1, Fig. 7.5), the percentage of IPTp use by dose was computed. ANC and IPTp coverages reported for 2021 were adjusted for disruptions in ANC services, as explained in Annex 1. Overall, the coverage of ANC and IPTp1 has decreased from nearly 82% and 65% in 2019, to 72% and 55% in 2021, respectively. Coverage of IPTp3 in 2021, at 35%, remained largely unchanged from 2019 and 2020, and well below the target of at least 80%. This underscores the substantial number of missed opportunities for IPTp, given that 55% of women received IPTp1 in 2021 (Fig. 7.5). Between 2020 and 2021, estimated IPTp3 coverage increased in Angola, Benin, Burkina Faso, Cameroon, the Congo, Ghana, Kenya, Liberia, Mali, Mozambique, the Niger, Senegal, Sierra Leone, Togo and Zambia. FIG. 7.5. Percentage of pregnant women attending an ANC clinic at least once and receiving IPTp, by number of SP doses, sub-Saharan Africa, 2010–2021 Sources: NMP reports, CDC estimates and WHO estimates. 100 80 60 40 20 0 Pe rc en ta ge o f p re gn an t w om en 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 ■ Attending ANC at least once ■ IPTp1 ■ IPTp2 ■ IPTp3 46% 74% 57% 30% 70% 42% 32% 45% 72% 55% 35% 1% ANC: antenatal care; CDC: United States Centers for Disease Control and Prevention; IPTp: intermittent preventive treatment of malaria in pregnancy; IPTp1: first dose of IPTp; IPTp2: second dose of IPTp; IPTp3: third dose of IPTp; NMP: national malaria programme; SP: sulfadoxine-pyrimethamine; WHO: World Health Organization. 71 7 | Distribution and coverage of malaria prevention, diagnosis and treatment 7.5 MALARIA DIAGNOSIS AND TREATMENT This section presents information on manufacturer sales and deliveries and national distribution of RDTs and ACTs, treatment seeking for fever in children aged under 5 years, and population-level coverage of malaria diagnosis and treatment with ACTs. RDT data reflect sales by manufacturers eligible for procurement (i.e. under the Malaria RDT Product Testing Programme) from 2010 to 2017 and for WHO prequalification since 2018, and NMP distributions of RDTs. Manufacturer data on ACTs have been provided by eligible companies for WHO-prequalified products. Globally, 3.5 billion RDTs for malaria were sold by manufacturers between 2010 and 2021, with more than 82% of sales being in sub-Saharan African countries. In the same period, NMPs distributed 2.4 billion RDTs, with 88% of these being in sub-Saharan Africa (Fig. 7.6). This difference may be due to the lack of reporting of RDTs that are yet to be distributed to health facilities or to inadequate reporting of RDT use from the private sector. In 2021, manufacturers reported about 413 million RDT sales. All 10 eligible manufacturers reported figures for 2021. NMPs distributed 262 million RDTs in 2021, about 39 million fewer than in 2020. 450 400 350 300 250 200 150 100 50 0 20172016201520142013201220112010 2018 2019 2020 2021 N um be r o f R D Ts (m ill io n) Manufacturer sales Sub-Saharan Africa: ■ P. falciparum only tests ■ Combination tests Outside sub-Saharan Africa: ■ P. falciparum only tests ■ Combination tests NMP distributionsb ■ Sub-Saharan Africa ■ Outside sub-Saharan Africa FIG. 7.6. Number of RDTs sold by manufacturers and distributed by NMPs for use in testing suspected malaria cases, 2010–2021a Sources: NMP reports and delivery data from eligible manufacturers. NMP: national malaria programme; P. falciparum: Plasmodium falciparum; RDT: rapid diagnostic test. a NMP distributions do not reflect those RDTs still in storage that have yet to be delivered to health facilities and to community health workers. Madagascar, Malawi, Mali, Mauritania, Mozambique, the Niger, Nigeria, Sao Tome and Principe, Senegal, Sierra Leone, South Sudan, Togo, Uganda, the United Republic of Tanzania, Zambia and Zimbabwe. 2 The Comoros and Sao Tome and Principe are not included owing to their low malaria burden. 72 W O RL D M AL AR IA R EP O RT 2 02 2 Almost 3.8 billion treatment courses of ACT were delivered globally by manufacturers between 2010 and 2021 (Fig. 7.7). About 2.6 billion of these deliveries were to the public sector in malaria endemic countries, and the rest were either public sector or private sector co-payments (or both), or were exclusively through the private retail sector. National data reported by NMPs show that, in the same period, 2.4 billion ACTs were delivered to health service providers to treat malaria patients in the public health sector. In 2021, some 225 million ACTs were delivered by manufacturers to the public health sector. NMPs distributed 242 million 1 The 20 countries were Angola (malaria indicator survey [MIS] 2011; demographic and health survey [DHS] 2015), Benin (DHS 2006; DHS 2017), Burkina Faso (DHS 2010; MIS 2017), Burundi (DHS 2010; DHS 2016), Cameroon (DHS 2011; DHS 2018), Ghana (DHS 2008; MIS 2019), Guinea (DHS 2005; MIS 2021), Kenya (DHS 2008; MIS 2020), Liberia (MIS 2011; DHS 2019), Madagascar (MIS 2011; DHS 2021), Malawi (DHS 2010; MIS 2017), Mali (DHS 2006; DHS 2018), ACTs in 2021, of which 97% were in sub-Saharan Africa, where about 113 million ACTs were distributed in four countries: Nigeria (40.3 million), Uganda (31.7 million), Zambia (21.4 million) and the Democratic Republic of the Congo (19.3 million). Aggregated data from household surveys conducted in sub-Saharan Africa between 2005 and 2021 were used to analyse coverage of treatment seeking, diagnosis and use of ACTs by children aged under 5 years (Table 7.3). Data were from 20 countries1,2, that undertook at least two surveys in this period (baseline, FIG. 7.7. Number of ACT treatment courses delivered by manufacturers and distributed by NMPs to patients, 2010- 2021a,b Sources: Companies eligible for procurement by WHO/UNICEF and NMP reports. 500 400 300 200 100 0 20172016201520142013201220112010 2018 2019 2020 2021 AC T tre at m en t c ou rs es (m illi on ) Manufacturer deliveries ■ Public sector ■ Private sector – AMFm/GF co-payment mechanisms ■ Public sector – AMFm/GF co-payment mechanisms ■ Private sector – outside AMFm/GF co-payment mechanisms NMP distributionsb ■ Sub-Saharan Africa ■ Outside sub-Saharan Africa ACT: artemisinin-based combination therapy; AMFm: Affordable Medicines Facility–malaria; GF: Global Fund to Fight AIDS, Tuberculosis and Malaria; NMP: national malaria programme; UNICEF: United Nations Children’s Fund; WHO: World Health Organization. a NMP deliveries to patients reflect consumption reported in the public health sector. b AMFm/GF indicates that the AMFm operated from 2010 to 2013, with the GF co-payment mechanism operating from 2014. Mozambique (DHS 2011; MIS 2018), Nigeria (MIS 2010; DHS 2018), Rwanda (DHS 2010; DHS 2019), Senegal (DHS 2010; DHS 2019), Sierra Leone (DHS 2008; DHS 2019), Uganda (DHS 2011; MIS 2018), the United Republic of Tanzania (DHS 2010; MIS 2017) and Zambia (DHS 2007; DHS 2018). 2 Although surveys were available from Zimbabwe, data were not included owing to low case numbers. In addition, Ethiopia could not be included here because the interim mini-survey conducted in 2019 did not include questions on care seeking behaviour or fever. 73 7 | Distribution and coverage of malaria prevention, diagnosis and treatment 2005–2011; and most recent, 2015–2021). Comparing the baseline and latest surveys, there was little change in the prevalence of fever within the 2 weeks preceding the survey (median 25% versus 20%) or in treatment seeking for fever (median 65% versus 67%). Comparing the source of treatment between the baseline and latest surveys, the proportion who received care from public health facilities increased from a median of 58% to 69%, and the proportion who received care from the private sector fell from a median of 40% to 28%. Use of community health workers was low in both periods, with medians of 2% and 1%, respectively. The proportion of children aged under 5 years who received a diagnosis with a finger or heel prick increased from a median of 30% at baseline to 57% in the latest surveys. Use of ACTs among those who sought care also increased, from a median of 14% at baseline to 24% in the latest surveys. In the most recent surveys, among those who received a finger or heel prick, 29% were treated with ACTs, compared with 21% at baseline. ACT use among children for whom care was sought and who were prescribed antimalarial medicine increased to 55% in the recent period, compared with 39% in the baseline period. TABLE 7.3. Summary of coverage of treatment seeking for fever, diagnosis and use of ACTs for children aged under 5 years, from household surveys in sub-Saharan Africa, at baseline (2005–2011) and most recently (2015–2021) Source: household surveys. Children aged under 5 years Baseline (2005–2011) Most recent surveys (2015–2021) Indicator Median estimate Lower bound Upper bound Median estimate Lower bound Upper bound Prevalence of fever With fever in past 2 weeks 25.0% 19.7% 34.4% 19.9% 16.1% 26.1% Treatment seeking for fever With fever in past 2 weeks for whom treatment was sought 64.8% 58.8% 72.2% 66.5% 57.2% 74.2% Source of treatment for fever among those who were treated Public sector (health facility) 58.0% 48.0% 78.5% 68.8% 46.3% 83.3% Public sector (community health worker) 2.0% 0.7% 3.4% 1.1% 0.3% 5.0% Private sector (formal and informal) 39.8% 21.4% 51.0% 27.5% 16.7% 55.0% Diagnosis among those with fever and for whom care was sought Received a finger or heel prick 29.8% 12.2% 38.4% 56.5% 37.8% 64.7% Use of ACTs among those for whom care was sought Received treatment with ACTs 14.3% 7.5% 32.2% 23.9% 11.1% 43.6% Use of ACTs among those for whom care was sought and received a finger or heel prick Received ACTs 20.6% 16.3% 41.7% 28.8% 18.8% 53.2% Use of ACTs among those for whom care was sought and were treated with an antimalarial Received ACTs 38.9% 23.6% 68.2% 55.1% 35.5% 88.5% ACT: artemisinin-based combination therapy. 74 W O RL D M AL AR IA R EP O RT 2 02 2 Data from the most recent household surveys, conducted between 2015 and 2021, were used to analyse coverage of treatment seeking, diagnosis and use of ACTs by children aged under 5 years, by country (Table 7.4). The percentage of children with fever for whom care was sought ranged from 45.4% in Madagascar to 86.9% in Uganda. The percentage of those with fever for whom care was sought and who received a diagnosis ranged from 17.4% in Nigeria to 83.8% in Burundi. Among those for whom care was sought, the percentage who received ACTs ranged from 1.6% in Senegal to 61.8% in Uganda. The proportion receiving ACTs among those for whom care was sought and who received a finger or heel prick ranged from 2.9% in Senegal to 64.9% in Uganda. The percentage of antimalarial medicines prescribed that were ACTs ranged from 12% in Burundi to about 97% in Malawi and Zambia. TABLE 7.4. Summary of coverage of treatment seeking for fever, diagnosis and use of ACTs for children aged under 5 years from the most recent household survey for countries in sub-Saharan Africa Source: household surveys. Country Latest survey Treatment seeking for fever Diagnosis among those with fever and for whom care was sought Use of ACTs among those for whom care was sought Use of ACTs among those for whom care was sought and who received a finger or heel prick Use of ACTs among those for whom care was sought and were treated with an antimalarial Median (lower bound– upper bound) Median (lower bound– upper bound) Median (lower bound– upper bound) Median (lower bound– upper bound) Median (lower bound– upper bound) Angola DHS 2015 55.1 (51.8–58.4) 58.2 (53.9–62.4) 20.8 (17.4–24.7) 27 (22.1–32.4) 75.7 (69.1–81.3) Benin DHS 2017 53.9 (50.7–57.0) 29.3 (26.4–32.4) 10.9 (9.1–12.9) 18.7 (14.7–23.4) 38.3 (32.8–44.0) Burkina Faso MIS 2017 73.8 (69.8–77.6) 64.7 (59.9–69.2) 54.0 (49.7–58.2) 60.1 (55.2–64.9) 79.9 (75.8–83.5) Burundi DHS 2016 69.7 (67.8–71.6) 83.8 (82.1–85.3) 7.6 (6.3–9.1) 8.3 (6.9–10.0) 11.6 (9.7–13.8) Cameroon DHS 2018 63.0 (58.7–67.1) 31.0 (26.6–35.9) 10.6 (8.3–13.5) 17.1 (11.5–24.7) 22.4 (17.6–28.2) Ghana MIS 2019 69.5 (64.9–73.8) 47.9 (42.8–53.0) 51.4 (45.9–56.9) 58.7 (51.4–65.6) 86.8 (81.2–90.9) Guinea MIS 2021 62.2 (57.3–66.9) 43.1 (38.0–48.3) 25.2 (21.3–29.6) 36.6 (29.6–44.2) 53.1 (46.5–59.6) Kenya MIS 2020 63.8 (57.6–69.7) 48.8 (41.1–56.6) – – – Liberia DHS 2019 81.4 (78.0–84.4) 58.0 (53.3–62.6) 26.1 (22.6–29.9) 30.5 (26.3–35.2) 43.1 (37.4–49.1) Madagascar DHS 2021 45.4 (41.8–49.1) 40.0 (35.8–44.4) 15.0 (12.0–18.6) 26.9 (20.9–33.8) 55.1 (46.9–63.0) Malawi MIS 2017 54.1 (49.0–59.2) 64.8 (58.2–70.8) 49.0 (42.4–55.7) 61.0 (53.7–67.9) 97.4 (94.2–98.9) Mali DHS 2018 57.8 (53.8–61.8) 24.2 (20.2–28.7) 11.2 (8.6–14.6) 24.4 (17.8–32.4) 32.8 (25.9–40.5) Mozambique MIS 2018 69.1 (63.5–74.2) 68.3 (63.2–73.1) 46.2 (40.4–52.0) 57.9 (50.2–65.2) 98.4 (96.6–99.3) Nigeria DHS 2018 73.8 (72.1–75.4) 17.4 (16.0–19.0) 27.3 (25.6–29.1) 42.0 (37.9–46.2) 51.3 (48.8–53.9) Rwanda DHS 2019 62.9 (60.0–65.7) 60.9 (57.2–64.6) 12.2 (9.5–15.7) 18.9 (14.8–23.8) 92.3 (84.7–96.3) Senegal DHS 2019 50.3 (45.1–55.5) 27.7 (22.5–33.7) 1.6 (0.6–4.0) 2.9 (1.1–7.3) 43.0 (18.9–70.8) Sierra Leone DHS 2019 75.5 (72.7–78.1) 71.9 (68.2–75.3) 22.5 (19.2–26.2) 22.9 (19.2–27) 31.7 (27.4–36.4) Uganda MIS 2018 86.9 (84.7–88.8) 58.0 (53.8–62.2) 61.8 (56.9–66.5) 64.9 (59.0–70.3) 87.9 (83.9–91.0) United Republic of Tanzania MIS 2017 75.4 (72.2–78.3) 55.0 (50.4–59.5) 36.8 (31.3–42.6) 43.7 (36.6–51.1) 89.0 (82.7–93.2) Zambia DHS 2018 77.2 (74.2–79.9) 76.9 (72.5–80.7) 42.7 (38.2–47.4) 51.7 (46.8–56.5) 96.9 (94.8–98.2) ACT: artemisinin-based combination therapy; DHS: demographic and health survey; MIS: malaria indicator survey. 75 W O RL D M AL AR IA R EP O RT 2 02 2 76 The GTS calls for a reduction in malaria case incidence and mortality rates (compared with a 2015 baseline) of at least 40% by 2020, 75% by 2025 and 90% by 2030 (2). The updated estimates for 2020 and 2021 include the effect of disruptions of malaria services during the pandemic. The malaria mortality estimates were based on a new method introduced in 2021 for quantifying the malaria CoD fraction (Annex 1, Table 3.1). Projections for beyond 2021 if current malaria trends are sustained were informed by the trends observed between 2012 and 2021. Trends in estimated malaria cases and deaths were used to make annual projections from 2022 to 2030, to track progress towards the future GTS milestones and targets for 2025 and 2030, as mandated to WHO by the World Health Assembly (2). The data and projections presented here have been adjusted for potential disruptions during the COVID-19 pandemic, which – despite commendable global and national efforts to maintain essential malaria services – led to higher malaria morbidity and mortality in 2020 and 2021 (Section 3.1). 8.1 GLOBAL PROGRESS In 2021, the GTS 2020 targets for morbidity and mortality, based on the 2015 baseline, were not achieved globally, despite the considerable progress made since 2000 (Fig. 8.1). The GTS and SDG 2025 and 2030 targets for malaria morbidity and mortality will also not be met unless urgent actions are taken (Fig. 8.1). A malaria case incidence of 59.2 cases per 1000 population at risk in 2021 (instead of the expected 31 cases per 1000) means that, globally, we are now off track by 48% (i.e. the GTS target is 48% lower than the current case incidence); at the current trajectory, by 2030 the world could be off track by 89% (Fig. 8.1a). Malaria deaths per 100 000 population at risk decreased from 15 in 2015 to 14.8 in 2021. Globally, the world is off track by 48% (Fig. 8.1b); if this trajectory continues, by 2030 it will be off track by 88%. Fig. 8.2 and Fig. 8.3 present progress in all countries considered to be malaria endemic in 2015. Countries were ranked into eight categories to assess progress towards malaria case incidence and the mortality rate milestone in 2020 from the 2015 baseline: ■ on track (zero malaria cases); ■ on track (decrease by 40% or more); ■ decrease by between 25% and less than 40%; ■ decrease by less than 25%; ■ no increase or decrease since 2015 (less than 5% increase or decrease in case incidence or mortality rate); ■ increase by less than 25%; ■ increase by between 25% and less than 40%; and ■ increase by 40% or more. Of the 93 countries that were malaria endemic (including the territory of French Guiana) in 2015, four countries have been certified malaria free since 2015; Algeria, China, El Salvador and Sri Lanka. Thirty-nine countries (42.0%) met the GTS morbidity milestone for 2021, having achieved a reduction of 40% or more in case incidence or reported zero malaria cases. One fifth (19 countries; 20.4%) had made progress in reducing malaria case incidence by less than the expected target. Twenty-seven countries (29.0%) had experienced increased case incidence, with 14 countries (15.0%) experiencing an increase of 40% or more in 2021 compared with 2015. In eight countries (8.6%), malaria case incidence in 2021 was similar to that of 2015. Forty-three countries (46.2%) that were malaria endemic in 2015 met the GTS mortality milestone for 8GLOBAL PROGRESS TOWARDS THE GTS MILESTONES GTS: Global technical strategy for malaria 2016–2030; WHO: World Health Organization; WMR: world malaria report. FIG. 8.1. Comparison of global progress in malaria a) case incidence and b) mortality rate, considering two scenarios: current trajectory maintained (blue) and GTS targets achieved (green) Source: WHO estimates. 80 70 60 50 40 30 20 10 0 M al ar ia c as es p er 10 00 p op ul at io n at ri sk 20292028202720262025202420232022202120202019201820172016201520142013201220112010 2030 a) ■ Current estimates of global case incidence (WMR 2022) ■ ■ GTS milestones (baseline of 2015) ■ ■ Forecasted trend if current trajectory is maintained 59.4 36.1 31.0 56.3 59.260.1 15.0 6.0 52.9 25 20 15 10 5 0 M al ar ia d ea th s p er 10 0 00 0 po pu la tio n at ri sk 20292028202720262025202420232022202120202019201820172016201520142013201220112010 2030 b) ■ Current estimates of global mortality rate (WMR 2022) ■ ■ GTS milestones (baseline of 2015) ■ ■ Forecasted trend if current trajectory is maintained 7.8 13.7 14.8 9.0 15.115.0 3.8 12.4 1.5 77 2021, with 28 of them reporting zero malaria deaths. An additional 22 countries (23.7%) achieved reductions in the mortality rate, but progress was below the 40% target. In nine countries, malaria mortality rates remained at the same level in 2021 as in 2015 (9.7%), whereas rates increased in 19 countries (20.4%), among which 11 countries had increases of 40% or more. FIG. 8.2. Map of malaria endemic countries (including the territory of French Guiana) showing progress towards the GTS 2020 malaria case incidence milestone of at least 40% reduction from a 2015 baseline Source: WHO estimates. ■ O n tr ac k (z er o m al ar ia c as es ) ■ O n tr ac k (d ec re as e by 4 0% o r m or e) ■ D ec re as e by b et w ee n 25 % an d <4 0% ■ D ec re as e by < 25 % ■ N o in cr ea se o r d ec re as e sin ce 2 01 5 ■ In cr ea se b y <2 5% ■ In cr ea se b y be tw ee n 25 % an d <4 0% ■ In cr ea se b y 40 % or m or e ■ C er tifi ed m al ar ia fr ee a fte r 2 01 5 ■ N on -e nd em ic p rio r t o 20 15 ■ N ot a pp lic ab le G TS : G lo ba l t ec hn ic al st ra te gy fo r m al ar ia 2 01 6– 20 30 ; W H O : W or ld H ea lth O rg an iz at io n. 78 W O RL D M AL AR IA R EP O RT 2 02 2 ■ O n tr ac k (z er o m al ar ia d ea th s) ■ O n tr ac k (d ec re as e by 4 0% o r m or e) ■ D ec re as e by b et w ee n 25 % an d <4 0% ■ D ec re as e by < 25 % ■ N o in cr ea se o r d ec re as e sin ce 2 01 5 ■ In cr ea se b y <2 5% ■ In cr ea se b y be tw ee n 25 % an d <4 0% ■ In cr ea se b y 40 % or m or e ■ C er tifi ed m al ar ia fr ee a fte r 2 01 5 ■ N on -e nd em ic p rio r t o 20 15 ■ N ot a pp lic ab le FIG. 8.3. Map of malaria endemic countries (including the territory of French Guiana) showing progress towards the GTS 2020 malaria mortality rate milestone of at least 40% reduction from a 2015 baseline Source: WHO estimates. G TS : G lo ba l t ec hn ic al st ra te gy fo r m al ar ia 2 01 6– 20 30 ; W H O : W or ld H ea lth O rg an iz at io n. 79 8 | Global progress towards the GTS milestones FIG. 8.4. Comparison of progress in malaria a) case incidence and b) mortality rate in the WHO African Region considering two scenarios: current trajectory maintained (blue) and GTS targets achieved (green) Source: WHO estimates. 300 250 200 150 100 50 0 M al ar ia c as es p er 10 00 p op ul at io n at ri sk 20292028202720262025202420232022202120202019201820172016201520142013201220112010 2030 ■ Current estimates of regional case incidence (WMR 2022) ■ ■ GTS milestones (baseline of 2015) ■ ■ Forecasted trend if current trajectory is maintained 233.6 146.1 125.5 212.3 229.4 243.5 60.9 192.7 24.3 a) 80 W O RL D M AL AR IA R EP O RT 2 02 2 8.2 WHO AFRICAN REGION Analysis of the trends by region shows that, in 2021, the WHO African Region was off track for both the malaria morbidity and mortality GTS milestones, by 45% and 47%, respectively (Fig. 8.4). In 2020, only Cabo Verde, Ethiopia, the Gambia, Ghana and Mauritania met the GTS 2020 target, and in 2021, in addition to those countries, Rwanda, South Africa and Zimbabwe were now on track to meet the GTS 2025 target, with at least a 40% reduction in malaria case incidence. Algeria has already been certified malaria free. Although not on track, 15 countries (Burkina Faso, Cameroon, Equatorial Guinea, Eswatini, Guinea, Kenya, Malawi, Mali, Mozambique, the Niger, Senegal, Sierra Leone, Togo, the United Republic of Tanzania and Zambia) achieved reductions in malaria case incidence by 2021 compared with 2015 (Fig. 8.2). There was no difference (<5% increase or decrease) in case incidence in 2021 compared with 2015 in seven countries (Benin, the Central African Republic, the Congo, Gabon, Liberia, Nigeria and South Sudan). Case incidence was higher in 2021 than in 2015 by less than 25% in Chad, Côte d’Ivoire, the Democratic Republic of the 100 90 80 70 60 50 40 30 20 10 0 M al ar ia d ea th s p er 10 0 00 0 po pu la tio n at ri sk 20292028202720262025202420232022202120202019201820172016201520142013201220112010 2030 ■ Current estimates of regional mortality rate (WMR 2022) ■ ■ GTS milestones (baseline of 2015) ■ ■ Forecasted trend if current trajectory is maintained 32.2 51.7 58.2 37.5 60.4 62.5 15.6 44.6 6.2 b) GTS: Global technical strategy for malaria 2016–2030; WHO: World Health Organization; WMR: world malaria report. 81 8 | Global progress towards the GTS milestones Congo, Guinea-Bissau, Sao Tome and Principe and Uganda; increased by 25–40% in Eritrea and Namibia; and increased by 40% or more in Angola, Botswana, Burundi, the Comoros and Madagascar. Cabo Verde and Eswatini had zero estimated malaria deaths in 2021 (Fig. 8.3), and Ethiopia, South Africa and Zimbabwe achieved a reduction in mortality rate of 40% or more. Although 20 countries did not meet the GTS 2021 mortality milestones (Benin, Burkina Faso, Cameroon, the Central African Republic, Chad, Côte d’Ivoire, Equatorial Guinea, Gabon, the Gambia, Ghana, Guinea, Malawi, Mali, Mauritania, Mozambique, the Niger, Sierra Leone, Togo, the United Republic of Tanzania and Zambia), these countries did achieve mortality rate reductions of less than 40%. Eight countries (Burundi, the Congo, Kenya, Liberia, Nigeria, Rwanda, Senegal and Uganda) showed no change (<5% decrease or increase) in mortality rate in 2021 compared with 2015, whereas increases in mortality rate of between 5% and 25% were seen in four countries (Angola, the Democratic Republic of the Congo, Guinea-Bissau and South Sudan); increases of between 25% and 40% were seen in Namibia; and increases of 40% or more were reported in Botswana, the Comoros, Eritrea, Madagascar and Sao Tome and Principe. 82 W O RL D M AL AR IA R EP O RT 2 02 2 8.3 WHO REGION OF THE AMERICAS In the WHO Region of the Americas, El Salvador was certified malaria free in 2021 and Belize reported zero malaria cases for the third consecutive year in 2021. The Dominican Republic, French Guiana, Guatemala, Honduras, Mexico, Peru and Suriname all had a reduction of at least 40% in case incidence in 2021 compared with 2015 (Fig. 8.5). Over the same period, Haiti was estimated to have reduced malaria case incidence by less than 40% and Brazil by less than 25%. In Colombia, the estimated increase in case incidence was less than 25%, and in the Bolivarian Republic of Venezuela and Guyana, estimated increases were between 25% and 40%. In Costa Rica, Ecuador, Nicaragua, Panama and the Plurinational State of Bolivia, case incidence increased by 40% or more in 2021 compared with 2015. Analysis of progress towards the GTS malaria case incidence milestones in the WHO Region of the Americas shows that the region is currently off track by 48%. On the current trajectory, by 2030 there would be an increase in case incidence of 20%. At the regional level, most of the worsening of the trend is attributable to the epidemic in the Bolivarian Republic of Venezuela. When the estimated cases from the Bolivarian Republic of Venezuela are excluded from the analysis the trend is reversed, resulting in a decline in case incidence of 28% (Fig. 8.5a). FIG. 8.5. Comparison of progress in malaria a) case incidence and b) mortality rate in the WHO Region of the Americas considering two scenarios: current trajectory maintained (blue) and GTS targets achieved (green) Source: WHO estimates. 14 12 10 8 6 4 2 0 M al ar ia c as es p er 10 00 p op ul at io n at ri sk 20292028202720262025202420232022202120202019201820172016201520142013201220112010 2030 ■ Current estimates of regional case incidence (WMR 2022) ■ ■ GTS milestones (baseline of 2015) ■ ■ Forecasted trend if current trajectory is maintained ■ ■ GTS milestones (baseline of 2015) — without Venezuela (Bolivarian Republic of) ■ ■ Forecasted trend if current trajectory is maintained — without Venezuela (Bolivarian Republic of) 2.1 2.6 4.6 4.2 4.6 2.9 1.8 4.3 3.5 2.5 1.1 0.9 5.1 2.1 0.4 0.3 a) 2.2 83 8 | Global progress towards the GTS milestones The number of cases in the Bolivarian Republic of Venezuela more than halved in 2020 and reduced further in 2021. Control measures must be strengthened in high-risk and migrant and mobile populations in the WHO Region of the Americas. To get the region back on track, the increasing trend in case incidence observed in several countries needs to be reversed. There were few malaria deaths in the WHO Region of the Americas (Fig. 8.5b), and changes in 2021 relative to the GTS 2015 baseline should be interpreted with caution. For example, although the mortality rate in Brazil and the Plurinational State of Bolivia increased by 40% or more (Fig. 8.3), it is estimated that the actual number of deaths would be 58 and 5, respectively. An estimated additional 183 deaths were from the Bolivarian Republic of Venezuela and Guyana, where there were increases of 5–25% in the mortality rate. Nevertheless, the region is currently off track for achieving all current and future GTS mortality rate milestones, with no change in trend projected between 2021 and 2030. 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 M al ar ia d ea th s p er 10 0 00 0 po pu la tio n at ri sk 20292028202720262025202420232022202120202019201820172016201520142013201220112010 2030 ■ Current estimates of regional mortality rate (WMR 2022) ■ ■ GTS milestones (baseline of 2015) ■ ■ Forecasted trend if current trajectory is maintained 0.2 0.2 0.1 0.3 0.2 0.3 0.1 0.2 0.0 b) GTS: Global technical strategy for malaria 2016–2030; WHO: World Health Organization; WMR: world malaria report. 84 W O RL D M AL AR IA R EP O RT 2 02 2 8.4 WHO EASTERN MEDITERRANEAN REGION Since 2015, there has been an increase in case incidence and mortality rates in the WHO Eastern Mediterranean Region, and these rates are now off track by 60% and 65%, respectively (Fig. 8.6). The Islamic Republic of Iran reported no indigenous malaria cases for the fourth consecutive year in 2021, and Saudi Arabia reported zero indigenous malaria cases for the first time. It is estimated that Afghanistan and Pakistan reduced case incidence by 40% or more in 2021 compared with 2015, although these estimates need to be verified through ongoing subnational analysis. Djibouti, the Sudan and Yemen were off track, with malaria case incidence higher by 40% or more. Case incidence also increased in Somalia but by less than 25% (Fig. 8.2). Malaria mortality rates decreased by 40% or more in Afghanistan and Pakistan in 2021 compared with 2015. There was no increase or decrease in Somalia, but deaths increased by 40% or more in Djibouti, the Sudan and Yemen. Zero malaria deaths have been reported in Saudi Arabia since 2000 and zero malaria deaths have been reported in the Islamic Republic of Iran since 2018. In 2021, Afghanistan and Pakistan both reported zero malaria deaths (see Section 3.4 for issues related to data uncertainty in this region). GTS: Global technical strategy for malaria 2016–2030; WHO: World Health Organization; WMR: world malaria report. FIG. 8.6. Comparison of progress in malaria a) case incidence and b) mortality rate in the WHO Eastern Mediterranean Region considering two scenarios: current trajectory maintained (blue) and GTS targets achieved (green) Source: WHO estimates. 14 12 10 8 6 4 2 0 M al ar ia c as es p er 10 00 p op ul at io n at ri sk 20292028202720262025202420232022202120202019201820172016201520142013201220112010 2030 ■ Current estimates of regional case incidence (WMR 2022) ■ ■ GTS milestones (baseline of 2015) ■ ■ Forecasted trend if current trajectory is maintained a) 11.2 5.4 4.7 12.7 11.6 9.0 2.3 13.5 0.9 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0 M al ar ia d ea th s p er 10 0 00 0 po pu la tio n at ri sk 20292028202720262025202420232022202120202019201820172016201520142013201220112010 2030 ■ Current estimates of regional mortality rate (WMR 2022) ■ ■ GTS milestones (baseline of 2015) ■ ■ Forecasted trend if current trajectory is maintained 0.9 2.92.5 1.0 2.4 1.7 0.4 2.7 0.2 b) 85 8 | Global progress towards the GTS milestones 8.5 WHO SOUTH-EAST ASIA REGION The WHO South-East Asia Region met the GTS 2020 milestones for both mortality and morbidity (Fig. 8.7) and remains on track to meet the GTS 2025 and 2030 targets. Sri Lanka was certified malaria free in 2016 and remains malaria free. Seven of the nine endemic countries in the region — Bangladesh, Bhutan, the Democratic People’s Republic of Korea, India, Nepal, Thailand and Timor-Leste — reduced malaria case incidence by more than 40% in 2021 compared with 2015, with Timor-Leste reporting zero malaria cases in 2021. Indonesia reduced malaria case incidence by less than 40%, and in Myanmar there was no increase or decrease over the same period. For the first time since 2012, Myanmar had an increase in case incidence (by 2.5%) in 2021 compared with 2020 (Fig. 8.2). Zero malaria deaths were reported in Bhutan, the Democratic People’s Republic of Korea, Thailand and Timor-Leste. All other countries in the region had reductions in mortality rate of 40% or more except for Indonesia, where the reduction was less than 25% (Fig. 8.3). GTS: Global technical strategy for malaria 2016–2030; WHO: World Health Organization; WMR: world malaria report. FIG. 8.7. Comparison of progress in malaria a) case incidence and b) mortality rate in the WHO South-East Asia Region considering two scenarios: current trajectory maintained (blue) and GTS targets achieved (green) Source: WHO estimates. 18 16 14 12 10 8 6 4 2 0 M al ar ia c as es p er 10 00 p op ul at io n at ri sk 20292028202720262025202420232022202120202019201820172016201520142013201220112010 2030 ■ Current estimates of regional case incidence (WMR 2022) ■ ■ GTS milestones (baseline of 2015) ■ ■ Forecasted trend if current trajectory is maintained a) 5.0 3.0 3.2 2.1 4.3 8.4 0.8 0.8 1.8 3.0 2.5 2.0 1.5 1.0 0.5 0 M al ar ia d ea th s p er 10 0 00 0 po pu la tio n at ri sk 20292028202720262025202420232022202120202019201820172016201520142013201220112010 2030 ■ Current estimates of regional mortality rate (WMR 2022) ■ ■ GTS milestones (baseline of 2015) ■ ■ Forecasted trend if current trajectory is maintained b) 0.5 0.4 0.8 0.5 0.9 1.5 0.3 0.2 0.1 86 W O RL D M AL AR IA R EP O RT 2 02 2 8.6 WHO WESTERN PACIFIC REGION The WHO Western Pacific Region did not achieve the GTS 2020 milestones for malaria morbidity or mortality, and in 2021 case incidence and the mortality rate were off target by 53% and 51%, respectively (Fig. 8.8). Between 2015 and 2021, case incidence increased by 10% and the mortality rate by 4%. At the current trajectory, the burden and mortality rate are predicted to reduce by 12% and 24%, respectively, by 2030 (Fig. 8.8b). The lack of reduction in malaria case incidence and mortality rates is mainly due to an increase of between 5% and 25% in cases and deaths in Papua New Guinea, which accounts for about 87% of the burden of malaria in the region. If cases from Papua New Guinea are excluded from the analysis, then it is estimated that by 2030 the GTS case incidence milestone will be off track by 52%, whereas it will be off track by 90% if cases from Papua New Guinea are included in the projections (Fig. 8.8a). Increases of 40% or more in case incidence FIG. 8.8. Comparison of progress in malaria a) case incidence and b) mortality rate in the WHO Western Pacific Region considering two scenarios: current trajectory maintained (blue) and GTS targets achieved (green) Source: WHO estimates. 4 3 2 1 0 M al ar ia c as es p er 10 00 p op ul at io n at ri sk 20292028202720262025202420232022202120202019201820172016201520142013201220112010 2030 ■ Current estimates of regional case incidence (WMR 2022) ■ ■ GTS milestones (baseline of 2015) ■ ■ Forecasted trend if current trajectory is maintained ■ ■ GTS milestones (baseline of 2015) — without Papua New Guinea ■ ■ Forecasted trend if current trajectory is maintained — without Papua New Guinea 1.9 2.2 1.0 0.3 1.8 0.9 0.3 0.2 1.7 0.5 0.4 0.2 1 1.6 0.2 0.1 0.0 a) 0.1 87 8 | Global progress towards the GTS milestones were seen in Solomon Islands, which also accounts for a large proportion of cases in the region. China was certified malaria free in 2021, and Malaysia reported zero malaria cases for the fourth consecutive year in 2020. Decreases in case incidence of 40% or more occurred in all other countries in the region, except in Vanuatu, where there was a reduction in case incidence by less than 40% in 2021 compared with the GTS 2015 baseline (Fig. 8.2). In 2015, Vanuatu was affected by a major cyclone that severely disrupted malaria diagnostic services and care seeking. As a result, it is likely that malaria cases in 2015 were underestimated. This confounds assessment of progress towards the GTS targets relative to a 2015 baseline for Vanuatu. Between 2016 and 2021, estimated case incidence declined by 87%. Cambodia, the Republic of Korea, Vanuatu and Viet Nam all reported zero malaria deaths. There were also zero deaths from human malaria in Malaysia, but 13 deaths occurred due to zoonotic P. knowlesi infection. All other countries in the region reported fewer than 10 deaths, apart from Papua New Guinea. 1.0 0.8 0.6 0.4 0.2 0 M al ar ia d ea th s p er 10 0 00 0 po pu la tio n at ri sk 20292028202720262025202420232022202120202019201820172016201520142013201220112010 2030 ■ Current estimates of regional mortality rate (WMR 2022) ■ ■ GTS milestones (baseline of 2015) ■ ■ Forecasted trend if current trajectory is maintained 0.3 0.3 0.2 0.4 0.2 0.3 0.1 0.3 0.0 b) GTS: Global technical strategy for malaria 2016–2030; WHO: World Health Organization; WMR: world malaria report. W O RL D M AL AR IA R EP O RT 2 02 2 88 9.1 DELETIONS IN P. FALCIPARUM HISTIDINE-RICH PROTEIN 2 AND PROTEIN 3 GENES Histidine-rich protein 2 (HRP2) is the predominant target of the 413 million P. falciparum-detecting malaria RDTs that are sold each year. Parasites that cannot express HRP2 may not be detectable by RDTs based on HRP2, and those that no longer express both HRP2 and histidine-rich protein 3 (HRP3) are completely undetectable by such RDTs. Deletions in the P. falciparum genes for HRP2 (Pfhrp2) and HRP3 (Pfhrp3) in clinical isolates were first reported in 2010 in the Peruvian Amazon basin, by researchers characterizing blood samples that were negative by HRP2-based RDTs but positive by microscopy (70). In recent years, Pfhrp2/3-deleted parasites have been documented outside of South America, including in Asia, the Middle East, and central, east, southern and west Africa. Prevalence estimates vary widely, both within and between countries. The examples of Djibouti, Eritrea and Peru (where the prevalence of dual Pfhrp2- and Pfhrp3-deleted parasites among symptomatic patients was as high as 80% in some areas) demonstrate that these parasites can become dominant in the population, posing a serious global threat to malaria patients and increasing the risk that missed cases will progress to severe disease or death (71). WHO has published guidance on investigating suspected Pfhrp2/3 deletions (72) and recommends that countries that have reports of Pfhrp2/3 deletions, and their neighbouring countries, should conduct representative baseline surveys among suspected malaria cases, to determine whether the prevalence of Pfhrp2/3 deletions causing false negative RDT results has reached a threshold that requires a change of RDT (>5% Pfhrp2 deletions causing false negative RDT results). Alternative RDT options (e.g. based on detection of Plasmodium lactate dehydrogenase [LDH]) are limited; in particular, there are currently no WHO-prequalified non-HRP2 combination tests that can detect and distinguish between P. falciparum and P. vivax. WHO is tracking published reports of Pfhrp2/3 deletions using the Malaria Threats Map application (69, 73) and is encouraging a harmonized approach to mapping and reporting of Pfhrp2/3 deletions through publicly available survey protocols. In 2022, several countries in the WHO African Region began planning or implementing representative surveys for Pfhrp2/3 deletions, financed mainly through Global Fund grants and groups funded by the Bill & Melinda Gates Foundation; results from these surveys are expected in 2022 and 2023. WHO has launched a dashboard for tracking surveillance activities globally to inform priorities and resource allocation and RDT forecasting, and to avoid duplication of efforts. The dashboard features key characteristics of surveillance activities as well as timelines. Based on literature searches informing the Malaria Threats Map, there were 17 new publications between September 2021 and September 2022. These 9BIOLOGICAL AND OTHER THREATS TO MALARIA INTERVENTIONS 89 publications included data from 17 countries – Benin, Brazil, Cameroon, the Democratic Republic of the Congo, Djibouti, Ecuador, Equatorial Guinea, Eritrea, Ethiopia, Gabon, Ghana, India, Kenya, Madagascar, Rwanda, Sierra Leone and the United Republic of Tanzania. Of these, studies in Equatorial Guinea, Kenya and Rwanda did not identify any Pfhrp2 deletions, although deletions in all three countries have been reported in previous publications. The studies from Benin, Cameroon, Gabon and Sierra Leone were the first Pfhrp2 papers published from these countries. Based on all data from publications included in the Malaria Threats Map, some form of investigation for Pfhrp2/3 deletions has been conducted in 47 countries and the presence of deletions has been confirmed in 40 countries (85%). The WHO Global Response Plan for Pfhrp2/3 deletions outlines several areas for action beyond the scaling up of surveillance. These other areas for action include identifying new biomarkers, improving the performance of non-HRP2 RDTs, undertaking market forecasting and strengthening laboratory networks to support the demand for using molecular characterization to determine the presence or absence of these gene deletions. 9.2 STATUS OF ANTIMALARIAL DRUG EFFICACY AND RESISTANCE (2015–2021) Effective treatment for malaria is a key component in the fight against this disease. The emergence of resistance to artemisinin and partner drugs is a significant risk to the global effort to reduce the malaria burden (2). Artemisinin partial resistance in P. falciparum is suspected or has been confirmed in countries in the WHO African Region, the GMS and South America, and in Papua New Guinea. Resistance to ACT partner drugs has prompted changes in first-line treatment of P. falciparum in countries in the GMS, the Horn of Africa and south Asia. As part of the response to counter the threat of antimalarial drug resistance, WHO calls on malaria endemic countries and global malaria partners to strengthen the surveillance of antimalarial drug efficacy and resistance, and to ensure that the most effective treatments are selected for national treatment policy. Antimalarial drug efficacy is monitored through therapeutic efficacy studies (TES), which track clinical and parasitological outcomes among patients receiving antimalarial treatment. Studies conducted according to the criteria established in the WHO protocol (74) help to detect changes in treatment efficacy over time. Polymerase chain reaction (PCR) correction is required to distinguish between cases with treatment failure caused by reinfection and those due to recrudescence. WHO recently updated the guidance on methodology to distinguish reinfection from recrudescence in areas of high malaria transmission (75). TES are considered the gold standard by which NMPs can best determine their national treatment policies. In countries where malaria transmission is low and in countries pursuing elimination, surveillance systems for case management have been strengthened so that all malaria cases are detected, treated and followed up to ensure cure. In this context, drug efficacy monitoring can be conducted by integrated drug efficacy surveillance (iDES). Four countries – Cambodia, the Lao People’s Democratic Republic, Thailand and Viet Nam – have adopted this approach to monitoring drug efficacy. Antimalarial drug resistance can be assessed using several tools. For some drugs, genetic changes associated with reduced drug sensitivity have been identified. For example, several P. falciparum Kelch13 (PfKelch13) mutations are associated with delayed clearance after a treatment containing artemisinin; thus, surveys of these mutations can provide information on the spread of artemisinin partial resistance (defined as delayed clearance). Resistance to SP (an ACT partner drug and chemoprevention drug) can be monitored by the detection of mutations in the dihydrofolate reductase (dhfr) and dihydropteroate synthase (dhps) genes of P. falciparum. Resistance to mefloquine is associated with an increase in Pfmdr1 copy numbers, whereas resistance to piperaquine is associated with an increase in Pfplasmepsin 2/3 copy numbers and mutations in the P. falciparum chloroquine resistance transporter (PfCRT) (76). Some mutations have only been validated as markers associated with resistance in parasite strains from specific regions. This section of the report summarizes the status of antimalarial drug efficacy and resistance in malaria endemic countries, focusing on studies that found high proportions of treatment failures. Key results are presented for each WHO region for TES for P. falciparum and P. vivax from 2015 to 2021. Treatment failure rates were calculated using the per protocol method unless otherwise indicated. A minimum sample size of 20 patients was applied to the analysis. Details of studies referenced here can be found in the Malaria Threats Map (73). 90 W O RL D M AL AR IA R EP O RT 2 02 2 WHO African Region In the WHO African Region, the first-line treatments for P. falciparum include artemether-lumefantrine (AL), artesunate-amodiaquine (AS-AQ), artesunate- pyronaridine (AS-PY) and dihydroartemisinin- piperaquine (DHA-PPQ). Most TES conducted according to the WHO standard protocol have demonstrated good efficacy (Fig. 9.1). Some studies in the WHO African Region have shown higher levels of treatment failure; these results warrant further investigation, but should be treated with caution because of significant deviations from the WHO standard protocol. Fig. 9.1 shows four studies reporting treatment failure rates greater than 10% after treatment with AL using the WHO-recommended methodology in the WHO African Region in 2015–2021: studies conducted at three sites in Burkina Faso (77) and one study conducted in Uganda (78). Two of the study sites in Burkina Faso (both reporting high failure rates with AL) reported treatment failure rates greater than 10% after treatment with DHA-PPQ (77); however, concern has been raised about the quality of microscopy in Burkina Faso (79). A further five studies with AL and two studies with DHA-PPQ reported treatment failure rates greater than 10% when using Bayesian algorithms to distinguish between reinfection and recrudescence: they included studies in Angola (80, 81), the Democratic Republic of the Congo (82) and Uganda (83). For the studies in the Democratic Republic of the Congo and Uganda, results using the WHO-recommended methodology were also available; this methodology resulted in very low estimated rates (≤2%) of treatment failure. FIG. 9.1. Number of P. falciparum TES finding more or less than 10% treatment failures in the WHO African Region, by ACT (2015–2021), among studies with at least 20 patients Number of studies 100500 150 AL AS-AQ AS-PY DHA-PPQ ■ <10% ■ ≥10% 132 97 30 2 1 4 ACT: artemisinin-based combination therapy; AL: artemether-lumefantrine; AQ: amodiaquine; AS: artesunate; DHA: dihydroartemisinin; PPQ: piperaquine; PY: pyronaridine; TES: therapeutic efficacy studies; WHO: World Health Organization. 91 9 | Biological and other threats to malaria interventions Surveillance of PfKelch13 polymorphisms associated with artemisinin partial resistance has been undertaken in several countries in the WHO African Region (73). There is now evidence of clonal expansion of PfKelch13 mutations in Eritrea, Rwanda and Uganda (Fig. 9.2) (84, 85). In Rwanda, the R561H mutation was first identified in 2014. Several studies undertaken in 2018 and 2019 found R561H in more than 15% of the samples (86). Further, the presence of the R561H mutation has been associated with delayed parasite clearance among patients treated with AL (86, 87). Rwanda was the first country in the WHO African Region to confirm the presence of artemisinin partial resistance. In Uganda, the validated markers of artemisinin partial resistance, C469Y and A675V, were also identified; in addition, in a recent survey of samples from 2018 and 2019, those two mutations were found in more than 15% of samples from three sites (85). Treatment failure rates in Rwanda and Uganda remained below 10% because the partner drug is still effective. Additionally, R622I, a validated marker of artemisinin partial resistance, has been found in an increasing proportion of samples in the Horn of Africa, particularly in Eritrea. WHO launched its Strategy to respond to antimalarial drug resistance in Africa (10) in 2022. P. vivax is endemic in only a few countries in the WHO African Region. The efficacy of chloroquine (CQ) and DHA-PPQ for the treatment of P. vivax was investigated in Ethiopia: all seven studies of CQ and one study of DHA-PPQ demonstrated treatment failure rates of less than 5%. FIG. 9.2. Countries in the WHO African Region with more than 5% of parasites sampled with PfKelch13 mutations and main mutations identified (2015–2020) ■ Wild type ■ Mutations Rwanda (n=425) Eritrea (n=769) Uganda (n=2872) Africa (n=18327) n=number of samples collected. 85% Mutations 11% 4% 14% 3%3% 3% 0.3%0.5% 0.5%0.6% 1.8% R561H Others R6221 Others A675V R5C469Y61H Others R561HR6221 C469Y A675V Others 15% 86% 14% 91% 9% 96% Mutations Mutations Mutations4% WHO: World Health Organization. 92 W O RL D M AL AR IA R EP O RT 2 02 2 WHO Region of the Americas The first-line treatments for P. falciparum in the WHO Region of the Americas include AL, artesunate- mefloquine (AS-MQ) and CQ. Limited data are available from the WHO Region of the Americas. TES undertaken using AL in Brazil (2015) and Colombia (2018) demonstrated high efficacy of this drug (88, 89). All malaria endemic countries in the WHO Region of the Americas recommend CQ as a first-line treatment for P. vivax. Efficacy of CQ was studied in Brazil and was found to be high. In Guyana, the C580Y mutation was sporadically observed between 2010 and 2017 (90); the mutation has not been found in any of the more recent samples, indicating that the mutation has probably disappeared. WHO South-East Asia Region The first-line treatments for P. falciparum in the WHO South-East Asia Region include AL, AS-MQ, AS-PY, AS plus SP (AS+SP) and DHA-PPQ. As can be seen in Fig. 9.3a, no TES reported more than 10% treatment failures. Treatment failures with AS+SP remained low in India; however, findings from a study in Chhattisgarh state between 2015 and 2017 looking at dhfr and dhps mutations (91) could be an early warning of changes that prompted a need for treatment policy change from AS+SP in north-eastern India. In Thailand, drug efficacy is assessed using iDES (92). In 2019, a disproportionately high treatment failure rate was detected in Sisaket province, with failure rates of up to 50%; this led the province to change its first-line treatment to AS-PY in 2020. In the GMS, PfKelch13 mutations associated with artemisinin partial resistance have reached a high prevalence. Among samples collected in Myanmar and western Thailand between 2015 and 2020, PfKelch13 wild-type parasites were found in 65.5% of samples (73). The first-line treatments for P. vivax are CQ, AL and DHA-PPQ. High treatment efficacy was found in all studies of CQ, DHA-PPQ and AS-PY. WHO Eastern Mediterranean Region The first-line treatments for P. falciparum in the WHO Eastern Mediterranean Region are AL and AS+SP. TES detected high treatment failure rates with AS+SP only in Somalia and the Sudan (93, 94) (Fig. 9.3b). These findings led to treatment policy changes to AL in both countries. The first-line treatments for P. vivax are AL and CQ. Data on the efficacy of first-line treatments are available from one study of AL in Somalia and one study of CQ in Afghanistan, with no treatment failures observed in either study. WHO Western Pacific Region The first-line treatments for P. falciparum in the WHO Western Pacific Region include AL, AS-MQ, AS-PY and DHA-PPQ. In this region, TES have found high failure rates in studies with AL, AS-AQ and DHA-PPQ (Fig. 9.3c). In the Lao People’s Democratic Republic, a high treatment failure rate was observed with AL in one study in Sekong province in 2017 (17.2%); however, the study was limited to 29 patients. AL was subsequently found to be effective in the Lao People’s Democratic Republic provinces of Champassak, Salavan and Savannakhet in 2019, with failure rates of up to 5%. The presence of AQ resistance was documented in Cambodia in 2016–2017, with high treatment failure rates with AS-AQ in the provinces of Mondulkiri (22.6%) and Pursat (13.8%) (94). High rates of treatment failure were detected with DHA-PPQ in Cambodia, the Lao People’s Democratic Republic and Viet Nam. In Cambodia, the findings prompted the replacement of DHA-PPQ with AS-MQ as the first-line treatment in 2016. In Viet Nam, AS-PY has replaced DHA-PPQ in provinces where high treatment failure rates were detected. PfKelch13 wild-type parasites were found in 29.9% of samples collected between 2015 and 2020 in Cambodia, the Lao People’s Democratic Republic and Viet Nam. In Papua New Guinea, the PfKelch13 C580Y mutation has emerged and appears to be spreading (95). The first-line treatments for P. vivax in the WHO Western Pacific Region are AL, AS-MQ and CQ. In Viet Nam in 2015, one study of CQ found treatment failure rates of 9.8%. 93 9 | Biological and other threats to malaria interventions FIG. 9.3. Number of P. falciparum TES finding more or less than 10% treatment failures, a) in the WHO South-East Asia Region, b) in the WHO Eastern Mediterranean Region, c) in the WHO Western Pacific Region, by ACT (2015–2021), among studies with at least 20 patients ACT: artemisinin-based combination therapy; AL: artemether-lumefantrine; AQ: amodiaquine; AS: artesunate; DHA: dihydroartemisinin; MQ: mefloquine; PPQ: piperaquine; PY: pyronaridine; SP: sulfadoxine-pyrimethamine; TES: therapeutic efficacy studies; WHO: World Health Organization. 30150 45 c) WHO Western Pacific Region AL AS-AQ AS-MQ AS-PY DHA-PPQ Number of studies ■ <10% ■ ≥10% 11 1 2 8 11 11 19 45 AL AS-MQ AS-PY AS+SP DHA-PPQ Number of studies 30150 a) WHO South-East Asia Region ■ <10% 36 1 14 12 4 45 Number of studies 30150 b) WHO Eastern Mediterranean Region AL AS+SP DHA-PPQ ■ <10% ■ ≥10% 18 5 2 11 94 W O RL D M AL AR IA R EP O RT 2 02 2 9.3 VECTOR RESISTANCE TO INSECTICIDES Of the 88 countries that reported insecticide resistance monitoring data to WHO between 2010 and 2020, 78 confirmed resistance to at least one insecticide in one malaria vector species from one mosquito collection site. Of these countries, 29 confirmed resistance to four insecticide classes – pyrethroids, organophosphates, carbamates and organochlorines – in at least one malaria vector species across different sites in the country (Fig. 9.4). Of these, 19 presented at least one site where resistance was confirmed for all these four classes in at least one local vector. Globally, resistance to pyrethroids was detected in at least one malaria vector in 87% of the countries and 68% of the sites, to organochlorines in 82% of the countries and 64% of the sites, to carbamates in 69% of the countries and 34% of the sites, and to organophosphates in 60% of the countries and 28% of the sites. Resistance to these four insecticide classes was confirmed in all WHO regions; however, its geographical extent varied considerably between regions (Fig. 9.5). Maps showing the status of resistance to different insecticides at each site are available in the Malaria Threats Map (73). Of the 38 countries that reported data on the intensity of pyrethroid resistance, high intensity resistance was detected in 27 countries and 293 sites, moderate to high intensity resistance in 34 countries and 406 sites, and moderate intensity resistance in 21 countries and 78 sites. High intensity resistance to pyrethroids has been detected in western Africa more frequently than in other regions. Between 2019 and 2020, WHO Member States reported the results of 835 bioassays conducted with chlorfenapyr and 603 with clothianidin. For chlorfenapyr, WHO requirements are more elaborate than for previous procedures for testing of mosquito resistance to other insecticides. Specifically, bottles FIG. 9.4. Reported insecticide resistance status as a proportion of sites for which monitoring was conducted, by WHO region, 2010–2020, for pyrethroids, organochlorines, carbamates and organophosphates Status was based on mosquito mortality where <90% = confirmed resistance, 90–97% = possible resistance, and ≥98% = susceptible. Where multiple insecticide classes or types, mosquito species or time points were tested at an individual site, the highest resistance status was considered. Numbers above bars indicate the total number of sites for which data were reported. Sources: reports from NMPs and national health institutes, their implementation partners, research institutions and scientific publications. WPRSEAREUREMR Organochlorines Carbamates Organophosphates AMRAFR WPRSEAREUREMRAMRAFR 80 60 40 20 100 0 80 60 40 20 100 0 Pe rc en ta ge o f m os qu ito c ol le ct io n sit es ■ Confirmed resistance ■ Possible resistance ■ Susceptible n=2060 n=168 n=368 n=50 n=551 n=327 n=1055 n=40 n=199 n=48 n=148 n=110 n=1373 n=40 n=187 n=48 n=48n=173 n=18 n=1254 n=88 n=175 n=169 n=77 Pyrethroids AFR: WHO African Region; AMR: WHO Region of the Americas; EMR: WHO Eastern Mediterranean Region; EUR: WHO European Region; n: number; NMP: national malaria programme; SEAR: WHO South-East Asia Region; WHO: World Health Organization; WPR: WHO Western Pacific Region. IRMMP: insecticide resistance monitoring and management plan; NMP: national malaria programme. FIG. 9.5. Number of classes to which resistance was confirmed in at least one malaria vector in at least one monitoring site, 2010–2020 Sources: Reports from NMPs and national health institutes, their implementation partners, research institutions and scientific publications. Status of IRMMPs Completed In progress Not started Insecticide class number Four insecticide classes Three insecticide classes Two insecticide classes One insecticide class No resistance detected to any classes No data reported Not malaria endemic Not applicable 95 9 | Biological and other threats to malaria interventions need to be coated with 1 mL of chlorfenapyr-acetone mixture at the discriminating dose 24 hours before the test; tests need to be conducted strictly within a temperature range of 27 ±2 °C and a humidity range of 80% ±10%; mosquito mortality has to be measured 72 hours after exposure in bottles; and a susceptible colony has to be tested in parallel to the wild mosquitoes. Resistance can only be confirmed when mortality in the exposed wild vector population 72 hours after exposure is less than 90% and mortality in the susceptible colony tested in parallel is more than 98%; the same mortality must be recorded in at least three bioassays conducted at the same site with the same wild vector population at different time points. To date, WHO has received results from 80 tests conforming to these requirements, conducted in 62 sites across seven countries. Until three complete tests are available from each of these sites, WHO cannot interpret these results. Results of biochemical and molecular assays to detect metabolic resistance mechanisms were available for 35 countries and 364 sites for the period 2010–2020. Mono-oxygenases were detected in 68.3% of the sites for which reports were available, glutathione S-transferases were detected in 81.9% of the sites, esterases in 78.5% of the sites and acetylcholinesterases in 73.5% of the sites. Results of assays to detect target- site resistance mechanisms were available for 40 countries and 596 sites. Kdr L1014F was detected in 76% of the sites and Kdr L1014S in 53.1% of the sites. Insecticide resistance data collected using WHO procedures are included in the WHO global database on insecticide resistance (96) and are publicly available via the Malaria Threats Map (73). This tool provides a summary table showing the status of phenotypic resistance and resistance mechanisms by country; presents maps to inform discussions on the deployment of pyrethroid-PBO nets; allows selected datasets to be downloaded; and includes an animation of insecticide resistance evolution over time, based on reports received by WHO. An. stephensi: Anopheles stephensi; NMP: national malaria programme; WHO: World Health Organization. FIG. 9.6. Detections of An. stephensi in the Horn of Africa and Nigeria, reported to WHO (2012–2021) Sources: Reports from NMPs and national health institutes, their implementation partners, research institutions, scientific publications and WorldPop (73, 98). 96 W O RL D M AL AR IA R EP O RT 2 02 2 9.4 ANOPHELES STEPHENSI INVASION AND SPREAD Anopheles stephensi is an efficient vector of both P. falciparum and P. vivax parasites. It was originally native to parts of Asia and the Arabian Peninsula, where it is a major malaria vector in rural and urban areas. In 2012, it was detected in Djibouti and was implicated in two consecutive malaria outbreaks (97). Since then, it has continued to spread in the Horn of Africa and has also been detected in Nigeria. To date, WHO has received reports of An. stephensi detections from over 64 different sites in Djibouti, Ethiopia, Somalia and the Sudan (Fig. 9.6). The characteristics of this vector make its control challenging. An. stephensi breeds in human-made water storage containers in urban areas and appears to quickly adapt itself to the local environment (including cryptic habitats such as deep wells). It also survives extremely high temperatures during the dry season, when malaria transmission usually reaches a seasonal low. Insecticide resistance data reported to WHO show that An. stephensi has exhibited resistance to pyrethroids, organophosphates, carbamates and organochlorines in the Arabian Peninsula and Asia. In the Horn of Africa, it has exhibited resistance to pyrethroids, organophosphates and carbamates. An. stephensi poses a threat to malaria control and elimination in Africa, the Arabian Peninsula and southern Asia. If uncontrolled, its spread across the Horn of Africa, combined with rapid and poorly planned urbanization, may increase the risk of malaria transmission in African cities. WHO therefore encourages countries where An. stephensi invasion is suspected or has been confirmed to take immediate action. WHO recommends that countries increase vector surveillance to delineate the geographical spread of this vector, and use the data to implement interventions aimed at preventing its further spread, especially into urban and periurban areas. Research institutions and implementation partners are encouraged to immediately report any detection of An. stephensi to ministries of health and WHO, to inform national and global responses. Further guidance on how to monitor and control An. stephensi is provided in the relevant WHO vector alert (17). 97 9 | Biological and other threats to malaria interventions 9.5 EFFECTIVENESS OF ITNS ITNs are the most widely used malaria vector control intervention. Since 2005, about 2.5 billion ITNs, mainly pyrethroid-only LLINs, have been distributed for malaria prevention globally (Section 7). Modelling analysis suggests that ITNs were the main drivers of the declines in malaria transmission and burden in the period 2005–2015 (99), especially in settings with moderate to high transmission. Despite widespread pyrethroid resistance, WHO considers that LLINs continue to provide better protection than untreated nets, and encourages their continued use to prevent malaria (9). Since 2015, progress against malaria has slowed (and did so even before the COVID-19 pandemic), especially in moderate and high transmission settings (Section 3). Many factors are likely to have contributed to this situation, including a stagnation of malaria funding amid increasing populations in countries with moderate to high transmission (Section 6), considerable gaps in prevention and therapeutic interventions (Section 7), biological threats to malaria interventions (Section 9), and additional challenges of health and other humanitarian emergencies (Section 2.5). Given that LLINs are the main vector control tool in most malaria endemic countries, the factors that impair their effectiveness (and the effectiveness of ITNs more generally) in malaria prevention are likely to be important in progress against malaria. In operational settings, these factors can be grouped into four broad categories: ■ insecticide resistance resulting in reducing the effects of LLINs on repelling and killing mosquitoes; ■ operational and behavioural constraints (e.g. delivery, access, coverage and acceptability, use, maintenance and retention); ■ physical durability, which comprises fabric integrity; and chemical durability (bioefficacy), which is the availability of one or more active ingredients on the surface of the net over time; and ■ vector dynamics (species biting and resting behaviours). This section summarizes the implications of these factors on the overall effectiveness of ITNs in operational settings. 9.5.1 Epidemiological impact of insecticide resistance The emergence and wide geographical spread of pyrethroid resistance (Section 9.3) is the most recognized threat to ITN effectiveness, but the epidemiological impact of such resistance remains contested. Many factors make it difficult to measure the epidemiological impact of insecticide resistance, including the physical barrier to mosquitoes provided by the nets, the difference in fitness between resistant and susceptible mosquitoes, and the variations in insecticide concentration and bioavailability between pyrethroid- only nets and PBO on non-pyrethroid nets tested in the field (100). A multicountry trial commissioned by WHO showed that ITNs remained highly protective against malaria even in the presence of high pyrethroid resistance (101). In contrast, data from experimental hut studies suggest that as vector susceptibility to pyrethroids falls the repellent and mortality effects on mosquitoes are greatly reduced (102). In addition, evidence from two trials, one from Uganda (103) and one from the United Republic of Tanzania (104), compared the epidemiological impact of pyrethroid-PBO nets against malaria with that of pyrethroid-only LLINs. Both trials were conducted in areas with mosquitoes that are highly resistant to pyrethroid (defined by the review team as mosquitoes demonstrating <30% mortality in discriminating dose assays), and showed that ITNs with PBO had a greater impact than pyrethroid-only LLINs. In addition, a systematic review of the impact of PBO ITNs (105) provided evidence of high to moderate certainty (according to the WHO grading system) that malaria parasite prevalence was lower where pyrethroid-PBO nets were deployed. With this evidence – and taking into account that PBO is less wash-resistant than a pyrethroid-only net treatment, resulting in faster declines in bioavailability over the 3-year estimated lifespan – WHO published a conditional recommendation for pyrethroid-PBO ITNs in areas of high pyrethroid resistance (9). Modelling analysis suggests that the epidemiological impact of ITNs is considerably reduced at high levels of pyrethroid resistance, and that even moderate levels of pyrethroid resistance may lead to an increase in incidence in areas where ITNs are widely used (106, 107). 9.5.2 Defining the durability of LLINs According to WHO guidelines (108), there are three elements to be considered in assessing the durability of ITNs: net survivorship, fabric integrity and insecticidal activity (bioefficacy). These elements are determined partly by factors intrinsic to the manufacture of the net (e.g. material composition, knitting pattern, quality of finishing, insecticide type and content, and additives) and partly by extrinsic factors that cause wear and tear (e.g. storage, exposure to sunlight and rodents, use, washing frequency, housing condition and other household level maintenance). These factors vary between and within countries and between and within households. CI: confidence interval; LLIN: long-lasting insecticidal net.98 W O RL D M AL AR IA R EP O RT 2 02 2 Survivorship of ITNs is defined as the proportion of distributed nets still available for use as intended in the households to which they were given after a defined period. Attrition (the opposite of survivorship) is the proportion of nets no longer in use as intended after a defined period after their distribution to the households. Attrition can be categorized by the main reasons why a net is no longer used; that is, decay (e.g. destroyed or so torn or worn out that it is considered useless for protection against mosquitoes), absence (e.g. stolen, given away or moved) or used for other purposes. Physical or fabric integrity reflects the number, location and size of holes in each net. When possible, the assessment can also be categorized by type of hole (burn, tear, seam failure or chewed by animals). The physical or fabric integrity of the surviving nets can be assessed as a function of length of use, until deterioration leads to the net being discarded or used for another purpose. Insecticidal activity (bioefficacy) is the degree of knock- down, mortality or inhibition of blood-feeding induced in susceptible mosquitoes, as determined by standard WHO test procedures and criteria (i.e. cone bioassay or tunnel test). Insecticidal activity is associated with the type and content or availability of insecticide and the length of use. 9.5.3 Operational and behavioural constraints In sub-Saharan Africa, where most ITNs are distributed, about 590 million ITNs were delivered to communities in the period 2019–2021 (Section 7). At a ratio of one ITN to two persons, a population of about 1 billion people, and assumptions of perfect universal coverage and a 3-year median lifespan for a long-lasting ITN, the distribution of 590 million ITNs would mean that 100% of the population in this region would have access to an ITN. In reality, in 2021, the estimated percentage of the population with access to an ITN within their household and the percentage of the population sleeping under an ITN was 54% and 47%, respectively (Section 7). This is in part because net loss starts almost immediately and net retention reduces over time, while initial allocation inefficiencies mean that some populations may not own a net at all. Research on ITN access, use and nets-per-capita over time has identified three main barriers to universal ITN ownership and use: allocation efficiency, retention and use rate (69). The allocation barrier is seen in the relationship between ITN nets-per-capita and ITN access; at higher coverage levels, instead of the two being almost linearly correlated, additional nets-per-capita lead to diminishing returns in terms of gains in access because FIG. 9.7. Median LLIN retention time by country, ordered from highest to lowest Dots show mean parameter values, and vertical bars indicate 95% CI width. Countries with fewer surveys have less stable model fits; those having fewer than three surveys are indicated in red. Source: Bertozzi-Villa et al. (2021) (69). Eq ua to ria l G ui ne a N ig er Ca m er oo n G ab on Er itr ea Su da n M al i Z im ba bw e To go So m al ia Ke ny a N ig er ia Ta nz an ia Co m or os Ce nt ra l A fr ic an R ep ub lic G ha na Cô te d ’Iv oi re U ga nd a M ad ag as ca r G am bi a Rw an da Bu rk in a Fa so G ui ne a Si er ra L eo ne D em oc ra tic R ep ub lic o f t he C on go G ui ne a− Bi ss au Se ne ga l M oz am bi qu e Et hi op ia M al aw i Za m bi a Bu ru nd i An go la Be ni n M au rit an ia D jib ou ti Li be ria So ut h Su da n Ch ad Re pu bl ic o f t he C on go 1 2 3 4 LL IN m ed ia n re te nt io n tim e (y ea rs ) ITN: insecticide-treated mosquito net; LLIN: long-lasting insecticidal net. 99 9 | Biological and other threats to malaria interventions it becomes increasingly difficult to allocate an ITN to someone who does not already have access to one (69). In terms of retention, there is growing evidence that, in some settings, nets are discarded on average much sooner than they are replaced by new mass distribution campaigns (109–112), mainly owing to the nets developing holes (113) through the stresses of day-to-day use, which reduces their effectiveness at preventing mosquito entry and increases the likelihood that nets will be discarded (114). Therefore, the household’s attitudes towards their nets, net handling behaviours and other hazards to and durability of the net fabric and construction (115) are key drivers of retention (69). Retention of ITNs is variable between and within countries, with the median retention period ranging from 1 year to 6 years (69, 109), and most countries having median retention times of 1.0–2.7 years (Fig. 9.7). Pooled data suggest a median retention time of about 1.9 years (69). Even when nets are available to household members, they are not used 100% of the time, with factors such as age, season, gender and malaria risk affecting usage (116–119). For example, children and teenagers aged 5–19 years often have a lower level of ITN use than other age groups, particularly in households with insufficient nets (116, 117). This age group has also been identified as an important reservoir of malaria transmission (120). ITN use is higher in children aged under 5 years and pregnant women (Section 7) – the two population groups with the highest risk of developing disease in areas of moderate to high transmission. The same modelling framework used to estimate ITN access and use over time (Fig. 7.2 and Fig. 7.3) was harnessed to explore the hypothetical impact of overcoming each of these barriers, by first setting the use rate to 100%, then setting median net retention time to 3 years and finally imagining perfect allocation of nets (Fig. 9.8). Under these scenarios, net use is less of a barrier to universal coverage targets than net retention or net access. Results suggest that improving net retention alone would, over time, be able to lift most countries above the 80% universal coverage target and that improving net allocation would provide a substantial additional boost to coverage. When net use, retention and allocation barriers are removed, it is estimated that 29 of 40 countries in sub-Saharan Africa would meet their universal coverage targets (Annex 1). In addition to allocation efficiency, overall impact may also be improved through subnational tailoring of ITNs based on epidemiological and other factors. These operational constraints are challenging to overcome and require considerable investments in delivery systems and public awareness (Section 9.5.5). FIG. 9.8. The impact on ITN use of reducing operational and behavioural constraints in sub-Saharan Africa Source: Institute for Disease Modeling and Malaria Atlas Project (68). 100 75 50 25 0 Pe rc en ta ge o f p op ul at io n sle ep in g un de r a L LI N 2005 20102000 2015 2020 2021 ■ Current estimate of percentage of population sleeping under a LLIN ■ Percentage of population sleeping under a LLIN if everyone with access to a net used it ■ Percentage of population sleeping under a LLIN if all nets were retained for a median of 3 years and everyone with access to a net used it ■ Percentage of population sleeping under a LLIN if nets were perfectly allocated and retained and had 100% access and use ITN: insecticide-treated mosquito net; LLIN: long-lasting insecticidal net.100 W O RL D M AL AR IA R EP O RT 2 02 2 9.5.4 Implications for overall ITN effectiveness Insecticide resistance and challenges of allocation, retention and use collectively reduce the impact of ITNs in real-life settings. This section presents a modelling analysis to describe the interaction of these factors and their impact on overall ITN effectiveness. A malaria transmission model (121) was used to estimate how much impact each of these constraints has on the potential effectiveness of ITNs over the 3 years after net distribution (Fig. 9.9, Table 9.1). Starting from a net that has the required insecticidal levels, and is highly durable, perfectly allocated, always used and never discarded, scenarios were explored in which each constraint was re-introduced sequentially. To create scenarios that mirrored the effectiveness of the ITNs distributed today, first, insecticidal efficacy was assumed to wane over time, then mosquitoes were assumed to be less susceptible to insecticide, then net durability was assumed to wane over time, and so on. The decomposition of ITN impact, in units of percentage point reduction in prevalence compared with a scenario with no nets, is shown for a baseline moderate to high transmission setting in Fig. 9.9. Shortly after net distribution, lower killing due to insecticide resistance has a large impact on overall net effectiveness. By the end of the time series, however, the main causes of reductions in net effectiveness are the waning of both insecticide efficacy and physical durability. When combined with barriers to ITN access, use rates and retention, the impact of the ITN campaign is almost zero by the end of the third year. The physical durability FIG. 9.9. Sequential decomposition of LLIN effectiveness over 3 years (modelling pyrethroid-only ITNs; further details explaining different scenarios are provided in Table 9.1) Sources: Institute for Disease Modeling and Malaria Atlas Project (68). 70 60 50 40 30 20 10 0 Pe rc en ta ge p oi nt d i

er en ce in p re va le nc e co m pa re d to S ce na rio 8 1 Year 20 3 ■ Scenario 1: “Ideal” nets, perfect campaigns, perfect net use rate, nets retained forever ■ Scenario 2: “Ideal” nets except that insecticide wanes; perfect campaigns, net use rate, and net retention ■ Scenario 3: “Ideal” nets except that insecticide wanes and vectors are not fully susceptible; perfect campaigns, net use rate, and net retention ■ Scenario 4: “Realistic” nets: net durability wanes, insecticide wanes, and vectors are not fully susceptible; perfect campaigns, net use rate, and net retention ■ Scenario 5: “Realistic” nets, realistic campaigns (87% coverage), perfect net use rate and net retention ■ Scenario 6: “Realistic” nets, realistic campaigns, realistic net use rate (83%), perfect net retention ■ Scenario 7: “Realistic” nets, realistic campaigns, realistic net use rate, realistic net retention (50% of nets discarded after 1.9 years) ■ Scenario 8: No LLINs in use in the population 101 9 | Biological and other threats to malaria interventions of ITNs was assumed to decline in a piecewise linear fashion over the years following distribution, such that the probability of a mosquito breaching a net declined by 10% at the end of year one, 50% at the end of year two and 80% at the end of year three. Pyrethroid-only LLINs are modelled in this analysis (Fig. 9.9, Table 9.1). The modelling does not include any potential impact achieved by nets distributed through continuous distribution systems including school-based distributions. The results shown in Fig. 9.9 are sensitive to the order in which constraints are re-introduced, because every subsequent scenario is working on the margin of the impact of the previous scenario. Therefore, this plot should be interpreted as sequential and cumulative, rather than reflecting the independent effect of each intervention change on the burden overall. In this case, the order was chosen to first consider the impact of the physical and chemical properties of the net (killing and physical durability), followed by features of campaigns (access), followed by the impact of net use and retention by owners. This analysis is illustrative and is intended to demonstrate that, in relative terms, ITNs suffer a considerable effectiveness decay that impairs their impact in preventing malaria. The analysis must be interpreted with the understanding that the results have important uncertainties and represent a simulated scenario. Actual effectiveness of ITNs will vary between and within countries. TABLE 9.1. Descriptions of scenarios presented in Fig. 9.9a Sources: Institute for Disease Modeling and Malaria Atlas Project (68). Full details Scenario 1 60% mosquito killing without waning; 90% net physical durability without waning; 100% coverage; 100% use rate; nets never discarded before 3 years Scenario 2 60% mosquito killing with waning (3-year half-life); 90% net physical durability without waning; 100% coverage; 100% use rate; nets never discarded before 3 years Scenario 3 42% mosquito killing with waning (3-year half-life); 90% net physical durability without waning; 100% coverage; 100% use rate; nets never discarded before 3 years Scenario 4 42% mosquito killing with waning (3-year half-life); 90% net physical durability with waning (decline by 10% after year 1, 50% after year 2, 80% after year 3); 100% coverage; 100% use rate; nets never discarded before 3 years Scenario 5 42% mosquito killing with waning (3-year half-life); 90% net physical durability with waning (decline by 10% after year 1, 50% after year 2, 80% after year 3); 87% coverage; 100% use rate; nets never discarded Scenario 6 42% mosquito killing with waning (3-year half-life); 90% net physical durability with waning (decline by 10% after year 1, 50% after year 2, 80% after year 3); 87% coverage; 83% use rate; nets never discarded before 3 years Scenario 7 42% mosquito killing with waning (3-year half-life); 90% net physical durability with waning (decline by 10% after year 1, 50% after year 2, 80% after year 3); 87% coverage; 83% use rate; nets discarded with a 1.9-year median retention time Scenario 8 No LLINs in use in the population LLIN: long-lasting insecticidal net. a The term “net physical durability” refers to the probability of a mosquito not breaching a net after landing on it. “Killing” refers to the probability of death for a mosquito that lands on a net. 102 W O RL D M AL AR IA R EP O RT 2 02 2 9.5.5 Improving the effectiveness of ITNs ITNs are the foundation of the malaria vector control that has driven many of the gains recorded so far, but efforts must be made to improve their effective coverage of vulnerable populations. This starts with producing more robust ITNs that can counteract the effects of pyrethroid resistance; however, other (extrinsic) factors must also be considered. Locally appropriate distribution models could help to reduce wastage and ensure that old nets are efficiently replaced, while a focus on the end user and usage practices could help to prolong the life of ITNs and the protection they afford. Improving the physical durability of LLINs Over the years, the focus of global efforts has been to keep the price of LLINs low, create predictability in supply and ensure increased community-level coverage. Analyses of the effect of these price reductions on quality of ITNs have been limited, although all ITNs originally recommended by the WHO Pesticide Evaluation Scheme (WHOPES) and all of those that are now WHO prequalified were subject to minimum quality standards (122). Under the current recommendation, an LLIN would be expected to retain its biological activity for at least 20 standard washes under laboratory conditions and 3 years of recommended use under field conditions (123). Ongoing discussions on ITN effectiveness have focused on the design of the product and its potential efficacy; selection of an appropriate product for the cultural and entomological context in which the ITN is intended to be used; consistent formulation and manufacturing; proper storage, transport and handling; and use of the product as instructed (115). In particular, it is challenging to develop predictive metrics of the operational physical durability of ITNs because of the many influencing factors. This makes it difficult to establish the level of physical quality that confers optimum benefits at affordable costs, which is needed to guide a value-based approach to the process of ITN production and purchase. In an attempt to address the issue of physical durability, some indices have been explored in the published literature. For example, resistance to damage (RD) scores have been proposed as correlates of physical survival of ITNs (124). The RD score, ranging from 0 to 100, was developed as a composite measure of ITN snag strength, bursting strength, abrasion resistance and resistance to hole enlargement. An analysis of data from 10 sites in four countries where ITN durability was monitored for six LLIN brands showed that the RD score was a strong predictor of physical durability of ITNs in the field (125). Another metric of physical durability, functional survival – defined as survival of nets in “serviceable” condition – of three pyrethroid- only LLINs was compared in a study in the United Republic of Tanzania (126). At the end of the 3-year period, the median functional survival was significantly different between the three net products, at 2.0, 2.5 and 2.6 years. Functional survival in other settings has varied from 3.2 to 5.6 years for the same product in different areas of Nigeria (110), 1.6 to 2.2 years for the same product in the Democratic Republic of the Congo (111), 2.3 to 2.8 years for different products in Ghana (127), 2.0 to 2.4 years for different products in Kenya (128), 4.0 to 4.6 years for the same product in Liberia (129), 2.2 to 3.1 years for the same product in Mozambique (130), 2.1 to 3.4 years for different products in Mali (131) and 2.7 to 2.9 years for different products in Zanzibar (United Republic of Tanzania) (132). In view of the various challenges of measuring ITN durability, in October 2022, the WHO Prequalification Department launched an open public consultation on updated guidelines for the prequalification assessments of ITNs, followed by a technical consultation (133). Among the many issues covered in the draft guidelines were the post-production stages in the life of an ITN, from its shelf-life to initiation of use (i.e. storage, transport and distribution) and the life-stage of ITNs when in use (e.g. reduction of active ingredients over time, physical durability, resilience to washing and exposure to sunlight). Improving allocation efficiency of ITNs Analysis shows that, under current assumptions of populations in need, increasing net-per-capita is challenging because saturation levels are reached (69). However, many countries rely on projections of census data for the purposes of microplanning and quantifying target populations for ITN distribution. As shown in the recent example of Benin (134), such census- based projections can underestimate populations in need and result in suboptimal coverage. By switching to digitalized mass campaign distribution of ITNs (based on household mapping and an enumeration exercise), an additional 14% of the population was identified, leading to over 94% coverage of LLINs (134). Digitalized campaign microplanning can therefore help to bridge the persistent coverage gap observed in many countries, increase access and ensure fairer distribution of nets. Although the upfront costs of establishing such digital platforms and the field enumeration of households can be relatively high, the overall investment can have a high return on investment and improve cost–effectiveness, not just by ensuring high and equitable LLIN coverage, but also by supporting the establishment of the platforms and data repositories needed to improve population access across other interventions for other types of campaign (e.g. SMC and immunizations). 103 9 | Biological and other threats to malaria interventions With the median ITN half-life being about 2 years, it has been suggested that reducing the frequency of mass campaign cycles from 3 years to 2 years could sustain coverage levels and achieve greater impact; however, this would lead to considerably higher commodity and operational costs, including a strain on human resources. Alternative configurations of continuous distribution channels may yield high and sustained coverage compared with 3- or 2-year ITN campaign cycles. A modelling analysis using specific indicators from the United Republic of Tanzania suggests that using continuous school-based distributions, in addition to current distributions through Expanded Immunization Programme (EPI) and ANC clinics, is likely to achieve higher sustained coverage with fewer nets compared with 3-year mass campaigns (135). Across the WHO African Region, however, there is variation in access to schools and EPI and ANC clinics; hence, further analysis is required on the potential impact on equity of continuous distribution under different settings. Although studies show that the costs of continuous distribution are in line with the costs of mass campaigns (136, 137), additional cost analysis from different settings and operational approaches is needed. Improving maintenance and use of ITNs Net durability and retention are affected by attitudes towards care for ITNs, behaviour such as improper storage and frequent washing, and damage through hanging, exposure to sunlight and rodents. Behavioural change interventions could positively impact on these factors (138). For example, an intervention– control design study in Nigeria showed that targeted behavioural change interventions communicated through different channels improved the overall condition of ITNs, increasing median lifespan and retention (139). Similarly, a summary of studies on behavioural change interventions, communicated in culturally sensitive ways through multiple context- specific channels, showed that such interventions can improve the use of ITNs by household members (140). Although clear social and behaviour change communication (SBCC) strategies exist, these critical interventions remain underresourced and greater investment is required (141). Mitigating the impact of widespread insecticide resistance In many settings, pyrethroid-only LLINs remain effective, even if they are less effective than before the local mosquito populations started to become resistant. However, as pyrethroid resistance becomes widespread and increases in intensity in many settings (9, 142), nets with newer chemistries are needed. Fortunately, there has been progress in this area. For example, the combination of pyrethroid with PBO has led to the development of pyrethroid-PBO ITNs, which have higher efficacy than pyrethroid- only nets; such nets are now recommended by WHO (9). Two pyrethroid combination ITNs are under evaluation: alphacypermethrin+pyriproxyfen ITNs and alphacypermethrin+chlorfenapyr ITNs. Pyriproxyfen is an insect growth regulator that reduces adult mosquito fertility, thereby reducing the vector population (9). Chlorfenapyr targets the mosquitoes’ mitochondria, stopping respiration at the cellular level and thus killing the mosquito. A recent randomized controlled trial (RCT) in the United Republic of Tanzania compared the effectiveness of pyrethroid-only LLINs with these three types of net with pyrethroid combinations (143). The study showed that, overall, the combination ITNs performed better at preventing malaria infections in children aged under 5 years; however, the difference was statistically significant only for the chlorfenapyr combination (odds ratio 0.45; 95% CI: 0.30–0.67; P=0.0001). This evidence was recently presented to WHO and formal assessment and development of a recommendation is currently ongoing. Other ITN products in the R&D pipeline are discussed in Section 10.4. 9.6 EFFECTIVENESS CHALLENGES RELATED TO IRS IRS is the second most widely implemented vector control intervention by NMPs (Section 7.2). When carried out correctly, IRS has been shown to be a powerful intervention to reduce adult mosquito vector density and longevity and, therefore, to reduce malaria transmission (144). However, despite its long tradition and the large body of associated operational experience, few RCTs have been conducted on IRS (8). There are some conditions similar to those considered for ITNs that are important for IRS if it is to have impact: most of the vector population feeds and rests indoors, people mainly sleep indoors at night and there are high levels of acceptance in the community. However, implementation of IRS at the required quality is logistically more challenging and considerably more expensive than the distribution of ITNs. In addition, evidence showing the impact of IRS when implemented in areas with high levels of ITN coverage is limited (8). Many factors influence the effectiveness of IRS, including insecticide resistance, the timing of spraying and the level of training of spray operators (which affects the quality of spraying) (145). In addition, at large scale, the costs are high. A recent review showed that, compared with ITNs, IRS was about five times more expensive per person protected per year, making it considerably less cost effective (146). 104 W O RL D M AL AR IA R EP O RT 2 02 2 There have been suggestions that community-based delivery of IRS, where spray operators and materials are closer to the households and engagement of community health workers, may improve performance efficiencies, as observed in a study in Ethiopia (147). However, this study found a modest increase in coverage at a slightly lower cost without major differences in quality between community-based and campaign-type delivery systems. Although such alternative approaches may offer a pathway to implement less costly IRS, the maintenance of equipment, the sustained supervision of sprayers and the large budgets needed for widespread scale-up will remain a challenge. Costs are likely to increase even further as resistance to insecticides used in IRS emerges and more expensive chemicals are needed. Longer lasting insecticides, if developed and made available at reasonable prices, will reduce the frequency of IRS and may eventually reduce the cost of logistics. 9.7 BEYOND THE REACH OF ITNS: ADDRESSING RESIDUAL TRANSMISSION Neither ITNs nor IRS are suited to reducing the effects of outdoor biting, a situation made even more challenging by both the genetic (insecticide resistance) and behavioural adaptations of the mosquitoes. Insecticide resistance is well known and closely monitored. However, the behavioural adaptation of the mosquito – in terms of changes to biting, feeding and resting behaviour – may be equally important, but is less well known and far less monitored (148– 150). Evidence is increasing of the vector’s attempts to reduce contact with insecticides; for example, by biting early before people go to bed, spending more time resting outdoors or feeding on livestock instead of humans. In addition, vectors that have this behavioural plasticity become more dominant than those with limited behavioural changes. One example of such evidence comes from a 4-year longitudinal study of the malaria vectors An. arabiensis and An. funestus s.l. – which rest indoors and outdoors and seek a host indoors – in four villages in the United Republic of Tanzania. The study found evidence of changes in mosquito vector abundance, resting habitat and host that were consistent with the emergence of behavioural avoidance strategies, following a mass distribution of LLINs (149). Although not explicitly considered in the effectiveness analysis presented in Section 9.5.4, this behavioural adaptation reduces the effectiveness of ITNs and IRS, independent of other factors, and is implicated in residual transmission in some settings (151). Behavioural adaptation on the part of the vector will require investment in R&D into interventions that target outdoor transmission (Section 10). 105 W O RL D M AL AR IA R EP O RT 2 02 2 106 10MALARIA PREFERRED PRODUCTS AND R&D PIPELINE WHO/GMP has been involved in the development of preferred product characteristics (PPCs) and target product profiles (TPPs) as key tools to incentivize and guide the development of products with high public health impact and suitability for use in LMIC. In close coordination with WHO, product development partnerships such as the Foundation for Innovative New Diagnostics (FIND) (152), the Medicines for Malaria Venture (MMV) (153) and the Innovative Vector Control Consortium (IVCC) (154) have worked with partners such as Unitaid (155) and the Global Fund (156) and with the private sector to catalyse malaria R&D to develop innovative antimalarial tools. 10.1 PREFERRED PRODUCT CHARACTERISTICS AND TARGET PRODUCT PROFILES FOR NEW MALARIA INTERVENTIONS WHO PPCs and TPPs aim to communicate unmet public health needs and stimulate the development of products to meet those needs (157, 158). PPCs outline the intended use, target populations and other desired attributes of products, including characteristics related to safety and efficacy. Over recent years, new and updated PPCs have been developed for several malaria products. 10.1.1 Diagnostics WHO is coordinating the development of TPPs for glucose-6-phosphate dehydrogenase (G6PD) tests to meet the needs for P. vivax control (159). Several drugs used for P. vivax radical cure, such as primaquine and tafenoquine, can lead to haemolysis in G6PD-deficient individuals in malaria endemic areas; hence, there is a need for G6PD testing, to guide drug administration. Therefore, TPPs are being developed for point-of-care screening or triage tests for G6PD activity, and for one- time quantitative tests for G6PD activity to establish baseline G6PD activity (phenotype) +/- genotype (of common variants) and haemoglobin concentration. PPCs for point-of-care and laboratory-based testing of P. vivax recent infection have also been developed. 10.1.2 Vaccines Since the first malaria vaccine PPCs were published in 2014 (160), major milestones in R&D have been achieved. In 2021, RTS,S/AS01 became the first malaria vaccine to be recommended by WHO for use in settings with moderate to high transmission in sub-Saharan Africa. However, a healthy supply of vaccines will be needed to meet the global demand. Updated malaria vaccine PPCs were published in 2022 (161); they include an expanded set of strategic goals for malaria vaccine R&D for the prevention of blood- stage infection, reduction of morbidity and mortality, and reduction of community-level transmission. 107 10.1.3 Vector control Several PPCs for vector control have been published, ranging from the use of ITNs and indoor residual surface treatments (IRST) to endectocide and ectocide products: ■ PPCs for ITNs for malaria transmission control in insecticide-resistant mosquito populations, published in 2021 (162); ■ PPCs for vector control interventions to control malaria in complex humanitarian emergencies and in response to natural disasters, published in 2021 (163); ■ PPCs for endectocide and ectocide products for malaria transmission control, published in 2022 (164) – these products may be existing drugs that have been repurposed for malaria control or new drugs specifically developed for this purpose; and ■ PPCs for IRST for malaria transmission control in areas with insecticide-resistant mosquitoes, published in 2022 (165). 10.1.4 Chemoprevention In December 2020, WHO/GMP and MMV co-convened a technical consultation to develop PPCs for drugs for malaria chemoprevention (166). PPCs for malaria chemoprevention drugs aim to address several use- cases: chemoprevention in children (SMC, IPTi/ PMC, IPTsc and PDMC), during pregnancy (IPTp) and in nonimmune travellers. Development of new malaria chemoprevention drugs can include repurposing of approved malaria treatments for use in chemoprevention; recombining approved individual drugs into new combinations for chemoprevention; or development of new drug combinations specifically for chemoprevention. 10.1.5 Monoclonal antibodies Passive immunization via the direct administration of functional antibodies could potentially be used as prophylaxis for several months, providing short- term protection to populations at high risk of clinical malaria or individuals less able to develop robust immune responses. In November 2021, WHO/GMP and IVB convened a scientific development group (166) to develop PPCs and address clinical development considerations for monoclonal antibodies (mAbs) for malaria prevention. The most immediate public health priority is the reduction of morbidity and mortality in infants and children due to P. falciparum. Another use-case for future consideration is the reduction of morbidity and mortality in adults, particularly the prevention of infection in pregnant or lactating women, women of childbearing age and other high-risk or vulnerable adult populations. G6PD: glucose-6-phosphate dehydrogenase; HRP2: histidine-rich protein 2; LDH: lactate dehydrogenase; LFA: lateral-flow assay; Pf: Plasmodium falciparum; Pv: Plasmodium vivax; RDT: rapid diagnostic test; WHO: World Health Organization. a Test types currently eligible for the WHO prequalification programme. 108 W O RL D M AL AR IA R EP O RT 2 02 2 10.2 MALARIA DIAGNOSTICS The malaria diagnostic tests market is dominated by RDTs that detect the HRP2 antigen expressed by P. falciparum. Single-line tests reliant on this one antigen represented 80% of the 413 million RDTs procured from WHO-prequalified suppliers in 2021. However, the spread of P. falciparum parasites with Pfhrp2/3 gene deletions presents a major threat to reliable diagnosis. A diversified diagnostic landscape is needed to address this liability and the inadequate sensitivity of tools for species other than P. falciparum; also needed is a broader range of diagnostic use- cases (e.g. highly sensitive noninvasive screening for the prevention of re-introduction). The R&D pipeline includes products to address these gaps through efforts to both improve existing point-of-care platforms (specifically, lateral-flow immune-chromatographic antigen-detection tests [RDTs] and microscopy) and develop alternatives that use a range of sample types and technologies (Fig. 10.1). 10.2.1 Improvements to existing test types In response to the critical need for non-HRP2 tests for P. falciparum and for improved P. vivax assays, efforts have centred on improving the sensitivity of detecting the species-specific LDH biomarker. RDTs based on FIG. 10.1. Malaria diagnostics pipeline, showing the development stage of main product types according to primary intended use-case and sample type Source: Unitaid (2022) (167). Feasability and proof of concept Design and develop Lab and clinical evaluation Market entry registration and pilots Scale up Design lock Regulatory High-sensitivity HRP2-RDT Pf LDH RDT Pv LDH RDT Digital microscopy Hemozoin tests Point of care nucleic acid tests C AS E M AN AG EM EN T BL O O D N O N -I N VA SI VE Transdermal hemozoin Saliva-based Urine-based Volatiles-based G 6P D BL O O D Quantitative G6PD biosensor Qualitative G6PD LFA H YP N O ZO IT E BL O O D Serology platforms: past exposure detection Hypnozoite metabolites 109 10 | Malaria preferred products and R&D pipeline PfLDH and PvLDH are currently under clinical evaluation by FIND and PATH, with expected submissions for WHO prequalification in 2022 and 2023. Three products are eligible for Global Fund procurement through the Global Fund/Unitaid Evidence Review Panel for diagnostics (ERPd). P. falciparum parasites express lower levels of PfLDH than HRP2, which presents a technical challenge for testing sensitivity; however, PfLDH does not persist in the blood after infection clearance and is thus an indicator of acute infection. Products in development have a range of test line configurations (e.g. single line for detection of both HRP2 and PfLDH, versus separate lines, versus inclusion of PvLDH), which may suit different clinical applications. Digital apps (e.g. RDT readers and clinical decision- support tools) are being developed to support quality assurance and real-time surveillance. Demonstrating the benefits of such tools across use-cases is important to justify the additional costs these apps incur. Outside sub-Saharan Africa, microscopy is the most widely used method of diagnosis because it allows for speciation and estimation of parasite loads. To support quality assurance and standardized performance, several approaches have been taken to develop digital microscopy, with different degrees of automation. Several such devices have recently come to market and others are in development. 10.2.2 Alternative platforms and sample types There are numerous ongoing efforts to adapt complex laboratory nucleic acid detection methods into point-of-care devices, for the detection of very low parasite densities. The pipeline of molecular diagnostics includes early feasibility efforts through to commercially available products. Noninvasive diagnosis is a growing area of interest, with potential applications for rapid screening outside conventional medical settings. Several projects are in early development; analytes of interest include saliva, volatile organic compounds, urine and hemozoin (a crystalline by-product of parasite metabolism, found in the digestive vacuole of Plasmodium). Promising biomarkers in saliva have been identified and are now the focus of product development efforts, whereas standardized panels of volatile biomarker signatures are more challenging to characterize. A simple dipstick assay that detects HRP2 in urine has been developed and commercialized in Nigeria for use in self-testing, but this application is not currently recommended by WHO. Technological platforms are also being developed for use with varied analytes and biomarkers, with biosensors promising a field-adapted and highly sensitive option. Hemozoin can be detected, for example, through optical and magnetic methods, as an indicator of infection. In development are blood-sample-based methods and noninvasive strategies that detect hemozoin through the skin. Commercial hemozoin-based products using blood samples are currently available, although speciation remains a challenge; noninvasive approaches are in early-stage development. These new technologies are not currently eligible for submission for WHO prequalification, which to date include only lateral-flow RDT format assays. 10.2.3 Beyond diagnosis of acute infection of P. vivax Diagnosis of G6PD status is recommended before radical cure treatment of P. vivax patients. A biosensor for quantitative G6PD activity measurement is currently under review for WHO prequalification and has already been approved by ERPd; other diagnostic tests are in the pipeline. Simpler qualitative lateral-flow assays are also in development; these could increase access to testing, but it may not be possible to adapt them for the diagnosis of female patients, who express a continuum of G6PD enzyme activity levels. Manufacturer interest in this diagnostic application is limited by the small market and the constraints of ensuring affordability. However, neonatal screening could represent an additional use for the devices and expand their uptake. Another diagnostic application to recently join the pipeline but with prospects of a limited market size is the detection of latent P. vivax hypnozoite infection. Such diagnostic tests could have several uses, ranging from demonstrating an absence of transmission to identifying individuals at risk of relapse who could benefit from radical cure treatment. Investigation of both indirect and direct diagnostic approaches is ongoing. Serological platforms detecting antibody biomarkers that identify past exposure can be used as a proxy indicator of hypnozoite infection risk; product development using this indirect approach is ongoing. In terms of direct diagnostic approaches, feasibility studies to detect hypnozoite metabolites are being explored. 10.2.4 Supporting the diagnostic pipeline and landscape to promote quality diagnosis Supporting manufacturers to develop new products is necessary, given the pressures of low profit margins that characterize the malaria market and deter many suppliers. Before 2020, donor-funded procurement depended on only two suppliers that shared over 80% of the market. Since 2018, the supplier base has shifted significantly, partly due to deliberate efforts by large procurers to diversify and strengthen the market. There are ample WHO-prequalified products for each of the major conventional case management test types in 110 W O RL D M AL AR IA R EP O RT 2 02 2 which P. falciparum detection is by HRP2 (e.g. Pf-only, Pf/pan and Pf/Pv); however, for areas with Pfhrp2/3 deletions, there are limited quality assured tests. Maintaining an active pipeline of new products and a resilient diagnostics market requires further efforts to support both manufacturers and country programmes. WHO guidelines recommend a shift to non-HRP2-based tests in areas where 5% or more of P. falciparum infections are false negative on HRP2 RDTs because of Pfhrp2/3 gene deletions. However, the uptake of new products (e.g. PfLDH RDTs) has been slow. This may be due to insufficient evidence on the presence of deletions and the increased unit costs of the new RDTs, which would result in programmes being unable to meet their total demand. There has been slow uptake of a “high-sensitivity” HRP2- based RDT that came to market in 2017, possibly because of an unclear use-case and cost–benefit demonstration of its impact, as well as storage constraints (i.e. shorter shelf-life and tighter requirements for storage conditions compared with conventional RDTs). Clear communication around the use-cases for new test types in the pipeline, their anticipated market sizes and public health benefits is critical to incentivize manufacturers to develop products and countries to take up new products. Generating evidence around the impact of new diagnostic tests will help to develop investment cases for tools that may have higher costs than current RDTs; forecasts of procurement demands also help suppliers to commit to R&D investments and countries to budget for new diagnostic tests. In addition, guidance is needed on the evidence requirements for policy implementation of new technologies, including those that are not currently included in the scope of the WHO prequalification programme. Finally, concerted efforts to promote quality diagnosis in the largely unregulated retail sector, where about 40% of malaria consultations in the WHO African Region occur, could improve patient outcomes and create new market opportunities to encourage investment in malaria diagnostics. 10.3 MALARIA MEDICINES MMV (153) works in partnership with a network of international organizations, academic institutions and public-sector, nongovernmental organization (NGO) and private-sector entities, to discover, develop and facilitate access to new antimalarial medicines. The current focus of the malaria medicine R&D is on the development of next-generation life-saving medicines for adults, particularly pregnant women and children who are at elevated risk from the consequences of malarial infection. Delivering a non-ACT treatment option, to simplify therapy or as a contingency against the intensification of either artemisinin or the partner drug resistance, is a high priority. These medicines should also reduce the probability of onward disease transmission. Parenteral formulations are being developed to provide new options for the management of severe malaria. Next-generation products are also needed for chemoprevention to address the paucity of alternative options to SP and SP-AQ, to protect millions of pregnant women and vulnerable children, to assist in responses to epidemics, and to provide MDA and prophylaxis for nonimmune travellers. Under the coordination of MMV, there have been efforts to accelerate investment in promising new chemical entities (NCEs) in drug discovery, to offset the high attrition in the pipeline expected during clinical development of new oral treatments. Options under consideration for chemoprevention also include once per season injections with either prodrugs or mAbs. Currently, MMV and partners are progressing two candidate NCEs to preclinical development each year, a rate of output that has been maintained over the past 10 years. 10.3.1 Innovations with existing antimalarial treatments Triple artemisinin-based combination therapy The triple artemisinin-based combination therapy (TACT) approach relies on a combination of the short- acting artemisinin with two long-acting partner drugs to mitigate the risk of resistance. The combination of artemether-lumefantrine and amodiaquine (AL-AQ) is the most advanced in development. A loose combination of AL and AQ has been shown to be efficacious and well tolerated, with no new safety signals identified. The next study will probably involve the final presentation of a fixed-dose combination of the three drugs, to improve patient compliance. ACT plus single low-dose primaquine for transmission blocking A child-friendly primaquine tablet formulation is being developed for prequalification by WHO. Once quality manufacturing of required doses has been secured, co-blister packs with primaquine and ACTs, particularly AL, will be investigated to support the scale-up of this complementary transmission-blocking intervention. Artemether-lumefantrine for neonates In 2008, Coartem® Dispersible (AL) became the first fixed-dose ACT approved specifically for the treatment of children weighing 5 kg or more. A clinical trial is currently underway evaluating a different drug ratio of the two components to address the needs of neonatal patients under 5 kg with acute, uncomplicated malaria. 111 10 | Malaria preferred products and R&D pipeline 10.3.2 Next-generation antimalarial treatments Ganaplacide-lumefantrine Ganaplacide-lumefantrine is currently the most advanced non-artemisinin combination treatment in development. Ganaplacide (formerly known as KAF156) is a novel agent and it is paired with lumefantrine that has been reformulated to support a once-daily dosing regimen (compared with the current twice- daily dosing in combination with artemether). Results from Phase 2 studies showed safety and efficacy in adults, adolescents and children, and the combination is currently undergoing further evaluation in children aged down to 6 months as a 3-day oral regimen. A fixed-dose combination of the two components is expected to be tested in a Phase 3 trial. In addition to clearance of artemisinin resistant strains, this combination has the potential for transmission blocking. M5717-pyronaridine The M5717-pyronaridine combination has the potential for a shorter dosing regimen and is ready to enter Phase 2 development. Pyronaridine is a component of Pyramax®, an approved antimalarial medicine, and it has potent activity against multidrug-resistant strains. M5717 is a compound that has a novel mechanism of action, targeting the parasite protein synthesis pathway. The compound has demonstrated activity against all stages of the P. falciparum life cycle and thus has the potential to not only treat but also prevent malaria. ZY19489-ferroquine The ZY19489-ferroquine combination has the potential to be a single-dose cure owing to the long half-life of ferroquine; it is ready to start Phase 2 trials. Ferroquine, a derivative of chloroquine, was previously evaluated in Phase 2 studies in combination with artesunate but was not developed further. ZY19489 is a novel agent with potent antimalarial activity, with no resistance detected in vitro. Cipargamin Cipargamin (formerly known as KAE609) is a fast- acting, potent compound that is active against drug-resistant strains of malaria. The molecular target for cipargamin was identified as PfATP4, a cell membrane channel. The compound has been shown to be safe by oral route against the parasite as monotherapy in Phase 2 trials in adults in Africa. A new formulation for intravenous injection has been developed and the compound is currently being tested in a Phase 2 study in severe malaria. A combination drug partner has not yet been chosen. 10.3.3 Candidate molecules in early development Table 10.1 presents a list and description of candidate molecules for malaria therapy in early development. TABLE 10.1. Candidate molecules in the early development portfolio for the treatment of clinical malaria Molecule Description MMV533 A highly potent compound against P. falciparum and P. vivax strains, although its mechanism of action is unknown. It demonstrates no cross-resistance and is unable to select for resistant mutants in vitro. It has a long half-life in humans, and a predicted low human dose which together with the fast-killing parasite activity are desirable attributes for a single-dose regimen. It has completed a Phase 1 first-in-human and controlled human malaria infection study. INE963 A fast-killing compound that is highly potent against P. falciparum and P. vivax strains, although its mechanism of action is unknown. It demonstrates no cross-resistance and is unable to select for resistant mutants in vitro. It has a moderate half-life in humans. The molecule is currently being evaluated in a Phase 1 first-in-human study. GSK701 A compound with a novel mechanism of action and fast-killing parasite activity, highly potent only against P. falciparum strains, with a predicted low human dose. It has a short half-life and is currently being evaluated in a Phase 1 first-in-human study. MMV183 A compound with a low predicted human dose, a short to moderate half-life, and no pre-existing resistance or resistance generation in vitro. The very high solubility together with fast-killing activity make it an attractive candidate for use in severe malaria as an injectable formulation. It also has demonstrated transmission- blocking activity. It is currently in preclinical development. GSK484 A fast-killing compound with a predicted long half-life and low human dose – attributes that, if confirmed in humans, make it an attractive candidate for a single-dose regimen. No resistant parasite strains have been identified in the laboratory. It is currently in preclinical development. IWY357 A compound with a predicted low human dose and long half-life. It is fast killing and demonstrates no ability to select resistant mutants in vitro. It is currently in preclinical development. MMV609 A compound that shares the same mechanism of action as cipargamin. It is a fast-killing compound, with a predicted low human dose and long half-life. It has demonstrated transmission-blocking activity. It is currently in preclinical development. 112 W O RL D M AL AR IA R EP O RT 2 02 2 10.3.4 Next-generation chemoprevention treatments MMV has proposed a three-pronged approach for the R&D of new chemoprevention treatments, which was endorsed by WHO at a December 2020 meeting on PPCs for malaria chemoprevention. The three elements are summarized here and discussed below: ■ Repurpose – aimed at achieving a policy change to allow approved treatments with 3-day drug combinations (e.g. DHA-PPQ, pyronaridine- artesunate or atovaquone-proguanil) to be used for monthly, three-dose chemoprevention regimens similar to those currently used for protecting children, or single-dose cures similar to those used for protecting pregnant women. ■ Recombine – aimed at using approved antimalarial drugs in new combinations for prevention. This is a key R&D effort that needs to be accelerated immediately to help reduce the antimalarial burden in high-risk individuals and to bridge the time gap to the development of new drug combinations. ■ Develop – aimed at developing novel drugs in combinations for chemoprevention and getting approval through a joint process between WHO and a stringent regulatory authority. This is a long-term effort and is not expected to result in the launch of a new combination until after 2030. Repurpose DHA-PPQ has garnered most of the attention because it provides the longest post-prophylactic benefit of all ACTs. It has been used for monthly prophylaxis in sickle cell disease where older drugs have showed little benefit (168, 169). Hospital post-discharge prophylaxis for 3 months has also been explored for children with severe anaemia, and has shown significant reduction in hospital re-admissions and mortality (170). DHA-PPQ is also being explored for SMC in east Africa. In addition, three trials of DHA-PPQ have been conducted in east Africa for IPTp. The DHA-PPQ was well tolerated in the second and third trimesters of pregnancy. DHA-PPQ improved malaria outcomes in pregnant mothers and prevented moderate anaemia compared with the standard SP and is cost effective. Stillbirth and neonatal death were also reduced with DHA-PPQ, but low birthweight was marginally better with the use of SP (171). Further studies are needed on infant outcomes at 6 and 12 months. Recombine Combination of DHA-PPQ with SP is being evaluated for chemoprevention in pregnancy. The meta-analysis (171) suggests that SP is providing additional benefit to the fetus, resulting in improved birthweight in additional to its antimalarial properties. MMV tested a three-drug combination of atovaquone-proguanil and amodiaquine in 2019, but development did not progress beyond Phase 1 owing to a higher risk of severe neurological reactions than was seen with individual drugs. Other options for recombination include selecting two known members of the 4-aminoquinoline family (pyronaridine, piperaquine or chloroquine). Alternatively, a monthly treatment dose of atovaquone-proguanil combined with a 4-aminoquinoline, to protect against the development of resistance, might be useful. Develop Identifying a small molecule for either oral administration or injection for chemoprevention is a high priority. Several molecules with the potential for oral chemoprevention when given once monthly are in preclinical testing. With new technologies and formulations, long-acting oral or injectable approaches represent a long-term investment solution complementary to malaria vaccines. The two most advanced injectable prodrugs in the MMV portfolio are highlighted below. Monoclonal antibodies are also being investigated as a potential cost-effective approach for a once per season injection. Outside of the MMV portfolio, there are two monoclonal antibodies from the NIH of which L9LS is the most advanced, currently in Phase 2 development. ■ MMV371 is a prodrug of atovaquone with high liver-stage potency that is in development as a long-acting injection for malaria prevention. It has completed the preclinical safety package and is ready to enter a Phase 1 first-in-human study. A partner drug will have to be identified for further development to protect against resistance. ■ MMV167 (ELQ-331) is the prodrug of ELQ300, a potent inhibitor of P. falciparum cytochrome bc1 that is entering preclinical development. Formulation for oral administration is available, and efforts are ongoing to develop a long-acting injectable formulation. ITN: insecticide-treated mosquito net; R&D: research and development; WHO: World Health Organization. 113 10 | Malaria preferred products and R&D pipeline 10.4 MALARIA VECTOR CONTROL R&D PIPELINE The WHO Global Observatory on Health Research and Development (GOHRD) has collated malaria vector control interventions that are currently under development, reported by disease, type of intervention and stage of development (172). The GOHRD currently lists 28 vector control products in the R&D pipeline (Fig. 10.2). Eleven (39%) of these products are ITNs and seven (25%) are indoor residual wall treatments. Thirteen products (46%) are in the data-generation stage to support assessment of safety, quality and entomological efficacy. Seven products (25%) are undergoing epidemiological trials, six (21%) are being assessed by WHO to inform prequalification listing or WHO policy recommendation and two (7%) are at the prototype development stage. FIG. 10.2. Distribution of vector control products in the R&D pipeline by intervention type Source: WHO (2022) (172). 8 106420 12 14 Peridomestic combined repel and lure devices Personal protection Spatial repellent Systemic insecticides and endectocides Housing modification Larvicide Genetic engineering - self-limiting male mosquitoes Attractive targeted baits Genetic manipulation of vectors for disease control Indoor residual wall treatments ITNs 11 (39%) 7 (25%) 2 (7%) 1 (4%) 1 (4%) 1 (4%) 1 (4%) 1 (4%) 1 (4%) 1 (4%) 1 (4%) IGR: insect growth regulator; IRS: indoor residual spraying; ITN: insecticide-treated mosquito net; IVCC: Innovative Vector Control Consortium; PBO: piperonyl butoxide; TSB: targeted sugar baits. 114 W O RL D M AL AR IA R EP O RT 2 02 2 An overview of the current and previous malaria intervention products pipeline was provided by IVCC (154) and is presented in Fig. 10.3. Since its formation, IVCC has been helping to bring to market novel and repurposed insecticides with different modes of action, for use in ITNs and IRS, to support best practice insecticide resistance management (IRM). The ability to change modes of action every 3 years for ITNs and annually for IRS will preserve the efficacy of the insecticide by reducing the rate at which insecticide resistance will develop. Maintaining insecticide susceptibility by avoiding the use of a single class of insecticide across the same geographies and over consecutive years is key for the efficacy of vector control interventions. The IVCC discovery programme has supported the development and deployment of a suite of IRS products with different modes of action. Pirimiphos-methyl was listed by WHOPES in January 2013, clothianidin was WHO prequalification listed in October 2017 and clothianidin + deltamethrin was WHO prequalification listed in December 2018. These three agents represent a group of new insecticides being deployed across endemic countries as part of their IRM strategies. In addition, chlorfenapyr and broflanilide, for which a prequalification listing is expected in the near future, will provide additional resistance management options. The ability to change the mode of action on an ITN will also preserve, for as long as possible, the efficacy of new or repurposed insecticides applied to ITNs. Pyrethroids are becoming less efficacious because of their widespread use without an alternative mode of action. In 2017, a new ITN received a WHO recommendation. It combines a pyrethroid with a repurposed insecticide, chlorfenapyr, which has a different mode of action. The mixture of these two active ingredients is a step forward in insecticide resistance. Three further active ingredients are either in late-stage research or have progressed to full development and are expected to obtain a prequalification listing between 2026 and 2030. FIG. 10.3. A roadmap of current and new vector control products Sources: IVCC (2022) (173). 203920382037203620352034203320322031203020292028202720262025202420232022202120202019201820172016 2040 Pirimiphos-methyl Clothianidin Clothianidin + Deltamethrin Chlorfenapyr New IRS active ingredient Pyrethroid only Pyrethroid + PBO Pyrethroid + repurposed active ingredient (alphacypermethrin+ chlorfenapyr) Pyrethroid + IGR (alphacypermethrin+ pyriproxyfen) Next generation LLINs (new active ingredient) Targeted sugar baits designed to attract and kill mosquitoes (active ingredient 2) Targeted sugar baits designed to attract and kill mosquitoes (active ingredient 2) Targeted sugar baits designed to attract and kill mosquitoes (active ingredient 3) Larvicide Vector trap Eave tubes Spatial repellents Genetic control (gene drive/population suppression) IR S IT N TS B O th er 115 10 | Malaria preferred products and R&D pipeline An expanded vector control toolbox that includes larviciding, eave tubes and targeted sugar baits designed to attract and kill mosquitoes will further support IRM principles. This sugar bait, which is being developed to address the emerging threat of outdoor biting, is expected to achieve a prequalification listing in 2025. Other new interventions such as vector traps, eave tubes, spatial repellents and genetic control of mosquitoes are under different phases of development (Fig. 10.3). Preserving vector susceptibility is the foundation of malaria and other vector-borne disease control and requires an approach that can be addressed through an integrated vector management (IVM) framework. Compared with the situation a decade ago, today there are more vector control products available with different modes of action. However, these tools differ in their suitability for different disease transmission settings and geographies. How they are deployed in locally tailored IVM strategies will be critical in determining their effectiveness against malaria vectors and, ultimately, our ability to eradicate malaria. 10.5 MALARIA VACCINES The malaria vaccines currently in clinical development target various stages of the parasite life cycle, including the pre-erythrocytic stage (targeting sporozoite or liver stage antigens), the blood stage, and the sexual, sporogonic or mosquito stage (targeting pre-fertilization gametocytes/gametes or post- fertilization zygotes/ookinetes). A summary of the main candidates is presented in Table 10.2. Further details can be found in the WHO review of malaria vaccine clinical development on the GOHRD malaria vaccine dashboard (174). TABLE 10.2. Malaria vaccine candidates (in active development) by life cycle stage and R&D phase Source: WHO (2022) (174). Pre-erythrocytic stage P. falciparum ■ WHO recommended and prequalified - RTS,S/AS01 (circumsporozoite protein) ■ Phase 3 – R21/MatrixM (circumsporozoite protein) ■ Phase 2 – PfSPZ Vaccine (whole sporozoite); PfSPZ-CVac (PfSPZ challenge under chemoprophylaxis) ■ Phase 1 – VLPM01 (virus-like particle); rCSP/AP10-602 (circumsporozoite protein); PfGAP3-KO (genetically attenuated whole sporozoite); FMP013 and FMP014 (self-assembling nanoparticles); PfSPZ-GA1 (genetically attenuated whole sporozoite); DNA-ChAd63 PfCSP (heterologous prime-boost) P. vivax ■ Phase 2 – PvCSP (circumsporozoite protein); PvSPZ (whole sporozoite) Blood stage P. falciparum ■ Phase 2 – Rh5 (reticulocyte binding protein) ■ Phase 1 – BK-SE36 (PfSERA5 antigen) P. vivax ■ Phase 2 – PvDBP (Duffy-binding protein); Pf7G8 (chemically attenuated whole parasite); DNA-ChAd63 PfCSP PfAMA1 ME-TRAP (heterologous prime-boost) Sexual, sporogonic or mosquito stage (interrupting transmission) P. falciparum ■ Phase 2 – Pfs230D1M-EPA/AS01B (pre-fertilization); Pfs25M-EPA/AS01B (post-fertilization) ■ Phase 1 – Pfs25-IMX313/MatrixM (post-fertilization) P. vivax ■ Phase 1 – Pvs25-IMX313/MatrixM (post-fertilization) Malaria in pregnancy (targeting VAR2CSA antigens) ■ Phase 1 – PRIMVAC; PAMVAC DNA: deoxyribonucleic acid; P.: Plasmodium; WHO: World Health Organization. W O RL D M AL AR IA R EP O RT 2 02 2 116 11 KEY FINDINGS AND CONCLUSION The World malaria report 2022 tracks trends in the global malaria response and the impact of that response in the period 2000–2021. It highlights important barriers to progress, and the resilience needed to overcome them; it also provides the latest information on malaria intervention products that are in the R&D pipeline. 11.1 GLOBAL MALARIA RESPONSE AND TRENDS IN CASES AND DEATHS Malaria endemic countries, with support from partners, have largely held the line against further setbacks to malaria control in 2021. They have achieved this despite disruptions to malaria prevention, testing and treatment services owing to the COVID-19 pandemic and the often-devastating impacts of the pandemic on health, social and economic systems. In 2019, before the pandemic struck, there were an estimated 568 000 malaria deaths worldwide (Section 3). This estimate rose to 625 000 in the first year of the pandemic (2020) and then fell to 619 000 in 2021. During the 2 peak years of the pandemic (2020 and 2021), about 63 000 of the additional malaria deaths could be attributed to disruptions related to the COVID-19 pandemic. Although malaria cases continued to rise in 2021, the rate of increase was considerably lower than in the period 2019–2020. Cases stood at an estimated 247 million in 2021, compared with 245 million in 2020 and 232 million in 2019. Over the period 2020–2021, an estimated 13.4 million cases were attributed to disruptions to essential malaria services during the COVID-19 pandemic, mainly in moderate and high transmission countries. In the 11 HBHI countries, cases increased from 163 million to 168 million (Section 4). Many countries with a low burden of disease maintained effective malaria responses during the pandemic and continued their drive towards elimination (Section 4). Among the 25 countries and one territory identified as having the capacity to eliminate malaria by 2025 (E-2025), the number of malaria cases fell by about 83% in the period since 2010. Between 2020 and 2021, case reductions were observed in 12 countries: Bhutan, Botswana, the Dominican Republic, Mexico, Nepal, the Republic of Korea, Saudi Arabia, South Africa, Suriname, Thailand, Timor-Leste and Vanuatu (note that in South Africa, case detection rates in the community during the pandemic and overall case burden may be higher than reported). However, there was a 30% increase in cases in 2021 compared with 2020 in the E-2025 countries, owing to increases in the Comoros, Costa Rica, the Democratic People’s Republic of Korea, Ecuador, Eswatini, Guatemala, Honduras, Panama, Sao Tome and Principle and the territory of French Guiana. Countries in the GMS continued to successfully battle antimalarial drug resistance through lowering the case burden of P. falciparum. Across the region, P. falciparum has repeatedly developed drug resistance to artemisinin, the most effective medicine for treating malaria. In some areas, parasites have also developed resistance to the drugs that are commonly combined with artemisinin. Importantly, these mutations have not spread beyond the GMS, although drug resistance has evolved independently in parts of the WHO African Region. Between 2000 and 2021, the six GMS countries (Cambodia, China, the Lao People’s 117 Democratic Republic, Myanmar, Thailand and Viet Nam) reported a decrease of about 77% in indigenous malaria cases and of 94% in indigenous P. falciparum malaria cases (Section 4). During the pandemic period of 2020–2021, there was a 17% increase in indigenous malaria cases overall, but a 12% decline in P. falciparum cases. In 2021, Myanmar continued to account for most of the indigenous malaria cases (88%) and indigenous P. falciparum malaria cases (81%) in the subregion. The surveillance system has been disrupted in Myanmar, and estimated cases in 2021 may be lower than would have otherwise been reported. The impact of disruptions on intervention delivery varied for different tools and contexts. Even though the disruptions to essential services were greater in 2020 than in 2021, the absolute numbers of ITNs and RDTs distributed by NMPs were higher in 2020 than in any year since 2000 (Section 2 and Section 7). ACT distributions were also at a similar level to that seen in recent years before the pandemic. SMC distributions continued to increase in 2020 and 2021 relative to the period before the pandemic. The strong commitment by malaria endemic countries and partners to deliver vital interventions for malaria prevention, diagnosis and treatment led to countries reporting fewer deaths in 2021 than in 2020, and a much slower rate of increase in malaria cases in the same period. However, the pandemic continues to be disruptive; for example, six countries in the WHO African Region distributed less than 60% of their ITNs, with the high-burden countries of Nigeria and Uganda distributing only 53% and 41%, respectively (Section 2). 11.2 RISKS TO PROGRESS AGAINST MALARIA The overall progress towards malaria control began to slow after 2015, well before the COVID-19 pandemic (Section 3 and Section 8). Efforts to reach the milestones set by the GTS face even bigger challenges, particularly in the WHO African Region (Section 8) – an estimated 96% of all malaria deaths occurred in this region in 2021, with about four in five of those deaths being among children aged under 5 years (Section 3). Disruptions during the pandemic – combined with other humanitarian crises, health system challenges, restricted funding, rising biological threats and a decline in the effectiveness of core malaria control tools – threaten progress against malaria (Section 3, Section 6 and Section 9). By 2021, global malaria case incidence was 59 cases per 1000 population at risk, against a target of 31 cases per 1000 – off track by 47%. Also, malaria deaths per 100 000 population at risk stood at 14.8 against a target of 7.8 – also off track by 47%. In 2021, the WHO African Region was off track for both the morbidity and mortality GTS milestones, by 45% and 47%, respectively (Section 8). Gaps in access to equitable and quality care remain an important challenge to malaria. In sub-Saharan Africa, care was not sought for about a third of children who had fever in the 2 weeks before household surveys (Section 7). An analysis of inequality in malaria risks and access to interventions showed that although children in the poorest households were five times more likely to be infected with malaria, they were also two times less likely to access care (175). Among those who sought care, about 28% used the private sector, incurring costs that put considerable strain on the poorest households. Although nearly 70% of children with fever were cared for in the public sector, only about 1% were attended to by community health workers. The chances of a child being tested for malaria after seeking treatment nearly doubled in the period 2016–2021 compared with the period 2011–2015; nevertheless, about 42% of children with fever for whom treatment was sought did not receive a parasitological test. Comparing overall use of ACTs among these children to use of ACTs in children who were tested, it appears that most children were given ACTs without a test. This is supported by recent published literature showing important challenges with the quality of malaria case management; despite the increasing rollout of RDTs, many patients are still treated with ACTs without testing, as seen, for example, in sites in Angola (176), Ghana (177), Guinea (178) and Nigeria (179). The COVID-19 pandemic and disease outbreaks, serious climate events and conflict contributed to the global economy contracting by 3.4% in 2020 and have limited countries’ fiscal space to address multiple pressing concerns (Section 2 and Section 6). In 2020, 70% of malaria endemic LMIC experienced a shock in their annual real GDP. The GDP of 34 of these countries, half of which are in the WHO African Region, shrank by more than 1%. Although the global economy expanded by 5.5% in 2021, GDP remained lower in most countries than it was in 2019 (Section 6). Total funding in 2021 was estimated at US$ 3.5 billion, an increase from US$ 3.3 billion in 2020 and US$ 3.0 billion in 2019. However, investments in 2021 were well below the estimated US$ 7.3 billion that was required globally to stay on track for the GTS milestones. The funding gap between the amount invested and the resources needed has continued to widen, particularly over the past 3 years, increasing 118 W O RL D M AL AR IA R EP O RT 2 02 2 from a gap of US$ 2.6 billion in 2019, to US$ 3.5 billion in 2020 and US$ 3.8 billion in 2021. Since the COVID-19 pandemic and the war in the Ukraine, NMPs are being hit by rising costs associated with fuel, local distribution and supply chains. In addition, the cost of commodities used for malaria control are expected to rise, as newer ITNs and diagnostics need to be purchased to counter increasing resistance (Section 6). With the changing economic environment and the seventh Global Fund replenishment not achieving the expected targets (despite historic contributions by countries and partners), the challenges in the funding space for the malaria response will continue to increase. Although more efficient procurement processes and market priming will help to keep global commodity prices within an affordable range, countries will also need greater efficiencies in implementation. Here, data-informed and locally tailored approaches will be essential. Under the HBHI approach, countries are increasingly using their data through a subnational tailoring process to identify optimal mixes of interventions (180). However, more work is needed to improve both data quality and data use. WHO has developed a toolkit to assess the status of malaria surveillance systems (56), and a few countries have used early versions of these tools to measure the performance of their systems, with support from partners (Section 5). Data from Burkina Faso, the Democratic Republic of the Congo and Ghana showed impressive reporting rates from the public sector of above 90%. However, the completeness of core malaria variables from health facility registers was modest, ranging from 40% in Ghana to 74% in the Democratic Republic of the Congo. More concerning was the concordance between aggregate reported data and health facility registers, which was less than 40% in all countries (Table 5.2). To accelerate progress in Pillar 3 of the GTS (2), similar assessments are needed to design national surveillance action plans, supported by considerable investment across the surveillance cycle. In most malaria endemic countries, such investment should be directed at strengthening health information systems more broadly. Even where access to interventions is adequate, the effectiveness and performance of core malaria control tools face considerable challenges that threaten progress against malaria. Long-lasting ITNs treated with the insecticide pyrethroid alone have been one of the most important tools in the fight against malaria. Since 2005, over 2 billion such ITNs have been distributed for malaria prevention globally. A modelling analysis suggests that ITNs drove the declines in malaria seen from 2005 to 2015, especially in higher burden settings. Since 2015, however, progress against malaria has slowed and this world malaria report includes an extended analysis that describes the threats to this important prevention tool and highlights the collective impact of these challenges. The main challenges are insecticide resistance, physical and chemical durability of ITNs, allocation efficiency of delivery systems, retention of nets by households and the use of nets by household members. Although the data are widely variable by setting, analysis of pooled data suggest a median ITN retention time of about 1.9 years (69). A modelling analysis (Section 9.5.4) suggests that, shortly after net distribution, lower killing due to insecticide resistance has a large impact on overall net effectiveness. By the end of the time series, however, the waning of both insecticide and physical durability play the largest roles in reducing net effectiveness. When combined with barriers to ITN access, use and retention, the impact of the ITN campaign wanes dramatically by the end of the third year. In addition, the changing behaviour of mosquitoes to avoid ITNs – with changes to when and where they bite, feed and rest – limits the overall impact of ITNs, potentially increasing the risks of residual transmission. An increasing proportion of P. falciparum parasites no longer express the Pfhrp2/3 proteins most widely used to detect malaria through RDTs. Parasites that no longer produce HRP2 or the related HRP3 protein can escape detection by RDTs, presenting a major threat to reliable diagnosis. In 2021, nearly 80% of RDTs in use were based on HRP2 detection. Deletions in the genes for HRP2/3 proteins were first reported in 2010 in the Peruvian Amazon basin, and have since been documented in Asia, the Middle East, and central, east, southern and west Africa. Prevalence has reached 80% among symptomatic patients in Djibouti, Eritrea and Peru, demonstrating that these parasites can become dominant in the population, increasing the risk that missed cases will progress to severe disease and death. Recent studies have confirmed the emergence of P. falciparum artemisinin partial resistance in Africa (Section 9.2). There are also worrying signs of resistance to the partner drugs used in combination with artemisinin in ACTs. Resistance to ACT partner drugs has prompted changes in first-line treatment of P. falciparum malaria in countries in the GMS, the Horn of Africa and south Asia. Artemisinin partial resistance is associated with mutations in the parasite’s PfKelch13 gene, and the proportion of parasites carrying these mutations is expanding in Eritrea, Papua New Guinea, Rwanda and Uganda. Treatment failure rates in the countries affected by artemisinin partial resistance remained below 10%, and the ACTs remain highly efficacious. However, the rising proportion of parasites with mutations associated with artemisinin partial resistance is worrying. An. stephensi, a mosquito that can transmit both P. falciparum and P. vivax parasites, is posing an 119 11 | KEY FINDINGS AND CONCLUSION added challenge to disease control efforts in the WHO African Region. It can thrive in urban environments and is resistant to many of the insecticides used in public health. Originally native to parts of south Asia and the Arabian Peninsula, An. stephensi has now been reported in five countries in the WHO African Region. Although this mosquito can also adapt to rural settings, the WHO African Region is rapidly urbanizing, with 40% of the population already living in urban areas, increasing concerns of malaria outbreaks if An. stephensi spreads. 11.3 ENHANCING RESILIENCE The concept of health system resilience has become widespread as a result of the excess mortality associated with disruptions to health services during the Ebola outbreak in west Africa and the ongoing COVID-19 pandemic. Although different definitions of resilience exist, in the context of health systems, the overall focus is the ability to effectively manage change or the impact of extreme events, while maintaining essential health services. Despite agreement on the importance of resilience, there has been a relative failure to decisively translate the objectives and attributes of resilience into national policy, planning and service operations. In spite of the many challenges, malaria endemic countries and partners have demonstrated some good examples of resilience through the worst of times. Even before the COVID-19 pandemic, countries were dealing with a broad range of challenges. However, a more predictable and systematic approach to building resilience is needed because of the increasing frequency of natural and human-made disasters and conflict, and the threat of climate change and future disease pandemics at a time when global financial resources for health and malaria are constrained. There have been some useful lessons from the COVID-19 response, which illustrate how countries can make intentional efforts to build health system resilience to respond to a wide range of shocks and demands, using recovery as a platform and harnessing current political and economic impetus. At the start of the pandemic, as reported in the World malaria report 2021 (180), WHO convened partners to contribute to a malaria and COVID-19 response that included the development of guidance to countries for tailoring the malaria response to the COVID-19 pandemic. This was followed by concerted efforts by donors, especially the Global Fund and the US President’s Malaria Initiative (PMI), to provide additional funding or increase flexibilities within existing funding to help countries deliver essential malaria services. In March 2021, the government of the USA provided a US$ 3.7 billion emergency contribution to the Global Fund, to support the COVID-19 Response Mechanism (C19RM) (181). The C19RM has been critical to reinforcing the response by supporting COVID-19- related adaptation of programmes to fight HIV, TB and malaria, and by strengthening health and community systems. In September 2022, the World Bank established the financial intermediary fund (FIF), with technical leadership by WHO, to support LMIC in pandemic prevention, preparedness and response (182) (PPR). Building stronger and more resilient health systems is an important part of the FIF’s work programme and, to this end, other major diseases such as malaria are likely to benefit. Closer synergies between disease programmes and institutions responsible for pandemic PPR is essential. To support a unified approach to recovery and to build resilience, WHO outlined seven policy recommendations in its position paper, including focused support to countries with settings that are fragile or affected by conflict or violence (183). Although many definitions of resilience focus on unforeseen and extreme events, such as the COVID-19 pandemic, WHO is also providing support and guidance to countries to help them withstand some of the more predictable and chronic stresses that threaten the malaria response. Thus, WHO has increased the transparency and flexibility of and the access to its malaria recommendations, to help countries to easily adapt the recommendations to their local context (Section 2.2) (8). Also, in September 2022, WHO launched an initiative to stop the spread of the An. stephensi malaria vector in Africa (Section 2.4) (18). This initiative calls for collaboration, improved surveillance, information exchange, normative guidance and research as the key to the response. In October 2022, WHO launched the Global framework for the response to malaria in urban areas (20), along with UN-Habitat (Section 2.5). By 2050, almost 70% of people globally will live in cities, and unplanned urbanization is likely to result in a malaria disease burden that disproportionately affects the urban poor. The new framework provides guidance for city leaders, health programmes and urban planners for the control and elimination of malaria. In November 2022, WHO launched a new strategy to curb antimalarial drug resistance in the WHO African Region (Section 2.3) (10). The strategy aims to tackle drug resistance through, among other measures, tracking its spread, identifying the populations most at risk, and developing viable alternative treatments through four interconnected pillars. Global investment in surveillance systems has expanded the digitization of health information systems in many countries (Section 5.1). However, 120 W O RL D M AL AR IA R EP O RT 2 02 2 the overall status of surveillance systems, especially in moderate and high transmission countries where the malaria burden is concentrated, remain weak (Section 5.2). Strengthening surveillance will require greater investment not only in digital solutions, but also in greater health workforce capacity and synergies across disease and health programmes. Being ready to address these foreseen challenges and less predictable extreme events will require a multidisciplinary and multisectoral response with trusted national leadership and well-functioning, equitable and resilient health systems. Such systems will need to be built on a platform of primary health care and essential public health functions that encompasses the importance of a resilient community response that is able, and supported, to withstand, adapt to and recover from adversity. The revised GTS recognizes that these resilient systems underpin the overall success of the malaria response, delivering quality services to all those in need and effectively adapting to disruptive events – such as epidemics, pandemics and other natural disasters, including climate change. The benefits of such resilient systems would extend to all sectors and the economy. Despite uncertainties about exactly how climate change will affect malaria, it will certainly affect the geographical range, intensity and seasonality of vector-borne diseases. More broadly, climate change will directly affect human health across the world and threaten the capacity of health systems to manage and protect people’s health – particularly in areas of conflict and disaster, as climate change compounds these challenges. The WHO Operational framework for building climate resilient health systems (184) provides a wide range of guidance to support countries on health adaptation to climate change. 11.4 R&D PRODUCTS PIPELINE Despite recent setbacks, investment in R&D has played a crucial role in the success against malaria since 2000 (Section 10). Over the past 20 years, the backbone of the development of the malaria response has been RDTs to accurately diagnose malaria in primary health care settings and by community health workers, ACTs to respond to resistance to chloroquine and SP, and ITNs and eventually LLINs as vector control tools that were effective and relatively easy to scale up. In addition, new chemicals have been developed to address resistance of mosquitoes to the insecticides used in IRS. The most widely used RDT, which detects the parasite’s HRP2/3 protein, is becoming less effective as the malaria parasite mutates; however, there is hope that new diagnostic tests will be developed to replace current tests (Section 9.1). Currently, WHO recommends a shift to non-HRP2-based tests in countries where more than 5% of symptomatic P. falciparum infections have the genetic mutation that allows them to escape detection (72). Researchers are also pursuing the development of diagnostic tests that use alternative biomarkers (Section 10.2). In addition, noninvasive diagnosis using saliva and urine is a growing area of investigation, with potential for rapid screening outside of conventional medical settings. Other efforts seek to improve diagnostic tools related to P. vivax, which would allow the safe use of medical treatments for radical cure of the disease. Global health organizations and donors are supporting diversification of the diagnostic pipeline. Before 2020, only two suppliers of RDTs shared over 80% of the market. Procurement strategies by PMI and the Global Fund diversified this landscape, leading to seven new tests being approved in 2020. Currently, there are 19 prequalified RDTs from seven manufacturers, with a further eight tests from five suppliers under prequalification review. There are several ongoing innovations in malaria medicines for case management and chemoprevention (Section 10.3). Delivering non-ACT treatment options, as a contingency against the emergence of artemisinin resistance, is a global priority. A summary by MMV of medicines under development includes triple ACT that relies on a combination of the short-acting artemisinin with two long-acting partner drugs to mitigate the risk of resistance, and next-generation antimalarial treatments that use non-artemisinin chemical entities. Four novel drug combinations are now in clinical trials, and a host of candidate molecules are in early development; there are also new treatments to prevent malaria, particularly in pregnant women and in children at risk of infection. Work is ongoing to use currently approved treatments in new ways, with different schedules, and to recombine approved antimalarial drugs novel ways; there is also a longer term strategy to develop novel drugs for chemoprevention. Passive immunization with mAbs is a promising area of chemoprevention. A recent study suggests that human antibodies to malaria can be used to provide short-term protection to populations at high risk of clinical malaria (185). Innovations in vector control suggest a promising pipeline of products. Already, as pyrethroid resistance becomes more widespread and reduces the effectiveness of LLINs, the R&D pipeline has resulted in a new generation of ITNs to mitigate the development of resistance and improve effectiveness (Section 10.4). For example, based on trial evidence showing that pyrethroid-PBO nets were more effective than pyrethroid-only nets in lowering parasite prevalence, 121 11 | KEY FINDINGS AND CONCLUSION WHO has recommended pyrethroid-PBO nets for use in areas of high pyrethroid resistance (9). In 2021, 46% of the 220 million ITNs delivered were PBO nets (25% more than in 2020), with plans for further scale-up (Section 7). Promising results from pyrethroid + chlorfenapyr ITN trials also showed a marked improvement in preventing malaria compared with pyrethroid-only nets (143), and WHO is now reviewing evidence for a potential recommendation for its use. Overall, a summary by WHO and IVCC shows that there are 28 vector control products in the R&D pipeline, including new ITNs and IRS, targeted sugar baits, spatial repellents, endectocides and genetic modifications of the mosquito to limit their ability to transmit malaria (Section 10.4). In October 2021, WHO recommended the RTS,S/AS01 malaria vaccine for the prevention of P. falciparum malaria in children living in regions with moderate to high transmission – broader vaccine rollout is expected later in 2023, although supply will initially be limited. Gavi will facilitate the rollout to Gavi- eligible countries. A host of vaccine candidates are in development. As with RTS,S, many of them target the parasite before it enters the human liver where it can quickly multiple. The most advanced of these vaccine candidates is R21 (186). Other vaccines in development target later stages in the parasite’s life cycle, target P. vivax or seek to protect women during pregnancy. 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Annexes Annexes Annex 1 - Data sources and methods Annex 2 - Number of ITNs distributed through campaigns in malaria endemic countries, 2020–2022 Annex 3 - Regional profiles > A. WHO African Region a. West Africa b. Central Africa c. Countries with high transmission in east and southern Africa d. Countries with low transmission in east and southern Africa > B. WHO Region of the Americas > C. WHO Eastern Mediterranean Region > D. WHO South-East Asia Region > E. WHO Western Pacific Region Annex 4 - Data tables and methods > A. Policy adoption, 2021 > B. Antimalarial drug policy, 2021 > C. Funding for malaria control, 2019–2021 > D. Commodities distribution and coverage, 2019–2021 > Ea. Household survey results, 2017–2021, compiled through STATcompiler > Eb. Household survey results, 2017–2021, compiled through WHO calculations > F. Population denominator for case incidence and mortality rate, and estimated malaria cases and deaths, 2000–2021 > G. Population denominator for case incidence and mortality rate, and reported malaria cases by place of care, 2021 > H. Reported malaria cases by method of confirmation, 2010–2021 > I. Reported malaria cases by species, 2010–2021 > J. Reported malaria deaths, 2010–2021 > K. Methods for Tables A-D-G-H-I-J 131 ANNEx 1 – DATA SOURCES AND METhODS Fig. 2.1. Response interventions clustered into the strategy’s four pillars to address antimalarial drug resistance in Africa Figure extracted and adapted from the 2022 Strategy to respond to antimalarial drug resistance in Africa (1). Fig. 2.2. Aims of the WHO initiative to support an effective response to An. stephensi on the African continent Figure extracted and adapted from the World Health Organization (WHO) brochure WHO initiative to stop the spread of Anopheles stephensi in Africa (2). Fig. 2.3. Building blocks of the Global framework for the response to malaria in urban areas Figure extracted and adapted from the Global framework for the response to malaria in urban areas (3). Fig. 2.4. People in humanitarian need in malaria endemic countries as of December 2021 Figure developed from data extracted from the Global humanitarian overview 2022 (4). Fig. 2.5. ITNs planned and distributed during mass campaigns in 52 malaria endemic countries, 2020–2022 The figure summarizes the planned insecticide-treated mosquito net (ITN) distributions via mass campaigns in 2020 and 2021 and their spillover into 2022. In countries where no data were reported on planned distribution or where ITNs distributed were more than ITNs planned for distribution, adjustments were made so that ITNs planned for distribution are equal to ITNs distributed in the respective year. Data were submitted by countries to WHO, the Alliance for Malaria Prevention, the RBM Partnership to End Malaria and the Global Fund. Fig. 2.6. Results from WHO surveys on disruptions to malaria diagnosis and treatment services during the COVID-19 pandemic from three rounds of surveys in 2020–2021 Data used in these graphs are based on rounds 1, 2 and 3 of the essential health services (EHS) disruption surveys implemented by the WHO Integrated Health Services Department to determine the effects of the coronavirus (COVID-19) pandemic (5–7). Fig. 2.7. Number of malaria tests performed, by WHO region, in 86 malaria endemic countries Countries submitted data to WHO. Fig. 3.1. Countries with indigenous cases in 2000 and their status by 2021 Data on the number of indigenous cases (an indicator of whether countries are endemic for malaria) were as reported to WHO by national malaria programmes (NMPs). Countries with 3 consecutive years of zero indigenous cases are considered to have eliminated malaria. Table 3.1. Global estimated malaria cases and deaths, 2000–2021 a) Global estimated malaria cases For each country or area, the number of malaria cases was estimated by one of the three methods described below. Method 1 Method 1 was used for countries and areas outside the WHO African Region, and for low transmission countries and areas in the African Region as follows: Afghanistan, Bangladesh, the Bolivarian Republic of Venezuela, Botswana, Brazil, Cambodia, Colombia, the Dominican Republic, Eritrea, Ethiopia, French Guiana, the Gambia, Guatemala, Guyana, Haiti, Honduras, India, Indonesia, the Lao People’s Democratic Republic, Madagascar, Mauritania, Myanmar, Namibia, Nepal, Nicaragua, Pakistan, Panama, Papua New Guinea, Peru, the Philippines, the Plurinational State of Bolivia, Rwanda, Senegal, Solomon Islands, Timor-Leste, Vanuatu, Viet Nam, Yemen and Zimbabwe. Estimates were made by adjusting the number of reported malaria cases for completeness of reporting, the likelihood that cases were parasite positive, and the extent of health service use. The procedure, which is described in the World malaria report 2008 (8), combines national data annually reported by NMPs (i.e. reported cases, reporting completeness and likelihood that cases are parasite positive) with data obtained from nationally representative household surveys on health service use among children aged under 5 years, which was assumed to be representative of the service use in all ages. Briefly: T=(a + (c x e))/d x (1+f/g+(1−g−f)/2/g) where: a is malaria cases confirmed in the public sector b is suspected cases tested c is presumed cases (not tested but treated as malaria) d is reporting completeness e is test positivity rate (malaria positive fraction)=a/b f is the fraction seeking treatment in the private sector g is the fraction seeking treatment in the public sector Factor to adjust for those not seeking treatment: (1-g-f) Cases in the public sector: (a + (c x e))/d Cases in the private sector: (a + (c x e))/d x f/g To estimate the uncertainty around the number of cases, the test positivity rate was assumed to have a normal distribution centred on the test positivity rate value and standard deviation – defined as 0.244 × f0.5547 and truncated to be in the range 0, 1. Reporting completeness (d), when reported as a range or below 80%, was assumed 132 W O RL D M AL AR IA R EP O RT 2 02 2 to have one of three distributions, depending on the value reported by the NMP. If the value was reported as a range greater than 80%, the distribution was assumed to be triangular, with limits of 0.8 and 1.0, and the peak at 0.8. If the value was more than 50% but less than or equal to 80%, the distribution was assumed to be rectangular, with limits of 0.5 and 0.8. Finally, if the value was less than or equal to 50%, the distribution was assumed to be triangular, with limits of 0 and 0.5, and the peak at 0.5 (9). If the reporting completeness was reported as a value and was more than 80%, a beta distribution was assumed, with a mean value of the reported value (maximum of 95%) and confidence intervals (CIs) of 5% around the mean value. The fraction of children brought for care in the public sector and in the private sector was assumed to have a beta distribution, with the mean value being the estimated value in the survey and the standard deviation being calculated from the range of the estimated 95% CIs. The fraction of children not brought for care was assumed to have a rectangular distribution, with the lower limit being 0 and the upper limit calculated as 1 minus the proportion that were brought for care in the public and private sectors. The three distributions (fraction seeking treatment in the public sector, fraction seeking treatment in the private sector only and fraction not seeking treatment) were constrained to add up to 1. Sector-specific care seeking fractions were linearly interpolated between the years that had a survey, and were extrapolated for the years before the first or after the last survey. The parameters used to propagate uncertainty around these fractions were also imputed in a similar way or, if there was no value for any year in the country or area, were imputed as a mixture of the distributions of the region for that year. CIs were obtained from 10 000 draws of the convoluted distributions. The data were analysed using R statistical software, using the convdistr R package to propagate uncertainty and manage distributions (10). For India, the values were obtained at subnational level using the same methodology but adjusting the private sector for an additional factor because of the active case detection activities, estimated as the ratio of the test positivity rate in active case detection divided by the test positivity rate for passive case detection. This factor was assumed to have a normal distribution, with mean value and standard deviation calculated from the values reported in 2010. An additional adjustment was applied in several states in India for 2020 and 2021, to control for the reductions in reported testing rates associated with disruptions in health services related to the COVID-19 pandemic. The states with reductions in testing rates below those expected (defined as a change in testing rates of more than 10% observed between 2018 and 2019) in 2020 were Bihar, Chandigarh, Chhattisgarh, Dadra and Nagar Haveli, Delhi, Goa, Jharkhand, Karnataka, Puducherry, Punjab, Uttar Pradesh, Uttarakhand and West Bengal. In 2021, the states with reductions in testing rates were Assam, Chandigarh, Chhattisgarh, Daman and Diu, Delhi, Goa, Himachal Pradesh, Karnataka, Kerala, Manipur, Puducherry, Punjab, Uttar Pradesh, Uttarakhand and West Bengal. In these states, the excess number of indigenous cases expected in the absence of diagnostic disruptions was calculated by estimating the number of additional tests that would have been conducted if testing rates were similar to those observed in 2019 then applying the test positivity ratio observed in 2019 (or in 2020, for Delhi and Jharkhand, or in 2021 for Delhi and Puducherry) to this number. The malaria burden in countries outside the WHO African Region was affected by the COVID-19 pandemic in different ways. In several countries, the movement disruptions led to transmission reductions; in other cases, testing rates remained unchanged. This made it challenging to apply a single source of data for correction to all countries, considering also that it was difficult to relate the reported data to the EHS response. No adjustment for private sector treatment seeking was made for the following countries and areas because they report cases from the private and public sector together: Bangladesh, the Bolivarian Republic of Venezuela, Botswana, Brazil, Colombia, the Dominican Republic, French Guiana, Guatemala, Guyana, Haiti, Honduras, Myanmar (since 2013), Nicaragua, Panama, Peru, the Plurinational State of Bolivia and Rwanda. Method 2 Method 2 was used for high transmission countries in the WHO African Region and for countries in the Eastern Mediterranean Region in which the quality of surveillance data did not permit a robust estimate from the number of reported cases. These countries were Angola, Benin, Burkina Faso, Burundi, Cameroon, the Central African Republic, Chad, the Congo, Côte d’Ivoire, the Democratic Republic of the Congo, Equatorial Guinea, Gabon, Ghana, Guinea, Guinea-Bissau, Kenya, Liberia, Malawi, Mali, Mozambique, the Niger, Nigeria, Sierra Leone, Somalia, South Sudan, the Sudan, Togo, Uganda, the United Republic of Tanzania and Zambia. In this method, estimates of the number of malaria cases were derived from information on parasite prevalence obtained from household surveys. First, data on parasite prevalence from almost 60 000 survey records were assembled within a spatio-temporal Bayesian geostatistical model, together with environmental and sociodemographic covariates, and data distribution on interventions such as ITNs, antimalarial drugs and indoor residual spraying (IRS) (11) that are updated yearly to review the model. The geospatial model enabled predictions of Plasmodium falciparum prevalence in children aged 2–10 years, at a resolution of 5 × 5 km2, throughout all malaria endemic WHO African Region countries for each year from 2000 to 2020. Second, an ensemble model was developed to predict malaria incidence as a function of parasite prevalence (12). The model was then applied to the estimated parasite prevalence, to obtain estimates of the malaria case incidence at 5 × 5 km2 resolution for each year from 2000 to 133 ANNEx 1 – DATA SOURCES AND METhODS 2021.1 Data for each 5 × 5 km2 area were then aggregated within country and regional boundaries, to obtain both national and regional estimates of malaria cases (14). In 2020 and 2021, additional cases estimated using this method were added to account for the disruptions in malaria prevention, diagnostic and treatment services as a result of the COVID-19 pandemic and other events that occurred during this period. Disruption information was reported per country and was obtained from the national pulse surveys on continuity of EHS during the COVID-19 pandemic conducted by WHO (first round in May–July 2020, second in January–March 2021 and third in November–December 2021) (5-7). The medium, minimum and maximum (with a limit of 50%) values of the ranges provided by countries to define disruptions were used to quantify the percentage of malaria service disruptions. This information was integrated into the estimates by applying an approach previously used for assessing the impacts of interventions on malaria burden through the creation of counterfactual burden estimates for scenarios with varying levels of intervention coverage. It was assumed that COVID-19-related disruptions to health care manifested themselves as reduced treatment seeking for malaria and thus reduced effective treatment with an antimalarial drug. The counterfactual estimates were then aligned, per country, with the estimates from the pulse surveys to produce a set of estimates, adjusted for the COVID-19 pandemic, for 2020 and 2021. A comparison of the result of both models (in the presence and absence of diagnosis and treatment disruptions, with both models accounting for disruptions to prevention interventions) made it possible to estimate the number of additional cases associated with the disruptions that occurred during the COVID-19 pandemic. For countries for which the estimates with the updated spatio-temporal model were considerably different from previous estimates without addition of new data, or where trends in reported cases were significantly different in direction (Angola, Central African Republic, Chad, the Democratic Republic of the Congo, Mali, South Sudan, the Sudan, Uganda and Zambia), the case series published in the World malaria report 2020 (14) were used until 2020, adjusting for the changes in population at risk values. The values for 2021 were estimated by applying the change rate between the cases estimated using the spatio- temporal model of incidence between 2020 and 2021, and adjusting for population changes between these two years. Method 3 For most of the elimination countries and countries at the stage of prevention of reintroduction, the number of indigenous cases registered by NMPs are reported without further adjustments. The countries in this category were Algeria, Argentina, Armenia, Azerbaijan, Belize, Bhutan, Cabo Verde, China, the Comoros, Costa Rica, the Democratic People’s Republic of Korea, Djibouti, Ecuador, Egypt, El Salvador, Eswatini, Georgia, Iran (Islamic 1 See the Malaria Atlas Project website for methods on the development of maps (13). Republic of), Iraq, Kazakhstan, Kyrgyzstan, Malaysia, Mexico, Morocco, Oman, Paraguay, the Republic of Korea, Sao Tome and Principe, Saudi Arabia, South Africa, Sri Lanka, Suriname, the Syrian Arab Republic, Tajikistan, Thailand, Türkiye, Turkmenistan, the United Arab Emirates and Uzbekistan. Country-specific adjustments For some years, information for certain countries was not available or could not be used because it was of poor quality. For countries in this situation, the number of cases was imputed from other years where the quality of the data was better (adjusting for population growth), as follows: for Afghanistan, values for 2000–2001 were imputed from 2002–2003; and for Bangladesh, values for 2001–2005 were imputed from 2006–2008. For Ethiopia, values for 2000–2019 were taken from a mixed distribution between values from Method 1 and Method 2 (50% from each method). For the Gambia, values for 2000–2010 were imputed from 2011–2013; for Haiti, values for 2000– 2005, 2009 and 2010 were imputed from 2006–2008; for Indonesia, values for 2000–2003 and 2007–2009 were imputed from 2004–2006; and for Mauritania, values for 2000–2010 were imputed from a mixture of Method 1 and Method 2, starting with 100% values from Method 2 for 2001–2002, with that percentage decreasing to 10% of Method 1 in 2010. For Myanmar, values for 2000–2005 were imputed from 2007–2009; and for Namibia, values for 2000 were imputed from 2001–2003 and values for 2012 were imputed from 2011 and 2013. For Pakistan, values for 2000 were imputed from 2001–2003; and for Papua New Guinea, values for 2012 were imputed from 2009– 2011. For Rwanda, values for 2000–2006 were imputed from a mixture of Method 1 and Method 2, starting with 100% values from Method 2 in 2000, with that percentage decreasing to 10% in 2006. For Senegal, values for 2000– 2006 were imputed from a mixture of Method 1 and Method 2, with 90% of Method 2 in 2000, decreasing to 10% of Method 2 in 2006. For Thailand, values for 2000 were imputed from 2001–2003; for Timor-Leste, values for 2000– 2001 were imputed from 2002–2004; and for Zimbabwe, values for 2000–2006 were imputed from 2007–2009. Estimation of P. vivax cases The number of malaria cases caused by P. vivax in each country was estimated by multiplying the country’s reported proportion of P. vivax cases (computed as 1 − P. falciparum) by the total number of estimated cases for the country. For countries where the estimated proportion was not 0 or 1, the proportion of P. falciparum cases was assumed to have a beta distribution and was estimated from the proportion of P. falciparum cases reported by NMPs. Population at risk To transform malaria cases into incidence, an estimate of population at risk was used. The proportion of the population at high, low or no risk of malaria was provided 134 W O RL D M AL AR IA R EP O RT 2 02 2 by NMPs. Population at risk was estimated as the population at risk in high endemic areas and half of the population at risk in low endemic areas. This was applied to the latest United Nations (UN) population estimates available (2022, an update from the estimates used for the world malaria reports for 2020 and 2021), to compute the number of people at risk of malaria. This number was sustained over time to ensure comparability of incidence estimates across years in the same cohort of countries that had been endemic since 2000. The population at risk at regional and global level was aggregated; it included the population of all endemic countries since 2000, even though some of them achieved elimination during this time. b) Global estimated malaria deaths The number of malaria deaths was estimated using methods from Category 1, 2 or 3, as outlined below. Category 1 method The Category 1 method was used for low transmission countries and areas, both within and outside the WHO African Region: Afghanistan, Bangladesh, the Bolivarian Republic of Venezuela, Botswana, Cambodia, the Comoros, Djibouti, Eritrea, Eswatini, Ethiopia, French Guiana, Guatemala, Guyana, Haiti, Honduras, India, Indonesia, the Lao People’s Democratic Republic, Madagascar, Myanmar, Namibia, Nepal, Pakistan, Papua New Guinea, Peru, the Philippines, the Plurinational State of Bolivia, Solomon Islands, Somalia, the Sudan, Timor-Leste, Vanuatu (between 2000 and 2012), Viet Nam, Yemen and Zimbabwe. A case fatality rate of 0.256% was applied to the estimated number of P. falciparum cases, which represents the average of case fatality rates reported in the literature (15-17) and rates from unpublished data from Indonesia, 2004–2009.1 The proportion of deaths followed a rectangular distribution of between 0.01% and 0.40% – the minimum and maximum values available that were reported. A case fatality rate of 0.0375% was applied to the estimated number of P. vivax cases, representing the midpoint of the range of case fatality rates reported in a study by Douglas et al. (18), following a rectangular distribution of between 0.012% and 0.063%. Following the nonlinear association explained for the Category 2 method below, the proportion of deaths in children aged under 5 years was estimated as: Proportion of deathsunder 5=–0.2288 × Mortalityoverall2 + 0.823 × Mortalityoverall + 0.2239 where Mortalityoverall is the number of estimated deaths over the estimated population at risk per 1000 (see Annex 4-G for national estimates of population at risk). Category 2 method The Category 2 method was used for countries in the WHO African Region with a high proportion of deaths due to malaria: Angola, Benin, Burkina Faso, Burundi, Cameroon, the Central African Republic, Chad, the Congo, Côte d’Ivoire, the Democratic Republic of the Congo, Equatorial 1 Dr Ric Price, Menzies School of Health Research, Australia, personal communication (November 2014). Guinea, Gabon, the Gambia, Ghana, Guinea, Guinea- Bissau, Kenya, Liberia, Malawi, Mali, Mauritania, Mozambique, the Niger, Nigeria, Rwanda, Senegal, Sierra Leone, South Sudan, Togo, Uganda, the United Republic of Tanzania and Zambia. With this method, child malaria deaths were estimated using a new multinomial Bayesian least absolute shrinkage and selection operator (LASSO) model that was reviewed by the WHO Maternal and Child Health Epidemiology Estimation Group (MCEE) in 2021, to produce updated estimates of cause of death (CoD) in children aged 1–59 months between 2000 and 2019 (19). Mortality estimates (and 95% CIs) were derived for eight causes of post-neonatal death (pneumonia, diarrhoea, malaria, tuberculosis, meningitis, injuries, pertussis and other disorders), four causes arising in the neonatal period (prematurity, birth asphyxia and trauma, sepsis and other conditions of the neonate) and other causes (e.g. malnutrition). Deaths due to measles, unknown causes and HIV/AIDS were estimated separately. The resulting cause-specific estimates were adjusted, country by country, to fit the estimated all-cause mortality envelope of 1–59 months (excluding HIV/AIDS and measles deaths) for corresponding years. The number of malaria deaths among children aged under 5 years was calculated by applying the country- specific yearly malaria CoD fraction to the all-cause mortality envelope of 1–59 months estimated by the UN Inter-agency Group for Child Mortality Estimation (20). The same malaria CoD fractions observed in 2019 were used in 2020 and 2021. It was considered that the number of deaths follows a rectangular distribution, with limits being the estimated 95% CI. The malaria mortality rate in children aged under 5 years estimated with this method was then used to infer malaria- specific mortality in those aged 5 years and over, using the relationship between levels of malaria mortality in a series of age groups and the intensity of malaria transmission (21), and assuming a nonlinear association between under- 5-years mortality and over-5-years mortality, as follows: Proportion of deathsover 5=–0.293 × Mortalityunder 52 + 0.8918 × Mortalityunder 5 + 0.2896 where Mortalityunder 5 is estimated from the number of deaths from the MCEE-estimated malaria cause of death fractions and the all-cause under-5 malaria death envelope. In 2020 and 2021, additional malaria deaths estimated using this method were included to account for the disruptions in malaria diagnostic and treatment services as a result of the COVID-19 pandemic. Country-specific mortality inflation ratios were calculated by comparing the malaria mortality estimates for 2020 and 2021, in the presence and absence of diagnosis and treatment disruptions from MAP’s malaria mortality estimates (results not presented in the report, derived from the 135 ANNEx 1 – DATA SOURCES AND METhODS malaria incidence estimates), with both estimates accounting for disruptions to prevention interventions. Inflation ratios were then applied to the number of malaria deaths for 2020 and 2021 to estimate the number of deaths expected, considering the reported disruptions. Category 3 method For the Category 3 method, the number of indigenous malaria deaths registered by NMPs is reported without further adjustments. This category was used in the following countries: Algeria, Argentina, Armenia, Azerbaijan, Belize, Bhutan, Brazil, Cabo Verde, China, Colombia, Costa Rica, the Democratic People’s Republic of Korea, the Dominican Republic, Ecuador, Egypt, El Salvador, Georgia, Iran (Islamic Republic of), Iraq, Kazakhstan, Kyrgyzstan, Malaysia, Mexico, Morocco, Nicaragua, Oman, Panama, Paraguay, the Republic of Korea, Sao Tome and Principe, Saudi Arabia, South Africa, Sri Lanka, Suriname, the Syrian Arab Republic, Tajikistan, Thailand, Türkiye, Turkmenistan, the United Arab Emirates, Uzbekistan and Vanuatu (2013 to 2020). Fig. 3.2. Global trends in a) malaria case incidence (cases per 1000 population at risk) and b) mortality rate (deaths per 100 000 population at risk), 2000–2021; and c) distribution of malaria cases and d) deaths by country, 2021 See methods notes for Table 3.1. Table 3.2. Estimated malaria cases and deaths in the WHO African Region, 2000–2021 See methods notes for Table 3.1. Fig. 3.3. Trends in a) malaria case incidence (cases per 1000 population at risk) and b) mortality rate (deaths per 100 000 population at risk), 2000–2021; and c) malaria cases by country in the WHO African Region, 2021 See methods notes for Table 3.1. Table 3.3. Estimated malaria cases and deaths in the WHO South-East Asia Region, 2000–2021 See methods notes for Table 3.1. Fig. 3.4. Trends in a) malaria case incidence (cases per 1000 population at risk) and b) mortality rate (deaths per 100 000 population at risk), 2000–2021; and c) malaria cases by country in the WHO South-East Asia Region, 2021 See methods notes for Table 3.1. Table 3.4. Estimated malaria cases and deaths in the WHO Eastern Mediterranean Region, 2000–2021 See methods notes for Table 3.1. Fig. 3.5. Trends in a) malaria case incidence (cases per 1000 population at risk) and b) mortality rate (deaths per 100 000 population at risk), 2000–2021; and c) malaria cases by country in the WHO Eastern Mediterranean Region, 2021 See methods notes for Table 3.1. Table 3.5. Estimated malaria cases and deaths in the WHO Western Pacific Region, 2000–2021 See methods notes for Table 3.1. Fig. 3.6. Trends in a) malaria case incidence (cases per 1000 population at risk) and b) mortality rate (deaths per 100 000 population at risk), 2000–2021; and c) malaria cases by country in the WHO Western Pacific Region, 2021 See methods notes for Table 3.1. Table 3.6. Estimated malaria cases and deaths in the WHO Region of the Americas, 2000–2021 See methods notes for Table 3.1. Fig. 3.7. Trends in a) malaria case incidence (cases per 1000 population at risk) and b) mortality rate (deaths per 100 000 population at risk), 2000–2021; and c) malaria cases by country in the WHO Region of the Americas, 2021 See methods notes for Table 3.1. Fig. 3.8. Cumulative number of cases and deaths averted, globally and by WHO region, 2000–2021 See methods notes for Table 3.1 for information on estimation of cases and deaths. Estimated cases and deaths averted over the period 2000–2021 were computed by comparing current estimates for each year since 2000 with the malaria case incidence and mortality rates from 2000, assuming that they remained constant throughout the same period, adjusting for population growth. Fig. 3.9. Percentage of a) cases and b) deaths averted, by WHO region, 2000–2021 See methods notes for Table 3.1 for information on estimation of cases and deaths. See Fig. 3.8 for methods used to estimate cases and deaths averted. The percentage of cases and deaths averted was estimated using overall global cases and deaths averted as the denominator, and regional cases and deaths averted as the numerator. 136 W O RL D M AL AR IA R EP O RT 2 02 2 Fig. 3.10. Estimated prevalence of exposure to malaria infection during pregnancy, overall and by subregion in 2021, in moderate to high transmission countries in the WHO African Region Estimates of malaria-exposed pregnancies and preventable malaria-attributable low birthweight (LBW) deliveries in the absence of pregnancy-specific malaria prevention (i.e. long-lasting insecticidal net [LLIN] delivery based on intermittent preventive treatment in pregnancy [IPTp] or antenatal care [ANC]) were obtained using a model of the relationship between these outcomes, slide microscopy prevalence in the general population, and age- and gravidity-specific fertility patterns. This model was developed by fitting an established model of the relationship between malaria transmission and malaria infection by age (22) to patterns of infection in placental histology (23) and attributable LBW risk by gravidity, in the absence of IPTp or other effective chemoprevention (24). The model was run across a 0.2 degree (5 km2) longitude/ latitude grid for 100 realizations of the Malaria Atlas Project (MAP) (13) joint posterior estimated slide prevalence in children aged 2–10 years in 2021 (25). Country-specific, age-specific or gravidity-specific fertility rates, stratified by urban/rural status, were obtained from demographic and health surveys (DHS) and malaria indicator surveys (MIS), where such surveys had been carried out since 2014 and were available from the DHS programme website (26). Countries where surveys were not available were allocated fertility patterns from a survey undertaken in another country, matched on the basis of total fertility rate (27) and geography. Fertility patterns of individual women within simulations at each grid-point were simulated based on the proportion of women estimated to be living in urban or rural locations. Urban or rural attribution at a 1 km2 scale was conducted based on WorldPop 1 km2 population estimates from 2018 (28) and an urban/rural threshold of 386 people per km2 (29); the estimates were then aggregated to the 0.2 degree (5 km2) resolution of the MAP surfaces. This provided a risk of malaria infection and malaria-attributable LBW in the absence of prevention during pregnancy, along with a modelled per capita pregnancy rate for each grid-point, which was aggregated to country level (using WorldPop population estimates) to provide a per-pregnancy risk of malaria infection and a per live birth estimate of malaria-attributable LBW in the absence of prevention. These were then multiplied by country-level estimates of pregnancies and malaria- attributable estimates of LBW in 2021 (Fig. 3.11). Fig. 3.11. Estimated number of LBWs due to exposure to malaria infection during pregnancy (without IPTp versus at estimated levels of IPTp coverage), overall and by subregion in 2021, in moderate to high transmission countries in sub-Saharan Africa Methods for estimating malaria infection in pregnancy and malaria-attributable LBWs are described in Walker et al. (2014) (24). Numbers of pregnancies were estimated from the latest UN population-estimated number of births and were adjusted for the rate of abortion, miscarriage and stillbirths (30, 31). The underlying P. falciparum parasite prevalence estimates were from the PfPR2-10 estimates described in Table 3.1, using methods described in Bhatt et al. (25). Fig. 3.12. Estimated number of LBWs averted if current levels of IPTp coverage are maintained, and additional number averted if coverage of IPTp1 was optimized to match levels of coverage of ANC1 in 2021 while maintaining IPTp2 and IPTp3 at current levels, in moderate to high transmission countries in the WHO African Region Efficacy of IPTp was modelled as a per sulfadoxine- pyrimethamine (SP) dose reduction in the attributable risk of LBW, fitted to data from trials of IPTp-SP efficacy before the implementation of the intervention as policy; thus, the results reflect the impact on drug-sensitive parasites, with the central estimate being based on an assumed malaria- attributable LBW fraction of 40% within these trials. The modelling produced estimates of 48.5%, 73.5% and 86.3% efficacy in preventing malaria-attributable LBW for women receiving one, two or three doses of SP through IPTp, respectively. This analysis excluded South Sudan owing to the lack of consistent IPTp data reporting through time. Fig. 3.13. Estimated number of LBWs averted if levels of IPTp3 coverage were optimized to match levels of coverage of ANC1 in 2021, in moderate to high transmission countries in the WHO African Region See methods for Fig. 3.11 and Fig. 3.12. Fig. 3.14. Estimated number of LBWs averted if levels of IPTp3 were optimized to achieve 90% coverage in 2021, in moderate to high transmission countries in the WHO African Region See methods for Fig. 3.11 and Fig. 3.12. Fig. 4.1. Number of countries that were malaria endemic in 2000 and had fewer than 10, 100, 1000 and 10 000 indigenous malaria cases between 2000 and 2021 The figure is based on the countries where malaria was endemic in 2000 that also had cases of malaria reported in 2000 (108 endemic countries, excluding Egypt, Kazakhstan and the United Arab Emirates, with zero reported cases in 2000). P. knowlesi cases were not included. The number of estimated cases was tabulated. 137 ANNEx 1 – DATA SOURCES AND METhODS Table 4.1. Countries eliminating malaria since 2000 Countries are shown by the year in which they attained zero indigenous cases for 3 consecutive years, according to reports submitted by NMPs. Maldives was certified in 2015; however, it was already malaria free before 2000 and thus is not listed here. Table 4.2. Number of indigenous malaria cases in E-2020 and E-2025 countries, 2010–2021 Data were derived from NMP reports. Total indigenous malaria cases are based on confirmed malaria cases reported as indigenous by all countries in the “malaria eliminating countries for 2025” (E-2025) category between 2010 and 2021. For countries where not all cases are classified, total confirmed cases minus imported and introduced cases were used. For years where no case classification was carried out, all confirmed cases were considered to be indigenous. In Costa Rica (2021) and South Africa (2018–2021), unclassified cases were reclassified as indigenous and added to reported indigenous cases. P. knowlesi cases were excluded from countries reporting this species (Indonesia, Malaysia, the Philippines and Thailand). Fig. 4.2. Total indigenous malaria and P. falciparum indigenous cases in the GMS, 2000–2021 Data on the Greater Mekong subregion (GMS) were derived from the WHO database. The data were assembled using the following methodology: ■ Total indigenous malaria cases and indigenous P. falciparum cases are based on confirmed cases reported as indigenous by each country for all E-2025 countries and for those GMS countries where 100% of malaria cases are investigated and classified. ■ For GMS countries where not all cases are classified, total confirmed minus imported and introduced cases, and total P. falciparum minus imported and introduced P. falciparum cases were used to calculate indigenous malaria cases and indigenous P. falciparum cases, respectively. ■ For GMS countries where cases are not classified, all confirmed cases are assumed to be indigenous. Depending on the data that countries submit annually, the methodology used can vary by year for the same country. P. knowlesi cases were excluded from total indigenous cases. Fig. 4.3. Total indigenous malaria and P. falciparum cases in the GMS, by country, 2012–2021 See methods notes for Fig. 4.2. Fig. 4.4. Regional map of malaria incidence in the GMS, by area, 2012–2021 Data were derived from NMP reports to the GMS Malaria Elimination Database. Malaria incidence was calculated by total confirmed cases (rapid diagnostic test [RDT] + microscopy positive)/1000 population. For Viet Nam, data are shown at provincial level; for all other countries, data are shown at district level. Fig. 4.5. Estimated malaria a) cases and b) deaths in HBHI countries, 2000–2021 These estimates were for high burden to high impact (HBHI) countries. See methods notes for Table 3.1. Fig. 5.1. Implementation status of routine malaria surveillance modules Data were compiled from the University of Oslo's country status tracker database and information gathered from direct support and communications with countries and regional focal persons. Fig. 5.2. Overview of WHO case-based malaria elimination surveillance modules: a) malaria case notification, investigation and response, and b) foci investigation and response workflow DHIS2 system workflow for case notification, investigation and foci investigation and response, developed in collaboration with the University of Oslo. Table 5.1. Contents of the malaria surveillance assessment toolkit Summarized from the WHO malaria surveillance assessment toolkit implementation reference guide (32). Table 5.2. Results from the assessment of indicators for data quality and use Data were submitted by NMPs and partners. Table 6.1. Sources of funding data The table describes the main sources of funding as reported by donors and countries. An additional amount for patient care (based on estimated costs of patient care delivery services at public health facilities) is calculated for each country and added to domestic funding. Fig. 6.1. Funding for malaria control and elimination, 2010–2021 (% of total funding), by source of funds (constant 2021 US$) Total funding for malaria control and elimination over the period 2000–2021 was estimated using available data obtained from several sources. The methodology below describes the collection and analysis for all available public sector domestic funding and international funding for Figs. 6.1–6.6. Fig. 6.1 and Fig. 6.2 reflect data for the years 2010–2021 because country-specific unit cost estimates were not 138 W O RL D M AL AR IA R EP O RT 2 02 2 available until 2010 and data from the Organisation for Economic Co-operation and Development (OECD) use of the multilateral system were not available until 2011 (whereby 2010 estimates were derived from 2011 data). Fig. 6.5 reflects data for the years 2010–2021 because the trends in funding per person at risk before 2010 cannot be reliably interpreted owing to significant data gaps in international and domestic funding in each WHO region. Fig. 6.3, Fig. 6.4 and Fig. 6.6 reflect data for 2000–2021, where available. In the case of missing data for a specific funder, no imputation was conducted; hence, the trends presented in the main text should be interpreted carefully. Funding for malaria control and elimination is presented in constant 2021 US$ throughout the text and figures. Contributions from governments of endemic countries were estimated as the sum of government contributions reported by NMPs for the world malaria report of the relevant year plus the estimated costs of patient care delivery services at public health facilities. NMP contributions in the form of domestic expenditures were used from 2000 through 2021. When domestic government expenditure was unavailable, budgets were used. In cases where neither domestic expenditure nor budgets were available, estimates were conducted. These estimates were either based on an average from the country reported data for the previous 2 years (i.e. 2019 and 2020), or the data reported in the previous year (i.e. 2020), based on the country’s preference. The number of reported malaria cases attending public health facilities was sourced from NMP reports, adjusted for diagnosis and reporting completeness. Between 1% and 3% of uncomplicated reported malaria cases were assumed to have moved to the severe stage of disease, and 50–80% of these severe cases were assumed to have been hospitalized. Among the cases that were assumed to have been hospitalized, a 3-day average hospital stay was used. Costs of outpatient visits and inpatient bed-stays were estimated from the perspective of the public health care provider, using unit cost estimates from WHO- CHOosing Interventions that are Cost-Effective (WHO-CHOICE) (33). For each country, WHO-CHOICE 2010 unit cost estimates expressed in national currency were estimated for the period 2011–2021 using the gross domestic product (GDP) annual price deflator published by the World Bank (34) in July 2022 and converted in the base year 2010. Country-specific unit cost estimates were then converted from national currency to constant 2021 US$ for each year over the period 2010–2021. For each country, the number of adjusted reported malaria cases attending public health facilities was then multiplied by the estimated unit costs. In the absence of information on the level of care at which malaria patients attend public facilities, uncertainty around unit cost estimates was handled through probabilistic uncertainty analysis. The mean total cost of patient care service delivery was calculated from 1000 estimations. International bilateral funding data were obtained from several sources. Data on planned funding from the government of the United States of America (USA) were sourced with the technical assistance of the Kaiser Family Foundation (35). Country-level funding data were available from the US Agency for International Development (USAID) for the period 2006–2021. Country- specific planned funding data from two agencies – the US Centers for Disease Control and Prevention (US CDC) and the US Department of Defense (DoD) – were not available; therefore, data on total annual planned funding from each of these two agencies were used for the period 2001–2021. Total annual planned funding from USAID was used for 2001–2005, until the introduction of country-specific funding from 2006 through 2021. For the government of the United Kingdom of Great Britain and Northern Ireland (United Kingdom), data on funding towards malaria control since 2017 have been sourced from the Statistics on international development: final UK aid spend (36). Data from the final UK aid spend 2020 were used, with the technical assistance of the United Kingdom Foreign, Commonwealth and Development Office (FCDO). In this year’s report, disbursement data were not yet finalized for 2021 at the time of publishing; therefore, with the approval of the FCDO, disbursement data for 2021 were estimated using 2020 reported figures and converted to constant 2021 US$. The final UK aid spend data do not capture all spending from the United Kingdom that may affect malaria outcomes because the country supports malaria control and elimination through a broad range of interventions – for example, via support to overall health systems in malaria endemic countries, and through research and development (R&D) – that are not included in these data. For the period 2007–2016, United Kingdom spending data were sourced from the OECD creditor reporting system (CRS) database on aid activity (37). For all other donors, disbursement data were also obtained from the OECD CRS database on aid activity for the period 2002–2020. Disbursement data for 2021 were estimated using 2020 reported figures. All data were converted to constant 2021 US$. For years where no data were available for a particular funder, no imputation was conducted; hence, trends presented in the figures in the main text should be interpreted carefully. Malaria-related annual funding from donors through multilateral agencies was estimated from data on: i. donors’ contributions published by the Global Fund to Fight AIDS, Tuberculosis and Malaria (Global Fund) (38) from 2010 to 2021, and annual disbursements by the Global Fund to malaria endemic countries, as reported by the Global Fund; and ii. donors’ disbursements to malaria endemic countries published in the OECD CRS and in the OECD Development Assistance Committee (DAC) members’ total use of the multilateral system from 2011 through 2020 (37). All funding flows were converted to constant 2021 US$. 139 ANNEx 1 – DATA SOURCES AND METhODS For (i), the amount of funding contributed by each donor was estimated as the proportion of funding paid by each donor out of the total amount received by the Global Fund in a given year, multiplied by the total amount disbursed by the Global Fund in that same year. For (ii), contributions from donors to multilateral channels were estimated by calculating the proportion of the core contributions received by a multilateral agency each year by each donor, then multiplying that amount by the multilateral agency’s estimated investment in malaria control in that same year. Contributions from malaria endemic countries to multilateral agencies were allocated to governments of endemic countries under the “funding source” category. Contributions from non-DAC countries and other sources to multilateral agencies were not available and were therefore not included. Annual estimated investments were summed to estimate the total amount each funder contributed to malaria control and elimination over the period 2010–2021, and the relative percentage of the total spending contributed by each funder was calculated for the period 2010–2021. Fig. 6.1 excludes household spending on malaria prevention and treatment in malaria endemic countries. The data sources, boundaries, accounting rules and estimation methods used in this report are different from those of the System of Health Accounts 2011 (SHA2011). The malaria expenditure data reported here are thus not comparable with the disease expenditure data, including for malaria, that are reported in WHO's Global Health Expenditure Database. Fig. 6.2. Funding for malaria control and elimination, 2010–2021, by source of funds (constant 2021 US$) See methods notes for Fig. 6.1 for sources of information on total funding for malaria control and elimination from governments of malaria endemic countries and on international funding flows. Fig. 6.2 excludes household spending on malaria prevention and treatment in malaria endemic countries. The data sources, boundaries, accounting rules and estimation methods used in this report are different from those of the System of Health Accounts 2011 (SHA2011). The malaria expenditure data reported here are thus not comparable with the disease expenditure data, including for malaria, that are reported in WHO's Global Health Expenditure Database. Fig. 6.3. Funding for malaria control and elimination, 2000–2021, by channel (constant 2021 US$) See methods notes for Fig. 6.1 for sources of information on total funding for malaria control and elimination from governments of malaria endemic countries and on international funding flows. For years in which no data were available for a particular funder, no imputation was conducted; hence, trends presented in the main text figures should be interpreted carefully. Fig. 6.3 excludes household spending on malaria prevention and treatment in malaria endemic countries. The data sources, boundaries, accounting rules and estimation methods used in this report are different from those of the System of Health Accounts 2011 (SHA2011). The malaria expenditure data reported here are thus not comparable with the disease expenditure data, including for malaria, that are reported in WHO's Global Health Expenditure Database. Fig. 6.4. Funding for malaria control and elimination, 2000–2021, by World Bank 2021 income group and source of funding (constant 2021 US$) See methods notes for Fig. 6.1 for sources of information on total funding for malaria control and elimination from governments of malaria endemic countries and on international funding flows. Data on income group classification for 2021 were sourced from the World Bank (39). For years in which no data were available for a particular funder, no imputation was conducted; hence, trends presented in the main text figures should be interpreted carefully. Fig. 6.4 excludes household spending on malaria prevention and treatment in malaria endemic countries. The data sources, boundaries, accounting rules and estimation methods used in this report are different from those of the System of Health Accounts 2011 (SHA2011). The malaria expenditure data reported here are thus not comparable with the disease expenditure data, including for malaria, that are reported in WHO's Global Health Expenditure Database. Fig. 6.5. Funding for malaria control and elimination per person at risk, 2010–2021, by WHO region (constant 2021 US$) See methods notes for Fig. 6.1 for sources of information on total funding for malaria control and elimination from governments of malaria endemic countries and on international funding flows. Fig. 6.5 excludes household spending on malaria prevention and treatment in malaria endemic countries. The data sources, boundaries, accounting rules and estimation methods used in this report are different from those of the System of Health Accounts 2011 (SHA2011). The malaria expenditure data reported here are thus not comparable with the disease expenditure data, including for malaria, that are reported in WHO's Global Health Expenditure Database. 140 W O RL D M AL AR IA R EP O RT 2 02 2 Fig. 6.6. Funding for malaria control and elimination, 2000–2021, by WHO region (constant 2021 US$) See methods notes for Fig. 6.1 for sources of information on total funding for malaria control and elimination from governments of malaria endemic countries and on international funding flows. The “Unspecified” category in Fig. 6.6 includes all funding data for which there was no geographical information on the recipient. For years in which no data were available for a particular funder, no imputation was conducted; hence, trends presented in the main text figures should be interpreted carefully. The data sources, boundaries, accounting rules and estimation methods used in this report are different from those of the System of Health Accounts 2011 (SHA2011). The malaria expenditure data reported here are thus not comparable with the disease expenditure data, including for malaria, that are reported in WHO's Global Health Expenditure Database. Fig. 6.7. Real GDP growth annual per cent change, 2021, by World Bank income classification (constant 2021 US$) Data on real GDP growth annual per cent change for 2021 were sourced directly from the International Monetary Fund data mapper (40). Data on income group classification for 2021 were sourced from the World Bank (39). This section outlines the global economic outcome in 2020 and 2021, which provides a limitation because it does not capture the differences across countries. Fig. 6.8. Funding for malaria-related R&D, 2012–2021, by product type (constant 2021 US$) Data on funding for malaria-related R&D for 2012–2021 were sourced directly from Policy Cures Research through the G-FINDER data portal (41). Fig. 6.9. Funding for malaria-related R&D, 2012–2021, by sector (constant 2021 US$) See methods for Fig. 6.8. Fig. 7.1. Number of ITNs delivered by manufacturers and distributed by NMPs, 2010– 2021 Data on the number of ITNs delivered by manufacturers to countries were provided to WHO by Milliner Global Associates. Data from NMP reports were used for the number of ITNs distributed within countries; these data include nets distributed through ANC clinics, the Expanded Programme on Immunization (EPI), mass campaigns and other distribution channels. In 2021, ITN distribution data for Botswana, Chad, Djibouti, Guinea-Bissau, Haiti, India, Myanmar and the United Republic of Tanzania were provided by the Global Fund. Fig. 7.2. a) Indicators of population-level access to ITNs, sub-Saharan Africa, 2000–2021 and b) indicators of population-level use of ITNs, sub-Saharan Africa, 2000–2021 Estimates of ITN coverage were derived from a model developed by MAP (13, 42), using a two-stage process. First, a mechanism was designed for estimating net crop (i.e. the total number of ITNs in households in a country at a given time), taking into account inputs to the system (e.g. deliveries of ITNs to a country) and outputs (e.g. loss of ITNs from households). Second, empirical modelling was used to translate estimated net crops (i.e. total number of ITNs in a country) into resulting levels of coverage (e.g. access within households, use in all ages and use among children aged under 5 years). The model incorporates data from three sources: ■ the number of ITNs delivered by manufacturers to countries, as provided to WHO by Milliner Global Associates; ■ the number of ITNs distributed within countries, as reported to WHO by NMPs; and ■ data from nationally representative household surveys from 40 countries in sub-Saharan Africa, from 2000 to 2021. Countries for analysis The main analysis covered 40 of the 46 malaria endemic countries or areas of sub-Saharan Africa. The island of Mayotte (for which no ITN delivery or distribution data were available) was excluded, as were the low transmission countries of Botswana, Eswatini, Namibia, Sao Tome and Principe and South Africa, for which ITNs comprise a small proportion of vector control. Analyses were limited to populations categorized by NMPs as being at risk. Estimating national net crops through time As described by Flaxman et al. (43), national ITN systems were represented using a discrete-time stock-and-flow model. Nets delivered to a country by manufacturers were modelled as first entering a “country stock” compartment (i.e. stored in-country but not yet distributed to households). Nets were then available from this stock for distribution to households by the NMP or through other distribution channels. To accommodate uncertainty in net distribution, the number of nets distributed in a given year was specified as a range, with all available country stock (i.e. the maximum number of nets that could be delivered) as the upper end of the range and the NMP-reported value (i.e. the assumed minimum distribution) as the lower end. The total household net crop comprised new nets reaching households plus older nets remaining from earlier times, with the duration of net retention by households governed by a loss function. However, rather than the loss function being fitted to a small external 141 ANNEx 1 – DATA SOURCES AND METhODS dataset – as per Flaxman et al. (43) – the loss function was fitted directly to the distribution and net crop data within the stock-and-flow model itself. Loss functions were fitted on a country-by-country basis, were allowed to vary through time, and were defined separately for conventional ITNs (cITNs) and LLINs. The fitted loss functions were compared with existing assumptions about rates of net loss from households. The stock-and-flow model was fitted using Bayesian inference and Markov chain Monte Carlo methods, which provided time-series estimates of national household net crop for cITNs and LLINs in each country and an evaluation of underdistribution, all with posterior credible intervals. Estimating indicators of national ITN access and use from the net crop Rates of ITN access within households depend not only on the total number of ITNs in a country (i.e. the net crop), but also on how those nets are distributed among households. One factor that is known to strongly influence the relationship between net crop and net distribution patterns among households is the size of households, which varies among countries, particularly across sub-Saharan Africa. Many recent national surveys report the number of ITNs observed in each household surveyed. Hence, it is possible to both estimate net crop and generate a histogram that summarizes the household net ownership pattern (i.e. the proportion of households with 0, 1, 2, etc. nets). In this way, the size of the net crop was linked to distribution patterns among households while accounting for household size, making it possible to generate ownership distributions for each stratum of household size. The bivariate histogram of net crop to distribution of nets among households by household size made it possible to calculate the proportion of households with at least one ITN. Also, because the numbers of both ITNs and people in each household were available, it was possible to directly calculate two additional indicators: the proportion of households with at least one ITN for every two people, and the proportion of the population with access to an ITN within their household. For the final ITN indicator – the proportion of the population who slept under an ITN the previous night – the relationship between ITN use and access was defined using 62 surveys in which both these indicators were available (ITN use all ages=0.8133 × ITN access all ages + 0.0026, R2=0.773). This relationship was applied to the MAP’s country–year estimates of household access, to obtain ITN use among all ages. The same method was used to obtain the country– year estimates of ITN use in children aged under 5 years (ITN use children under 5=0.9327 x ITN access children under 5 + 0.0282, R2=0.754). Fig. 7.3. Percentage of the population at risk protected by IRS, by WHO region, 2010–2021 The number of people protected by IRS was reported to WHO by NMPs. The total population of each country was taken from the 2022 revision of the World population prospects (27); the population at risk of malaria was calculated using the methods previously described for Table 3.1. For Ecuador, Saudi Arabia and South Africa, the number of people protected by IRS exceeded the population at risk. Hence, in Saudi Arabia, the targeted population was used as the denominator, and in Ecuador and South Africa, the reported population at risk was used as denominator. Table 7.1. Average number of children treated with SMC per cycle, by year, in countries implementing SMC, 2012–2021 Data were provided by the London School of Hygiene & Tropical Medicine (LSHTM) and Medicines for Malaria Venture (MMV). The table shows the average number of children receiving seasonal malaria chemoprevention (SMC) for each district, regardless of the number of cycles (the average is based on 3, 4 or 5 cycles in a district where 3, 4 or 5 cycles have been done, respectively). The sum of the district averages is used to obtain the average for each country. In previous reports, the total number of children who received SMC at the country level was divided by four. The rationale for this approach was twofold. First, most countries performed four cycles in all districts up until 2021. Second, it was assumed that children receiving a fifth cycle had already received the first four cycles and were therefore de facto counted. The limitation of this approach was that it underestimated the average number of children covered by SMC in countries that performed fewer than four cycles. Table 7.2. Number of treatment doses delivered, by year, in countries implementing SMC, 2014– 2021 Data were provided by LSHTM and MMV. The number of treatments delivered is the sum of all the children who received SMC at each cycle. Previously, in the World malaria report 2021 (44), the number of treatments delivered was calculated by multiplying the average number of children treated by four. This assumed that each country conducted four cycles in each district, which is not the case. Fig. 7.4. Subnational areas where SMC was delivered, and number of treatment cycles per district, in implementing countries in sub-Saharan Africa, 2021 Data were provided by MMV and assembled through the SMC Alliance. Fig. 7.5. Percentage of pregnant women attending an ANC clinic at least once and receiving IPTp, by number of SP doses, sub-Saharan Africa, 2010–2021 The total number of pregnant women eligible for IPTp was calculated by adding total live births calculated from UN population data and spontaneous pregnancy loss (specifically, miscarriages and stillbirths) after the first 142 W O RL D M AL AR IA R EP O RT 2 02 2 trimester (30). Spontaneous pregnancy loss has previously been calculated by Dellicour et al. (31). Country-specific estimates of IPTp coverage were calculated as the ratio of pregnant women receiving IPTp during ANC visits to the estimated number of pregnant women eligible for IPTp in a given year. ANC attendance rates were derived in the same way, using the number of initial ANC clinic visits reported through routine information systems. Local linear interpolation of information for national representative surveys was used to compute missing values. The same dose-specific IPTp and ANC coverage estimates reported in 2020 were assumed to be observed in 2020 for three countries that had incomplete information for 2021: Equatorial Guinea, Gabon and Mauritania. Annual aggregate estimates exclude countries for which a report or interpolation was not available for the specific year. Dose coverage between 2010 and 2020 was calculated for 33 of the 35 countries with an IPTp policy (the Comoros and Sao Tome and Principe were excluded because of their low malaria burden). The coverages of at least one ANC visit were corrected in 2020 and 2021 based on the country-specific disruptions to ANC services reported per country and obtained from the national pulse surveys on continuity of EHS during the COVID-19 pandemic conducted by WHO (first round in May–July 2020, second in January–March 2021, and third in November–December 2021) (5-7). Disruptions were quantified by using the middle value of the disruption ranges reported by countries. A 5% reduction in ANC attendance was assumed in all countries that did not provide information on ANC service disruptions in the pulse surveys (45-49). The corrected number of women that attended at least one ANC visit, after adjusting for disruptions, multiplied by the operational coverage of the first IPTp dose reported in 2020 or 2021 (calculated as the number of women who received the first IPTp dose divided by the corrected number of women who attended the first ANC visit) made it possible to re-estimate the expected number of pregnant women who took the first IPTp dose, which in turn made it possible to re-estimate the population coverage of the first IPTp dose. The ratio observed among the first, second and third IPTp doses was used to calculate the corrected coverage for the second and third IPTp doses, assuming no disruptions in IPTp dose follow-up. Indicator Numerator Denominator Median prevalence of fever in the past 2 weeks Children aged under 5 years with a history of fever in the past 2 weeks Children aged under 5 years Median prevalence of fever in the past 2 weeks in children for whom treatment was sought Children aged under 5 years with a history of fever in the past 2 weeks for whom treatment was sought Children aged under 5 years with fever in the past 2 weeks Median prevalence of treatment seeking by source of treatment for fever in the public sector (health facility) Children aged under 5 years with a history of fever in the past 2 weeks for whom treatment was sought in the public sector (health facility) Children aged under 5 years with fever in the past 2 weeks for whom treatment was sought Median prevalence of treatment seeking by source of treatment for fever in the public sector (community health worker) Children aged under 5 years with a history of fever in the past 2 weeks for whom treatment was sought in the public sector (community health worker) Children aged under 5 years with fever in the past 2 weeks for whom treatment was sought Median prevalence of treatment seeking by source of treatment for fever in the private sector (formal and informal) Children aged under 5 years with a history of fever in the past 2 weeks for whom treatment was sought in the private sector (formal and informal) Children aged under 5 years with fever in the past 2 weeks for whom treatment was sought Median prevalence of receiving finger or heel prick Children aged under 5 years with a history of fever in the past 2 weeks and who received a finger or heel prick Children aged under 5 years with fever in the past 2 weeks for whom treatment was sought Median prevalence of treatment with ar- temisinin-based combination therapies (ACTs) Children aged under 5 years with a history of fever in the past 2 weeks for whom treatment was sought and who were treated with ACTs Children aged under 5 years with fever in the past 2 weeks for whom treatment was sought Median prevalence of treatment with ACTs among those who received a finger or heel prick Received ACT treatment Children aged under 5 years with fever in the past 2 weeks for whom treatment was sought and who received a finger or heel prick Median prevalence of treatment with ACTs Children aged under 5 years with a history of fever in the past 2 weeks for whom treatment was sought and who were treated with ACTs Children aged under 5 years with fever in the past 2 weeks for whom treatment was sought and who were treated with antimalarials 143 ANNEx 1 – DATA SOURCES AND METhODS Diagnostic testing and treatment The analysis is based on the latest nationally representative household surveys (DHS and MIS) conducted between 2015 and 2021, and surveys (the latest from 2000–2005) that were considered as baseline surveys from sub- Saharan African countries where data on malaria case management were available. The data are only available for children aged under 5 years because DHS and MIS focus on the most vulnerable population groups. Interviewers ask caregivers whether the child has had fever in the 2 weeks preceding the interview and, if so, where care was sought; whether the child received a finger or heel prick as part of the care; what treatment was received for the fever and when; and, in particular, whether the child received an artemisinin-based combination therapy (ACT) or other antimalarial medicine. In addition to self-reported data, DHS and MIS also include biomarker testing for malaria, using RDTs that detect P. falciparum histidine-rich protein 2 (HRP2). Percentages and 95% CIs were calculated for each country each year, taking into account the survey design. Median values and interquartile ranges were calculated using country percentages for the latest and baseline surveys. The indicators outlined above are presented in Table 7.3. The use of household survey data has several limitations. One issue is that, because of difficulty recalling past events, respondents may not provide reliable information, especially on episodes of fever and the identity of prescribed medicines, resulting in a misclassification of drugs. Also, because respondents can choose more than one source of care for one episode of fever, and because the question on diagnostic test and treatment is asked broadly and hence is not linked to any specific source of care, it has been assumed that the diagnostic test and treatment were received in all the selected sources of care. However, only a low percentage (<5%) of febrile children were brought to more than one source of care to receive care. Data may also be biased by the seasonality of survey data collection because DHS are carried out at various times during the year and MIS are usually timed to correspond with the high malaria transmission season. Another limitation, when undertaking trend analysis, is that DHS and MIS are done intermittently or not at all in some countries, resulting in a relatively small number of countries in sub-Saharan Africa or for any particular 4-year period. In addition, depending on the sample size of the survey, the denominator for some indicators can be small – countries where the number of children in the denominator was less than 30 were excluded from the calculation. Fig. 7.6. Number of RDTs sold by manufacturers and distributed by NMPs for use in testing suspected malaria cases, 2010–2021 The numbers of RDTs distributed by WHO region are the annual totals reported as having been distributed by NMPs. In 2021, RDT distribution data for Cambodia and the Gambia were provided by the Global Fund. Numbers of RDTs sold between 2010 and 2021 reflect sales by companies eligible for procurement. From 2010 to 2017, WHO received reports from up to 44 (cumulative number; the number of eligible manufacturers and responders differed from year to year) manufacturers that participated in the RDT Product Testing Programme by WHO, the Foundation for Innovative New Diagnostics (FIND), the US CDC and the Special Programme for Research and Training in Tropical Diseases. Since WHO prequalification became a selection criterion for procurement, sales data from 2018 onwards were provided by a limited number of eligible manufacturers. For 2021, all 10 of the eligible companies reported to WHO. Fig. 7.7. Number of ACT treatment courses delivered by manufacturers and distributed by NMPs to patients, 2010–2021 Data on ACT deliveries for 2021 were provided by 10 manufacturers eligible for procurement by WHO and the UN Children’s Fund (UNICEF). ACT deliveries were categorized as being to either the public sector or the private sector, also taking into account the Affordable Medicines Facility–for malaria (AMFm) initiative and the Global Fund co-payment mechanism for the relevant years, respectively. Data on ACTs distributed within countries through the public sector were taken from NMP reports. For 2019, 2020 and 2021, missing data from NMP reports for ACT distributions were calculated based on the rate of ACT distributions to the number of patients treated with ACTs from the previous year, multiplied by the number of patients treated with ACTs in the current year. If these data were not available, the number of patients treated with ACTs was used as a proxy for ACT distributions. Please also refer to the methods described for Annex 4-D, found in Annex 4-K. Table 7.3. Summary of coverage of treatment seeking for fever, diagnosis and use of ACTs for children aged under 5 years, from household surveys in sub-Saharan Africa, at baseline (2005–2011) and most recently (2015–2021) See the information provided in the section titled Diagnostic testing and treatment (under Fig. 7.5). Table 7.4. Summary of coverage of treatment seeking for fever, diagnosis and use of ACTs for children aged under 5 years from the most recent household survey for countries in sub-Saharan Africa See the information provided in the section titled Diagnostic testing and treatment (under Fig. 7.5). Fig. 8.1. Comparison of global progress in malaria a) case incidence and b) mortality rate, considering two scenarios: current trajectory maintained (blue) and GTS targets achieved (green) The Global technical strategy for malaria 2016–2030 (GTS) target is a 90% reduction of malaria incidence and 144 W O RL D M AL AR IA R EP O RT 2 02 2 mortality rate by 2030, with milestones of 40% and 75% reductions in both indicators for the years 2020 and 2025, respectively (50). A curve based on a quadratic fit is used for the malaria incidence milestones. For projection of malaria incidence under current estimated trends, the same year-on-year trend observed in the previous 10 years (2012–2021) is forecast up to 2030. The distance between the target and the observed or projected incidence or mortality estimates is calculated using the following formula: 1 minus (GTS expected value for a given year / observed or projected value for the same year). Fig. 8.2. Map of malaria endemic countries (including the territory of French Guiana) showing progress towards the GTS 2020 malaria case incidence milestone of at least 40% reduction from a 2015 baseline See methods notes for Fig. 8.1. Fig. 8.3. Map of malaria endemic countries (including the territory of French Guiana) showing progress towards the GTS 2020 malaria mortality rate milestone of at least 40% reduction from a 2015 baseline See methods notes for Fig. 8.1. Fig. 8.4. Comparison of progress in malaria a) case incidence and b) mortality rate in the WHO African Region considering two scenarios: current trajectory maintained (blue) and GTS targets achieved (green) See methods notes for Fig. 8.1. Fig. 8.5. Comparison of progress in malaria a) case incidence and b) mortality rate in the WHO Region of the Americas considering two scenarios: current trajectory maintained (blue) and GTS targets achieved (green) See methods notes for Fig. 8.1. Fig. 8.6. Comparison of progress in malaria a) case incidence and b) mortality rate in the WHO Eastern Mediterranean Region considering two scenarios: current trajectory maintained (blue) and GTS targets achieved (green) See methods notes for Fig. 8.1. Fig. 8.7. Comparison of progress in malaria a) case incidence and b) mortality rate in the WHO South-East Asia Region considering two scenarios: current trajectory maintained (blue) and GTS targets achieved (green) See methods notes for Fig. 8.1. Fig. 8.8. Comparison of progress in malaria a) case incidence and b) mortality rate in the WHO Western Pacific Region considering two scenarios: current trajectory maintained (blue) and GTS targets achieved (green) See methods notes for Fig. 8.1. Fig. 9.1. Number of P. falciparum TES finding more or less than 10% treatment failures in the WHO African Region, by ACT (2015–2021), among studies with at least 20 patients The bars show the number of therapeutic efficacy studies (TES) undertaken in 2015 to 2021, and which TES found more or less than 10% treatment failures for each ACT tested in the WHO African Region. Only studies with at least 20 patients were included. The data were obtained from the WHO Global database on antimalarial drug efficacy and resistance (51). Fig. 9.2. Countries in the WHO African Region with more than 5% of parasites sampled with PfKelch13 mutations and main mutations identified (2015–2020) The bars show the overall proportion of samples found to have PfKelch13 mutations and the main mutations identified. The data were summarized from data collated in the WHO Global database on antimalarial drug efficacy and resistance (51). Fig. 9.3. Number of P. falciparum TES finding more or less than 10% treatment failures, a) in the WHO South-East Asia Region, b) in the WHO Eastern Mediterranean Region, c) in the WHO Western Pacific Region, by ACT (2015–2021), among studies with at least 20 patients The bars show the number of therapeutic efficacy studies (TES) undertaken in 2015 to 2021, and which TES found more or less than 10% treatment failures for each ACT tested in the WHO South-East Asia Region, the WHO Eastern Mediterranean Region and the WHO Western Pacific Region. Only studies with at least 20 patients were included. The data were obtained from the WHO Global database on antimalarial drug efficacy and resistance (51). Fig. 9.4. Reported insecticide resistance status as a proportion of sites for which monitoring was conducted, by WHO region, 2010–2020, for pyrethroids, organochlorines, carbamates and organophosphates The status of resistance at each mosquito collection site for each insecticide class was assessed based on the lowest mosquito mortality reported across all standard WHO tube tests or US CDC bottle bioassays conducted at the site during 2010–2020, with validated discriminating concentrations of the insecticides in the class. If multiple insecticides and mosquito species were tested between 145 ANNEx 1 – DATA SOURCES AND METhODS 2010 and 2020 at the collection site, the lowest mosquito mortality was considered. If the lowest mosquito mortality was below 90%, resistance was considered to be confirmed at the site; if the lowest mosquito mortality was at least 90% but less than 98%, resistance was considered to be possible at the site; if the lowest mortality was 98% or more, vectors at the site were considered to be susceptible to the insecticide class. The figure was developed based on data in the WHO global database for insecticide resistance in malaria vectors. These data were reported to WHO by NMPs, national public health institutes, universities and research centres, the African Network for Vector Resistance, MAP (13), VectorBase and the US President’s Malaria Initiative (PMI), or were extracted from scientific publications. Fig. 9.5. Number of classes to which resistance was confirmed in at least one malaria vector in at least one monitoring site, 2010–2020 Resistance to an insecticide class was considered to be confirmed in a country if at least one vector species exhibited resistance to one insecticide in the class in at least one collection site in the country, as measured by standard WHO tube tests or US CDC bottle bioassays conducted with validated discriminating concentrations in 2010–2020. The map was developed based on data contained in the WHO global database for insecticide resistance in malaria vectors. These data were reported to WHO by NMPs, national public health institutes, universities and research centres, the African Network for Vector Resistance, MAP (13), VectorBase and PMI, or were extracted from scientific publications. Fig. 9.6. Detections of An. stephensi in the Horn of Africa and Nigeria, reported to WHO (2012–2021) Map of the invasion of An. stephensi was produced from data submitted to the WHO global database on invasive species (51) on the Malaria Threats Map (52). Fig. 9.7. Median LLIN retention time by country, ordered from highest to lowest Countries are labelled by IS03 code. Country labels are positioned at mean parameter values, while vertical bars indicate 95% CI width. Countries with fewer surveys have less stable model fits; those having fewer than three surveys are indicated in red. Analysis was supported by the Institute for Disease Modeling and MAP (13). This analysis uses a Bayesian mixed modelling framework built on data from net manufacturers, NMPs and cross-sectional household surveys over the past 20 years to estimate the history of ITN coverage metrics in sub-Saharan Africa (42). This approach includes two main steps. First, a national-level “stock and flow” mechanistic model tracks the distribution, acquisition and loss of ITNs by triangulating data from the three sources listed above. This step estimates both ITN retention times and ITN crop, the total number of nets in the community. The previous model on retention time was bounded at 1 year, suggesting that some countries might show even shorter median retention times in an unbounded setting. However, given the small number of surveys in these countries, such results might indicate underspecified models rather than truly short retention times. Fig. 9.8. The impact on ITN use of reducing operational and behavioural constraints in sub-Saharan Africa Analysis was supported by the Institute for Disease Modeling and MAP (13). ITN time-series counterfactuals under different operational and user behaviour scenarios were estimated using the same model as Fig. 9.5. All results were generated using the ITN coverage model described in Bertozzi-Villa et al. (42), with updated data through 2021. In addition to estimating ITN access, use and nets-per-capita over time, this model estimates a single median net retention time for each country. The horizontal black line is at 80%, a common target for “universal” net coverage. The yellow curve shows estimated true net use (people sleeping under a net divided by total population) from the model. The green curve shows what net use could be if everyone with access to a net used it. The orange curve shows what net use could be if, additionally, nets were retained for a median of 3 years (with no change to the quantity of nets distributed). Finally, the blue curve shows what net use could be if, in addition to 100% use and optimized retention, nets were perfectly allocated such that net access and nets-per-capita lined up exactly. Fig. 9.9. Sequential decomposition of LLIN effectiveness over 3 years (modelling pyrethroid-only ITNs; further details explaining different scenarios are provided in Table 9.1) Analysis was supported by the Institute for Disease Modeling and MAP (13). Results were generated using a mechanistic malaria model (53-55) run under a range of different ITN campaign parameters. Simulations were run without seasonality, to reduce the complexity of the results, and they used a vector composition of 84% An. arabiensis and 16% An. funestus. Simulations with these less anthropophilic and endophilic vectors were selected because, owing to the lack of seasonality and extreme effectiveness of the ITN simulations shown, scenarios run with primarily An. gambiae reached a stable equilibrium at elimination in most cases. The boundaries of ITN impact (0% and 100% lines on the plots) were calculated by running a simulation with no malaria interventions, as well as a simulation with idealized, near-perfect ITNs (100% coverage, no discarding of nets, 100% use rate, a constant 60% probability that a mosquito that lands on a net is killed, and a constant 90% probability that a mosquito that approaches a net does not enter it). Each coloured line of the plot represents the impact of sequentially reducing aspects of net 146 W O RL D M AL AR IA R EP O RT 2 02 2 effectiveness – first with waning insectide, then introducing vectors that are not fully susceptible, then introducing realistic net durability and so on. The palest lowest line, then, approximates the relative impact that nets today have, relative to the alternate scenarios. The 87% coverage, 83% use rate and 1.9-year retention half-life values were derived from the aggregated time series in Bertozzi-Villa et al. (42). The decision to reduce initial killing by 30% to approximate insecticide resistance was derived from an analysis of the WHO’s insecticide resistance database via the Malaria Threats Map (52). The 4-year killing half-life is based on the default net calibration of the Epidemiological MODeling software (EMOD), and the 1.9-year blocking half-life is set equal to the retention half-life based on the hypothesis that net retention and durability are closely linked (56). Table 9.1. Descriptions of scenarios presented in Fig. 9.9 See methods notes for Fig. 9.9. Fig. 10.1. Malaria diagnostics pipeline, showing the development stage of main product types according to primary intended use-case and sample type Information was extracted from Unitaid’s diagnostic market and technology landscape report (57). Table 10.1. Candidate molecules in the early development portfolio for the treatment of clinical malaria Information shown is a list and description of candidate molecules for malaria therapy in early development. Information was assembled by MMV. Fig. 10.2. Distribution of vector control products in the R&D pipeline by intervention type. Information was extracted from the WHO Global Observatory on Health Research and Development (58). Fig. 10.3. A roadmap of current and new vector control products Information was assembled by the Innovative Vector Control Consortium (IVCC) from its existing portfolio of vector control products (59). Table 10.2. Malaria vaccine candidates (in active development) by life cycle stage and R&D phase A summary of the main candidates extracted from the vaccine dashboard of the WHO Global Observatory on Health Research and Development (60). 147 References for Annex 1 1. Strategy to respond to antimalarial drug resistance in Africa. 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Disruptions in maternal health service use during the COVID-19 pandemic in 2020: experiences from 37 health facilities in low-income and middle-income countries. BMJ Glob Health. 2022;7:e007247. doi: https://doi.org/10.1136/bmjgh-2021-007247. 50. Global technical strategy for malaria 2016–2030. Geneva: World Health Organization; 2015 (https://apps.who.int/iris/bitstream/handle /10665/176712/9789241564991_eng.pdf;jsessionid=02F0657EB47EB043AD211FC20B1E2F1F?sequence=1). 51. Global database on antimalarial drug efficacy and resistance. Geneva: World Health Organization; 2020 (https://www.who.int/teams/ global-malaria-programme/case-management/drug-efficacy-and-resistance/antimalarial-drug-efficacy-database#:~:text=The%20 global%20database%20on%20antimalarial,efficacy%20in%20malaria%20endemic%20countries). 52. Malaria threats map [website]. Geneva: World Health Organization; 2022 (https://apps.who.int/malaria/maps/threats/). 53. Eckhoff PA. A malaria transmission-directed model of mosquito life cycle and ecology. Malar J. 2011;10:303. doi: https://doi.org/10.1186/1475- 2875-10-303. 54. Eckhoff PA. Malaria parasite diversity and transmission intensity affect development of parasitological immunity in a mathematical model. Malar J. 2012;11:419. doi: https://doi.org/10.1186/1475-2875-11-419. 55. Eckhoff PA. P. falciparum infection durations and infectiousness are shaped by antigenic variation and innate and adaptive host immunity in a mathematical model. PloS One. 2012:e44950. doi: https://doi.org/10.1371/journal.pone.0044950. 56. Koenker H, Kilian A, Zegers de Beyl C, Onyefunafoa EO, Selby RA, Abeku T et al. What happens to lost nets: a multi-country analysis of reasons for LLIN attrition using 14 household surveys in four countries. Malar J. 2014;13:464. doi: https://doi.org/10.1186/1475-2875-13-464. 57. Malaria diagnostics market and technology landscape (4th edition). Geneva: World Health Organization (for Unitaid); 2022 (https:// unitaid.org/assets/Malaria-Diagnostics-Market-and-Technology-Landscape.pdf). 58. Vector control interventions in the pipeline [website]. Geneva: World Health Organization; 2022 (https://www.who.int/observatories/ global-observatory-on-health-research-and-development/monitoring/vector-control-interventions-in-the-pipeline). 59. Envisioning insecticide resistance management and integrated vector management a ZERO by 40 perspective. Liverpool, United Kingdom: IVCC; 2022 (https://zeroby40.com/wp-content/uploads/2021/12/Zero_by_40_Perspective_Report.pdf). 60. WHO review of malaria vaccine clinical development [website]. Geneva: World Health Organization; 2022 (https://www.who.int/ observatories/global-observatory-on-health-research-and-development/monitoring/who-review-of-malaria-vaccine-clinical- development). 149 Data on number of insecticide-treated mosquito nets were collected from reports from national malaria programmes and other sources by the Alliance for Malaria Prevention and RBM Partnership to End Malaria. Country ITNs planned for distribution in 2020 ITNs distributed in 2020 ITNs remaining for distribution in 2021 Percentage of planned ITNs distributed in 2020 ITNs distributed in 2021 from 2020 campaigns ITNs planned for distribution in 2021 (including carry-over from 2020) ITNs distributed in 2021 (including carry-over from 2020) ITNs remaining for distribution in 2022 Percentage of ITNs planned for distribution in 2021 distributed in 2021 Afghanistan 2 833 365 2 833 365 0 100 NA NA NA NA NA Bangladesh 2 014 200 1 219 329 794 871 61 794 871 1 052 526 823 336 229 190 78 Benin1 8 609 873 7 652 166 957 707 89 443 484 957 707 443 484 514 223 46 Bhutan2 122 670 122 670 0 100 NA NA NA NA NA Bolivia (Plurinational State of)1 88 200 81 727 6 473 93 6 473 6 473 6 473 0 100 Botswana 89 179 80 525 8 654 90 0 8 654 0 8 654 0 Burundi3 7 016 519 6 784 964 231 555 97 231 555 231 555 231 555 0 100 Cambodia 793 359 793 068 291 99.6 291 829 358 559 790 269 568 67 Cameroon4 2 112 900 1 980 471 132 429 94 132 429 369 000 344 763 24 237 93 Central African Republic 2 861 765 2 312 311 549 454 81 0 1 626 470 0 1 626 470 0 Chad 8 779 988 8 686 550 93 438 99 0 93 438 0 93 438 0 Comoros1 444 750 412 022 32 728 93 30 891 32 728 30 891 1 837 94 Côte d'Ivoire 18 991 346 0 18 991 346 0 18 509 750 18 509 750 18 509 750 0 100 Democratic Republic of the Congo 29 056 290 15 541 956 13 514 334 53 13 514 334 16 717 712 16 717 712 0 100 Eritrea1 1 922 249 621 094 1 301 155 32 69 347 1 301 155 69 347 1 231 808 5 Eswatini 10 000 0 10 000 0 8 313 10 000 8 313 1 687 83 Ethiopia2 6 517 480 6 517 480 0 100 NA 7 897 450 7 897 450 0 100 Ghana 0 0 0 NA NA 18 948 893 15 976 996 2 971 897 84 Guinea-Bissau2 1 294 976 1 294 976 0 100 NA NA NA NA NA Haiti 1 216 186 971 530 244 656 80 0 244 656 0 244 656 0 Honduras 21 588 20 760 828 96 828 23 427 17 744 5 683 76 India 22 400 000 11 200 000 11 200 000 50 0 11 345 000 0 11 345 000 0 Indonesia1 3 632 014 3 354 382 277 632 92 50 350 277 632 50 350 227 282 18 Kenya 12 943 663 194 292 12 749 371 2 12 749 371 16 151 848 15 128 756 1 023 092 94 Liberia 0 0 0 NA NA 2 783 264 2 783 264 0 100 Madagascar 0 0 0 NA NA 13 703 700 13 329 038 374 662 97 Malawi2 0 0 0 NA NA 7 357 003 7 357 003 0 100 Mali1 7 620 011 7 479 747 140 264 98 140 264 140 264 140 264 0 100 Mauritania2 1 622 322 1 622 322 0 100 NA NA NA NA NA Mozambique1 17 467 640 17 255 566 212 074 99 212 074 212 074 212 074 0 100 Nicaragua 61 766 61 520 246 99.6 246 61 766 61 766 0 100 Niger 9 645 683 8 555 136 1 090 547 89 1 090 547 5 420 231 4 358 451 1 061 780 80 Nigeria 26 047 544 25 470 757 576 787 98 576 787 27 125 810 14 432 535 12 693 275 53 Pakistan 1 487 878 1 487 721 157 99.9 0 3 106 391 0 3 106 391 0 ANNEx 2 – NUMBER OF ITNs DISTRIBUTED ThROUGh CAMPAIGNS IN MALARIA ENDEMIC COUNTRIES, 2020–2022 150 W O RL D M AL AR IA R EP O RT 2 02 2 Data on number of insecticide-treated mosquito nets were collected from reports from national malaria programmes and other sources by the Alliance for Malaria Prevention and RBM Partnership to End Malaria. Country ITNs planned for distribution in 2020 ITNs distributed in 2020 ITNs remaining for distribution in 2021 Percentage of planned ITNs distributed in 2020 ITNs distributed in 2021 from 2020 campaigns ITNs planned for distribution in 2021 (including carry-over from 2020) ITNs distributed in 2021 (including carry-over from 2020) ITNs remaining for distribution in 2022 Percentage of ITNs planned for distribution in 2021 distributed in 2021 Afghanistan 2 833 365 2 833 365 0 100 NA NA NA NA NA Bangladesh 2 014 200 1 219 329 794 871 61 794 871 1 052 526 823 336 229 190 78 Benin1 8 609 873 7 652 166 957 707 89 443 484 957 707 443 484 514 223 46 Bhutan2 122 670 122 670 0 100 NA NA NA NA NA Bolivia (Plurinational State of)1 88 200 81 727 6 473 93 6 473 6 473 6 473 0 100 Botswana 89 179 80 525 8 654 90 0 8 654 0 8 654 0 Burundi3 7 016 519 6 784 964 231 555 97 231 555 231 555 231 555 0 100 Cambodia 793 359 793 068 291 99.6 291 829 358 559 790 269 568 67 Cameroon4 2 112 900 1 980 471 132 429 94 132 429 369 000 344 763 24 237 93 Central African Republic 2 861 765 2 312 311 549 454 81 0 1 626 470 0 1 626 470 0 Chad 8 779 988 8 686 550 93 438 99 0 93 438 0 93 438 0 Comoros1 444 750 412 022 32 728 93 30 891 32 728 30 891 1 837 94 Côte d'Ivoire 18 991 346 0 18 991 346 0 18 509 750 18 509 750 18 509 750 0 100 Democratic Republic of the Congo 29 056 290 15 541 956 13 514 334 53 13 514 334 16 717 712 16 717 712 0 100 Eritrea1 1 922 249 621 094 1 301 155 32 69 347 1 301 155 69 347 1 231 808 5 Eswatini 10 000 0 10 000 0 8 313 10 000 8 313 1 687 83 Ethiopia2 6 517 480 6 517 480 0 100 NA 7 897 450 7 897 450 0 100 Ghana 0 0 0 NA NA 18 948 893 15 976 996 2 971 897 84 Guinea-Bissau2 1 294 976 1 294 976 0 100 NA NA NA NA NA Haiti 1 216 186 971 530 244 656 80 0 244 656 0 244 656 0 Honduras 21 588 20 760 828 96 828 23 427 17 744 5 683 76 India 22 400 000 11 200 000 11 200 000 50 0 11 345 000 0 11 345 000 0 Indonesia1 3 632 014 3 354 382 277 632 92 50 350 277 632 50 350 227 282 18 Kenya 12 943 663 194 292 12 749 371 2 12 749 371 16 151 848 15 128 756 1 023 092 94 Liberia 0 0 0 NA NA 2 783 264 2 783 264 0 100 Madagascar 0 0 0 NA NA 13 703 700 13 329 038 374 662 97 Malawi2 0 0 0 NA NA 7 357 003 7 357 003 0 100 Mali1 7 620 011 7 479 747 140 264 98 140 264 140 264 140 264 0 100 Mauritania2 1 622 322 1 622 322 0 100 NA NA NA NA NA Mozambique1 17 467 640 17 255 566 212 074 99 212 074 212 074 212 074 0 100 Nicaragua 61 766 61 520 246 99.6 246 61 766 61 766 0 100 Niger 9 645 683 8 555 136 1 090 547 89 1 090 547 5 420 231 4 358 451 1 061 780 80 Nigeria 26 047 544 25 470 757 576 787 98 576 787 27 125 810 14 432 535 12 693 275 53 Pakistan 1 487 878 1 487 721 157 99.9 0 3 106 391 0 3 106 391 0 151 Data on number of insecticide-treated mosquito nets were collected from reports from national malaria programmes and other sources by the Alliance for Malaria Prevention and RBM Partnership to End Malaria. Country ITNs planned for distribution in 2020 ITNs distributed in 2020 ITNs remaining for distribution in 2021 Percentage of planned ITNs distributed in 2020 ITNs distributed in 2021 from 2020 campaigns ITNs planned for distribution in 2021 (including carry-over from 2020) ITNs distributed in 2021 (including carry-over from 2020) ITNs remaining for distribution in 2022 Percentage of ITNs planned for distribution in 2021 distributed in 2021 Papua New Guinea 1 483 074 1 183 982 299 091 80 299 091 1 236 059 1 071 802 164 257 87 Rwanda2 5 837 458 5 837 458 0 100 NA NA NA NA NA Sierra Leone 4 601 419 4 346 613 254 806 94 NA 254 806 0 254 806 0 Solomon Islands 7 530 7 530 0 100 NA 605 384 164 384 441 000 27 Somalia2 1 473 529 1 473 529 0 100 NA NA NA NA NA South Sudan 4 768 792 4 198 875 569 917 88 569 917 1 586 285 1 586 285 0 100 Sudan 4 625 027 4 626 027 0 100 NA NA NA NA NA Suriname2 6 864 6 864 0 100 NA 15 000 10 059 4 941 67 Thailand5 76 865 76 865 0 100 NA 220 873 118 300 102 573 54 Timor-Leste 150 652 140 878 9 774 94 9 774 29 283 19 948 9 335 68 Togo2 5 826 536 5 826 536 0 100 NA NA NA NA NA Uganda 29 184 557 23 728 961 5 455 596 81 1 410 031 5 455 596 1 410 031 4 045 565 26 United Republic of Tanzania2 8 728 803 8 728 803 0 100 NA 611 717 611 717 0 100 Zanzibar5 205 000 205 000 0 100 NA 746 420 712 872 33 548 96 Vanuatu 0 0 0 NA NA 70 747 37 090 33 657 52 Yemen 1 248 377 855 693 392 684 69 392 684 2 890 856 1 769 759 1 121 097 61 Zambia2 5 621 419 5 621 419 0 100 NA NA NA NA NA Zimbabwe2,5 443 231 443 231 0 100 NA 1 017 646 1 017 646 0 100 Total 272 014 537 201 916 671 70 098 865 74 51 243 702 171 286 607 128 020 998 43 265 609 75 ITN: insecticide-treated mosquito net; NA: not applicable. 1 The carry-over of ITNs from 2020 were distributed through routine channels. 2 Planned distribution was adjusted based on ITNs distributed; where ITN distribution was more than planned, ITNs distributed = ITNs planned for distribution. 3 The 2019 mass campaign resulted in carry-over of ITNs that were distributed in 2020 and 2021 through routine distribution channels. 4 The 2019 mass campaign resulted in carry-over of ITNs that were distributed in 2020 and 2021. 5 No data were reported on planned distribution; therefore adjustment was made that planned distribution = ITNs distributed. Note: Remaining nets not distributed through mass campaigns may be distributed through other channels (e.g. antenatal care). ANNEx 2 – NUMBER OF ITNs DISTRIBUTED ThROUGh CAMPAIGNS IN MALARIA ENDEMIC COUNTRIES, 2020–2022 152 W O RL D M AL AR IA R EP O RT 2 02 2 Data on number of insecticide-treated mosquito nets were collected from reports from national malaria programmes and other sources by the Alliance for Malaria Prevention and RBM Partnership to End Malaria. Country ITNs planned for distribution in 2020 ITNs distributed in 2020 ITNs remaining for distribution in 2021 Percentage of planned ITNs distributed in 2020 ITNs distributed in 2021 from 2020 campaigns ITNs planned for distribution in 2021 (including carry-over from 2020) ITNs distributed in 2021 (including carry-over from 2020) ITNs remaining for distribution in 2022 Percentage of ITNs planned for distribution in 2021 distributed in 2021 Papua New Guinea 1 483 074 1 183 982 299 091 80 299 091 1 236 059 1 071 802 164 257 87 Rwanda2 5 837 458 5 837 458 0 100 NA NA NA NA NA Sierra Leone 4 601 419 4 346 613 254 806 94 NA 254 806 0 254 806 0 Solomon Islands 7 530 7 530 0 100 NA 605 384 164 384 441 000 27 Somalia2 1 473 529 1 473 529 0 100 NA NA NA NA NA South Sudan 4 768 792 4 198 875 569 917 88 569 917 1 586 285 1 586 285 0 100 Sudan 4 625 027 4 626 027 0 100 NA NA NA NA NA Suriname2 6 864 6 864 0 100 NA 15 000 10 059 4 941 67 Thailand5 76 865 76 865 0 100 NA 220 873 118 300 102 573 54 Timor-Leste 150 652 140 878 9 774 94 9 774 29 283 19 948 9 335 68 Togo2 5 826 536 5 826 536 0 100 NA NA NA NA NA Uganda 29 184 557 23 728 961 5 455 596 81 1 410 031 5 455 596 1 410 031 4 045 565 26 United Republic of Tanzania2 8 728 803 8 728 803 0 100 NA 611 717 611 717 0 100 Zanzibar5 205 000 205 000 0 100 NA 746 420 712 872 33 548 96 Vanuatu 0 0 0 NA NA 70 747 37 090 33 657 52 Yemen 1 248 377 855 693 392 684 69 392 684 2 890 856 1 769 759 1 121 097 61 Zambia2 5 621 419 5 621 419 0 100 NA NA NA NA NA Zimbabwe2,5 443 231 443 231 0 100 NA 1 017 646 1 017 646 0 100 Total 272 014 537 201 916 671 70 098 865 74 51 243 702 171 286 607 128 020 998 43 265 609 75 ITN: insecticide-treated mosquito net; NA: not applicable. 1 The carry-over of ITNs from 2020 were distributed through routine channels. 2 Planned distribution was adjusted based on ITNs distributed; where ITN distribution was more than planned, ITNs distributed = ITNs planned for distribution. 3 The 2019 mass campaign resulted in carry-over of ITNs that were distributed in 2020 and 2021 through routine distribution channels. 4 The 2019 mass campaign resulted in carry-over of ITNs that were distributed in 2020 and 2021. 5 No data were reported on planned distribution; therefore adjustment was made that planned distribution = ITNs distributed. Note: Remaining nets not distributed through mass campaigns may be distributed through other channels (e.g. antenatal care). 153 ANNEx 3 - A. WhO AFRICAN REGION, a. WEST AFRICA EPIDEMIOLOGY Population denominator used to compute incidence and mortality rate: 421 million Parasites: P. falciparum (almost 100%) and other (<1%) Vectors: An. arabiensis, An. coluzzii, An. funestus s.l., An. gambiae s.l., An. hispaniola, An. labranchiae, An. melas, An. moucheti, An. multicolor, An. nili s.l., An. pharoensis and An. sergentii s.l. FUNDING (US$), 2010–2021 584.9 million (2010), 597.9 million (2015), 992.7 million (2021); 2010–2021: 70% increase Proportion of domestic sourcea in 2021: 23% a Domestic source excludes patient service delivery costs and out-of-pocket expenditure. INTERVENTIONS, 2010–2021 Countries with ≥80% coverage with either LLINs or IRS in 2021: Côte d'Ivoire, Mali, the Niger and Togo Countries with ≥50% coverage with either LLINs or IRS in 2021: Benin, Burkina Faso, Ghana, Liberia, Nigeria, Senegal and Sierra Leone Countries that implemented IPTp in 2021: Benin, Burkina Faso, Côte d’Ivoire, the Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Mauritania, the Niger, Nigeria, Senegal and Sierra Leone Countries with >30% IPTp3+ in 2021: Benin, Burkina Faso, Côte d’Ivoire, the Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, the Niger, Senegal, Sierra Leone and Togo Children treated with at least one dose of SMC per cycle in 2021: 40.4 million Note: No data for Guinea-Bissau in 2021. Percentage of suspected cases tested (reported):a 54% (2010), 73% (2015), 94% (2021) Number of ACT courses distributed: 32.2 million (2010), 47.4 million (2015), 85.4 million (2021) Number of any antimalarial treatment courses (incl. ACT) distributed: 32.2 million (2010), 49.4 million (2015), 86.5 million (2021) a No data for Guinea-Bissau in 2021. REPORTED CASES AND DEATHS,a 2010–2021 Total (presumed and confirmed) cases:b 30.6 million (2010), 56.8 million (2015), 68.3 million (2021) Confirmed cases: 6.8 million (2010), 36.4 million (2015), 63.2 million (2021) Percentage of total cases confirmed: 22.1% (2010), 64.1% (2015), 92.5% (2021) Deaths:c 39 000 (2010), 30 900 (2015), 26 500 (2021) a Includes malaria endemic countries only; b No data for Guinea-Bissau in 2021; c Nigeria only reports deaths in children aged under 5 years. Children aged under 5 years, presumed and confirmed cases:b 11.9 million (2010), 21.0 million (2015), 25.6 million (2021) Children aged under 5 years, percentage of total cases: 38.9% (2010), 37.0% (2015), 37.4% (2021) Children aged under 5 years, deaths:c 22 900 (2010), 22 100 (2015), 21 100 (2021) Children aged under 5 years, percentage of total deaths: 59% (2020), 72% (2015), 80% (2021) a Includes malaria endemic countries only; b No data for Guinea-Bissau in 2021; c No data for Guinea-Bissau, Mauritania and Togo in 2021. ESTIMATED CASES AND DEATHS, 2010–2021 Cases: 118.0 million (2010), 108.5 million (2015), 120.1 million (2021); 2010–2021: 2% increase Deaths: 371 700 (2010), 303 900 (2015), 328 200 (2020); 2010–2021: 12% decrease ACCELERATION TO ELIMINATION Countries with subnational/territorial elimination programme: the Gambia, Mauritania, the Niger and Senegal Countries with nationwide elimination programme: Cabo Verde Countries part of the E-2025 initiative: Cabo Verde Zero indigenous cases for 3 consecutive years (2019–2021): Cabo Verde Certification in process: Cabo Verde Certified as malaria free since 2010: Algeria (2019) THERAPEUTIC EFFICACY STUDIES (CLINICAL AND PARASITOLOGICAL FAILURE AMONG PATIENTS WITH P. FALCIPARUM MALARIA, %) Medicine Study years No. of studies Min. Median Max. Percentile 25 75 AL 2015–2021 56 0.0 1.2 42.6 0.0 3.5 AS-AQ 2015–2019 46 0.0 0.0 9.8 0.0 2.0 DHA-PPQ 2016–2021 10 0.0 1.8 18.7 0.0 2.9 AL: artemether-lumefantrine; AS-AQ: artesunate-amodiaquine; DHA-PPQ: dihydroartemisinin-piperaquine. STATUS OF INSECTICIDE RESISTANCEa PER INSECTICIDE CLASS (2010–2020) AND USE OF EACH CLASS FOR MALARIA VECTOR CONTROL (2020) Nu m be r o f c ou nt rie s 12 4 8 16 LLINsb 12 – – – IRSb 1 0 2 4 0 ■ Resistance confirmed ■ Tested but resistance not confirmed ■ Not monitored Pyrethroids OrganophosphatesOrganochlorines Carbamates a Resistance is considered confirmed when it was detected to one insecticide in the class, in at least one malaria vector from one collection site. b Number of countries that reported using the insecticide class for malaria vector control (2020). B. Malaria fundinga by source, 2010–2021 US $ (m illi on ) 2016 20182017201520142013201220112010 400 200 600 800 1000 0 ■ Domestic ■ Global Fund ■ World Bank ■ USAID ■ UK ■ Other 2019 2020 2021 Global Fund: Global Fund to Fight AIDS, Tuberculosis and Malaria; UK: United Kingdom of Great Britain and Northern Ireland; USAID: United States Agency for International Development. a Excludes patient service delivery costs and out-of-pocket expenditure; no domestic funding data were reported for Guinea-Bissau, Mauritania and Togo in 2021. A. P. falciparum parasite rate (Pf PR), 2021 Liberia Burkina Faso Guinea-Bissau Senegal Cabo Verde Guinea Gambia Côte d’Ivoire Niger Benin Mali Ghana Sierra Leone Togo Nigeria Mauritania US$ 86420 10 ■ Domestic ■ International C. Malaria fundinga per person at risk, average 2019–2021 Pf PR2-10 0 Insucient data Not applicable <0.1 0.25 0.5 1 2 >80 4 8 16 32 a Data are not collected on out-of-pocket expenditure and excluded patient service delivery costs. 154 D. Share of estimated malaria cases, 2021 KEY MESSAGES ■ In 2021, there were 15 malaria endemic countries in west Africa. Algeria was certified malaria free in May 2019, following 3 consecutive years with zero indigenous cases. Cabo Verde has had 3 consecutive years (2019–2021) of zero indigenous cases and has started the certification process. The high burden high impact (HBHI) initiative was initiated in Burkina Faso, Ghana, the Niger and Nigeria in 2019, and in Mali in 2020, leading to evidence-based national strategic plans and funding requests. In all countries of this subregion except Algeria and Cabo Verde, malaria transmission is year round and almost exclusively due to Plasmodium falciparum, with strong seasonality in the Sahelian countries. ■ Also in 2021, the subregion had about 120 million estimated cases and about 328 000 estimated deaths – a 2% increase and a 12% decrease compared with 2010, respectively. Five countries accounted for more than 80% of the estimated cases: Nigeria (54%), Burkina Faso (7%), the Niger (7%), Côte d’Ivoire (6%) and Mali (6%). About 68 million cases were reported in the public and private sectors and in the community, of which 37.4% were in children aged under 5 years, and 63 million (92.5%) were confirmed. The proportion of total cases that were confirmed has improved substantially over time, from only 22.1% in 2010 to 92.5% in 2021. Most deaths were in children aged under 5 years (80%). ■ In 11 of the 15 countries in this subregion, where routine distribution of long-lasting insecticidal nets (LLINs) or use of indoor residual spraying (IRS) are still applicable, 50% or more of the population had access to these interventions, despite country-specific variations. Eleven countries in the region implemented seasonal malaria chemoprevention (SMC), providing about 40.4 million children with at least one dose of SMC per cycle. Fifteen countries implemented intermittent preventive treatment of malaria in pregnancy (IPTp) in 2021 and 13 had more than 30% coverage with IPTp3. Eleven countries are also implementing SMC. Five countries met the Global technical strategy for malaria 2016–2030 (GTS) target by reducing case incidence by at least 40% or reaching zero malaria cases in 2021 compared with 2015: Algeria (which is already certified malaria free), Cabo Verde (zero cases), the Gambia, Ghana and Mauritania. Hence, these countries are currently on track to achieving the GTS targets in 2025 and 2030. In seven countries, although there has been progress towards meeting the GTS target, reductions were less than 40%: Burkina Faso, Guinea, Mali, the Niger, Senegal, Sierra Leone and Togo. In Benin, Côte d’Ivoire, Guinea-Bissau, Liberia and Nigeria, incidence increased in 2021 compared with 2015. After a large increase in indigenous cases in Cabo Verde between 2016 and 2017, the country has reported zero indigenous cases since February 2018. ■ Vector resistance to pyrethroids was confirmed in 91% of sites, to organochlorines in 96%, to carbamates in 43% and to organophosphates in 26%. The intensity of pyrethroid resistance in this region is high overall. Eleven countries have developed insecticide resistance monitoring and management plans. ■ Total funding for malaria has continued to increase over the past decade, largely stemming from international sources. In 2021, domestic expenditures represented only 23% of total funding for malaria. From 2010 through 2021, there was a 70% increase in total funding; from 2020 to 2021, there was a 12% increase in total funding, stemming principally from increases in domestic expenditures (29%) and from the Global Fund to Fight AIDS, Tuberculosis and Malaria (Global Fund) (16%). Funding per person at risk ranged from US$ 0.15 in Mauritania to US$ 9.37 in Liberia over a 3-year average. ■ The Nouakchott Declaration was adopted In 2013 and the new Sahel Malaria Elimination Initiative (SaME) was launched in 2018 by ministers of the eight Sahelian countries (Burkina Faso, Cabo Verde, Chad, the Gambia, Mali, Mauritania, the Niger and Senegal) to accelerate implementation of high- impact strategies towards eliminating malaria by 2030. In line with these initiatives, an action plan was adopted in 2019. In addition to Cabo Verde as an eliminating country, the Gambia, Mauritania, the Niger and Senegal have reoriented their programmes towards malaria subnational elimination. ■ Challenges include inadequate political commitment and leadership, weak malaria programme management, insufficient prioritization and sustainability of interventions, inappropriate application of larviciding, inadequate domestic financing and weak surveillance systems (including a lack of well- functioning vital registration systems and shortage of human resources to carry out malaria activities). The coronavirus (COVID-19) pandemic disrupted diagnostic services, as indicated by the 18% decrease in diagnostic tests in 2020 compared with 2019; also, numbers of diagnostic tests decreased in all countries except the Niger and Senegal. In 2021, the number of suspects tested increased again as service disruptions due to the COVID-19 pandemic were resolved. Tackling the abovementioned issues could greatly improve the malaria situation in the region. I. Change in estimated malaria incidence and mortality rates, 2015–2021 2020 milestone: -40% Cabo Verdea Mauritania Gambia Ghana Togo Niger Senegal Sierra Leone Guinea Burkina Faso Mali Benin Liberia Nigeria Côte d’Ivoire Guinea-Bissau f Reduction Increase p 50%0%-50%-100% 100% ■ Incidence ■ Mortality . E. Percentage of population with access to either LLINs or IRS, 2021 Source: ITN coverage model from MAP Niger Togo Mali Côte d’Ivoire Sierra Leone Benin Ghana Liberia Senegal Burkina Faso Nigeria Guinea Gambia Mauritania Guinea-Bissau 40200 1008060 J. Total number of suspected malaria cases tested, 2010–2021 201520142013201220112010 2016 2017 2018 2019 2020 2021 100 000 000 80 000 000 60 000 000 40 000 000 20 000 000 0 ■ Mexico F. Estimated number of cases in countries that reduced case incidence by ≥40% in 2021 compared with 2015 201520142013201220112010 2016 2017 2018 2019 2020 2021 12 000 000 9 000 000 6 000 000 3 000 000 0 ■ Mauritania ■ Gambia ■ Ghana Baseline (2015) G. Estimated number of cases in countries that reduced case incidence by <40% in 2021 compared with 2015 201520142013201220112010 2016 2017 2018 2019 2020 2021 40 000 000 30 000 000 20 000 000 10 000 000 0 ■ Senegal ■ Togo ■ Sierra Leone ■ Guinea ■ Mali ■ Niger ■ Burkina Faso Baseline (2015) H. Estimated number of cases in countries with an increase or no change in case incidence, 2015–2021 201520142013201220112010 2016 2017 2018 2019 2020 2021 80 000 000 60 000 000 40 000 000 20 000 000 0 ■ Guinea-Bissau ■ Liberia ■ Benin ■ Côte d’Ivoire ■ Nigeria Baseline (2015) Cabo Verde 420 6 8 0 7 ■ 2015 ■ 2021 IRS: indoor residual spraying; ITN: insecticide-treated mosquito net; LLIN: long-lasting insecticidal net; MAP: Malaria Atlas Project. Note: Algeria was certified malaria free in 2019 and Cabo Verde is a low transmission country where vector control is targeted to foci. K. Reported indigenous cases in countries with national elimination activities, 2015 versus 2021 ■ Nigeria ■ Burkina Faso ■ Niger ■ Mali ■ Côte d’Ivoire ■ Ghana ■ Benin ■ Guinea ■ Sierra Leone ■ Togo ■ Liberia 6%6% 4% 4% 4% 2% 2% 2% 7% 7% Senegal, 0.8% Gambia, 0.2% Guinea-Bissau, 0.2% Mauritania, 0.1% 54% a This country achieved the 40% reduction in mortality rate in 2015; since then, there has been no change. Note: Algeria was certified malaria free in 2019. Cabo Verde met the 2020 GTS milestone with zero malaria cases and had zero cases in 2021 again. W O RL D M AL AR IA R EP O RT 2 02 2 155 ANNEx 3 - A. WhO AFRICAN REGION, b. CENTRAL AFRICA A. P. falciparum parasite rate (Pf PR), 2021EPIDEMIOLOGY Population denominator used to compute incidence and mortality rate: 203 million Parasites: P. falciparum (100%) Vectors: An. arabiensis, An. funestus s.l., An. gambiae s.l., An. melas, An. moucheti, An. nili s.l. and An. pharoensis. FUNDING (US$), 2010–2021 264.0 million (2010), 396.8 million (2015), 422.6 million (2021); 2010–2021: 60% increase Proportion of domestic sourcea in 2021: 16% a Domestic source excludes patient service delivery costs and out-of-pocket expenditure. INTERVENTIONS, 2010–2021 Countries with ≥80% coverage with either LLINs or IRS in 2021: None Countries with ≥50% coverage with either LLINs or IRS in 2021: Burundi, Cameroon, the Central African Republic, Chad, the Congo and the Democratic Republic of the Congo Countries that implemented IPTp in 2021: Angola, Burundi, Cameroon, the Central African Republic, Chad, the Congo, the Democratic Republic of the Congo and Sao Tome and Principe Countries with >30% IPTp3+ in 2021: Burundi, Cameroon, the Central African Republic and the Democratic Republic of the Congo Percentage of suspected cases tested (reported):a 46% (2010), 92% (2015), 92% (2021) Number of ACT courses distributed:a 18.2 million (2010), 22.4 million (2015), 36.0 million (2021) Number of any antimalarial treatment courses (incl. ACT) distributed:a 19.1 million (2010), 22.4 million (2015), 41.9 million (2021) a No data for Equatorial Guinea in 2021. REPORTED CASES AND DEATHS,a 2010–2021 Total (presumed and confirmed) cases:b 20.4 million (2010), 26.6 million (2015), 50.2 million (2021) Confirmed cases:b 6.1 million (2010), 23.4 million (2015), 45.2 million (2021) Percentage of total cases confirmed: 30.1% (2010), 87.9% (2015), 90.0% (2021) Deaths:b 40 400 (2010), 58 200 (2015), 48 500 (2021) Children aged under 5 years, presumed and confirmed cases:b 9.1 million (2010), 11.3 million (2015), 22.1 million (2021) Children aged under 5 years, percentage of total cases: 44.9% (2010), 42.6% (2015), 44.1% (2021) Children aged under 5 years, deaths:b 26 000 (2010), 37 100 (2015), 28 000 (2021) Children aged under 5 years, percentage of total deaths: 64% (2010), 64% (2015), 58% (2021) a Includes malaria endemic countries only; b No data for Equatorial Guinea in 2021. ESTIMATED CASES AND DEATHS, 2010–2021 Cases: 43.4 million (2010), 42.7 million (2015), 57.1 million (2021); 2010–2021: 32% increase Deaths: 145 600 (2010), 109 500 (2015), 136 800 (2020); 2010–2021: 6% decrease ACCELERATION TO ELIMINATION Countries with nationwide elimination programme: Sao Tome and Principe Countries part of the E-2025 initiative: Sao Tome and Principe THERAPEUTIC EFFICACY STUDIES (CLINICAL AND PARASITOLOGICAL FAILURE AMONG PATIENTS WITH P. FALCIPARUM MALARIA, %) Medicine Study years No. of studies Min. Median Max. Percentile 25 75 AL 2015–2021 29 0.0 1.4 13.6 0.0 3.6 AS-AQ 2015–2021 29 0.0 0.0 7.7 0.0 4.8 DHA-PPQ 2015–2017 9 0.0 0.0 2.0 0.0 0.0 AL: artemether-lumefantrine; AS-AQ: artesunate-amodiaquine; DHA-PPQ: dihydroartemisinin-piperaquine. STATUS OF INSECTICIDE RESISTANCEa PER INSECTICIDE CLASS (2010–2020) AND USE OF EACH CLASS FOR MALARIA VECTOR CONTROL (2020) Nu m be r o f c ou nt rie s 8 6 4 2 10 LLINsb 9 – – – IRSb 2 0 1 2 0 Pyrethroids OrganophosphatesOrganochlorines Carbamates ■ Resistance confirmed ■ Tested but resistance not confirmed a Resistance is considered confirmed when it was detected to one insecticide in the class, in at least one malaria vector from one collection site. b Number of countries that reported using the insecticide class for malaria vector control (2020). Pf PR2-10 0 Not applicable <0.1 0.25 0.5 1 2 >80 4 8 16 32 B. Malaria fundinga by source, 2010–2021 US $ (m illi on ) 2016 2018 2019 2020 20212017201520142013201220112010 100 200 400 300 500 0 ■ Domestic ■ Global Fund ■ World Bank ■ USAID ■ UK ■ Other Global Fund: Global Fund to Fight AIDS, Tuberculosis and Malaria; UK: United Kingdom of Great Britain and Northern Ireland; USAID: United States Agency for International Development. a Excludes patient service delivery costs and out-of-pocket expenditure; no domestic funding data were reported for Chad, the Congo and Sao Tome and Principe in 2021. Cameroon Burundi Central African Republic Congo Equatorial Guinea Democratic Republic of the Congo Chad Angola Sao Tome and Principe Gabon US$ 3210 4 ■ Domestic ■ International C. Malaria fundinga per person at risk, average 2019–2021 a Data are not collected on out-of-pocket expenditure and excluded patient service delivery costs; total funding includes negative disbursements due to under expenditure of international funding. 156 KEY MESSAGES ■ About 203 million people living in the 10 countries of central Africa are at high risk of malaria. Malaria transmission, almost exclusively due to P. falciparum, occurs throughout the year except in the north of Cameroon, northern Chad and the southern part of the Democratic Republic of the Congo. The HBHI initiative has been initiated in Cameroon and the Democratic Republic of the Congo. ■ In 2021, the subregion had more than 57 million estimated cases and almost 137 000 estimated deaths – a 32% increase and a 6% decrease compared with 2010, respectively. Three countries in the region accounted for more than 80% of the estimated cases: the Democratic Republic of the Congo (53%), Angola (15%) and Cameroon (12%). A similar distribution was seen for estimated malaria deaths, which were mainly observed in the Democratic Republic of the Congo (58%), Angola (13%) and Cameroon (10%). More than 50 million cases were reported in the public and private sectors and in the community; of these, 44% were in children aged under 5 years and 45.2 million (90%) were confirmed. The proportion of total cases that were confirmed has improved substantially over time, from only 30% in 2010. ■ None of the countries in the subregion met the GTS target of a 40% reduction in estimated incidence by 2021 compared with 2015. Two countries, Cameroon and Equatorial Guinea, did reduce incidence, but by less than 40%. Five countries saw an increase of more than 5% in estimated malaria incidence between 2015 and 2021; Burundi had the largest increase (46%), followed by Angola (44%), Sao Tome and Principe (19%), Chad and the Democratic Republic of the Congo (each 8%). Hence, none of these countries is currently on track to achieving the GTS targets in 2025 and 2030. Sao Tome and Principe, which is part of the malaria eliminating countries for 2025 (E-2025) initiative for elimination, reported a 40.6% increase in the number of cases in 2021 compared with 2020. Coverage of preventive vector control measures remains low in the subregion, except in the Congo which has almost 80% coverage. In 2021, Cameroon and the Democratic Republic of the Congo conducted LLIN mass campaigns. Additionally, Cameroon and Chad are implementing SMC in targeted areas of the country, covering approximately 4.4 million children with at least one dose of SMC per cycle in 2021. ■ Vector resistance to pyrethroids was confirmed in 86% of sites, to organochlorines in 90%, to carbamates in 21% and to organophosphates in 6%. Vector resistance to pyrethroids and to organochlorines was confirmed in all countries of the subregion. Six countries have developed insecticide resistance monitoring and management plans. ■ Total funding has increased consistently in the subregion since 2010 (60%), with domestic expenditures representing 16% of total funding in 2021. Total funding has remained relatively stable over the past 3 years; however, since 2020, there has been a 7% decrease in domestic expenditures, which was already low as a proportion of total funding. Total funding per person at risk in 2021 ranged from US$ 0.13 in Gabon to US$ 3.70 in Cameroon over a 3-year average. These trends highlight the need for an increase in total funding and a push for more domestic funding. ■ The performance of the surveillance system varies across countries in the region, as can be seen through the completeness of public sector data reported for 2021, with all countries except Burundi and Sao Tome and Principe reporting a rate of less than 100%. Reporting rates were particularly low in the Congo in 2020 and 2021, at 22% and 76%, respectively. Equatorial Guinea has not reported data for the past 2 years. Additional challenges include insufficient domestic and international funding, and frequent malaria outbreaks. The COVID-19 pandemic disrupted diagnostic services, as indicated by the 10% decrease in diagnostic tests in 2020 compared with 2019; numbers of diagnostic tests decreased in all countries except the Democratic Republic of the Congo and Gabon. In 2021, the number of suspects tested increased again as service disruptions due to the COVID-19 pandemic were resolved. Tackling the abovementioned issues could greatly improve the malaria situation in the region. D. Share of estimated malaria cases, 2021 H. Change in estimated malaria incidence and mortality rates, 2015–2021 2020 milestone: -40% Equatorial Guinea Cameroon Central African Republic Gabon Congo Democratic Republic of the Congo Chad Sao Tome and Principea Angola Burundi f Reduction Increase p 50%0%-50%-100% 100% ■ Incidence ■ Mortality E. Percentage of population with access to either LLINs or IRS, 2021 Source: ITN coverage model from MAP Congo Cameroon Democratic Republic of the Congo Burundi Central African Republic Chad Equatorial Guinea Angola Gabon 40200 1008060 201520142013201220112010 2016 2017 2018 2019 2020 2021 75 000 000 60 000 000 45 000 000 30 000 000 15 000 000 0 ■ Mexico I. Total number of suspected malaria cases tested, 2010–2021 F. Estimated number of cases in countries that reduced case incidence by <40% in 2021 compared with 2015 201520142013201220112010 2016 2017 2018 2019 2020 2021 7 500 000 6 000 000 4 500 000 3 000 000 1 500 000 0 ■ Equatorial Guinea ■ Cameroon Baseline (2015) G. Estimated number of cases in countries with an increase or no change in case incidence, 2015–2021 201520142013201220112010 2016 2017 2018 2019 2020 2021 50 000 000 40 000 000 30 000 000 20 000 000 10 000 000 0 Baseline (2015) ■ Sao Tome and Principe ■ Gabon ■ Congo ■ Burundi ■ Central African Republic ■ Chad ■ Angola ■ Democratic Republic of the Congo IRS: indoor residual spraying; ITN: insecticide-treated mosquito net; LLIN: long-lasting insecticidal net; MAP: Malaria Atlas Project. Note: Sao Tome and Principe is a low transmission country where vector control is targeted to foci. Sao Tome and Principe 200010000 3000 2056 ■ 2015 ■ 2021 2719 J. Reported indigenous cases in countries with national elimination activities, 2015 versus 2021 ■ Democratic Republic of the Congo ■ Angola ■ Cameroon ■ Burundi ■ Chad ■ Central African Republic ■ Congo 6% 6% 3% 2% 12% 15% Gabon, 0.9% Equatorial Guinea, 0.6% Sao Tome and Principe, <0.1%53% a This country achieved the 40% reduction in mortality rate in 2015; since then, there has been no change. W O RL D M AL AR IA R EP O RT 2 02 2 157 ANNEx 3 - A. WhO AFRICAN REGION, c. COUNTRIES WITh hIGh TRANSMISSION IN EAST AND SOUThERN AFRICA A. P. falciparum parasite rate (Pf PR), 2021EPIDEMIOLOGY Population denominator used to compute incidence and mortality rate: 381 million Parasites: P. falciparum (almost 100%), P. vivax (<1%) and other (<1%) Vectors: An. arabiensis, An. funestus s.l., An. gambiae s.l., An. gambiae s.s., An. leesoni, An. nili, An. pharoensis, An. rivulorum, An. stephensi s.l.a and An. vaneedeni. a A potential vector identified. FUNDING (US$), 2010–2021 799.7 million (2010), 773.4 million (2015), 940.4 million (2021); 2010–2021: 18% increase Proportion of domestic sourcea in 2021: 17% a Domestic source excludes patient service delivery costs and out-of-pocket expenditure. INTERVENTIONS, 2010–2021 Countries with ≥80% coverage with either LLINs or IRS in 2021: None Countries with ≥50% coverage with either LLINs or IRS in 2021: Kenya, Madagascar, Malawi, Mozambique, Rwanda, South Sudan, Uganda, the United Republic of Tanzania and Zambia Countries that implemented IPTp in 2021: Kenya, Malawi, Mozambique, South Sudan, Uganda, the United Republic of Tanzania (mainland), Zambia and Zimbabwe Countries with >30% IPTp3+ in 2021: Kenya, Madagascar, Malawi, Mozambique, Uganda and Zambia Children treated with at least one dose of SMC per cycle in 2021: 200 000 Percentage of suspected cases tested (reported): 38% (2010), 80% (2015), 98% (2021) Number of ACT courses distributed: 67.9 million (2010), 108.2 million (2015), 105.8 million (2021) Number of any antimalarial treatment courses (incl. ACT) distributed: 68.0 million (2010), 109.9 million (2015), 107.4 million (2021) REPORTED CASES AND DEATHS,a 2010–2021 Total (presumed and confirmed) cases: 53.3 million (2010), 59.0 million (2015), 56.4 million (2021) Confirmed cases: 8.6 million (2010), 36.2 million (2015), 53.5 million (2021) Percentage of total cases confirmed: 16.1% (2010), 61.5% (2015), 94.9% (2021) Deaths: 70 700 (2010), 38 400 (2015), 15 200 (2021) Children aged under 5 years, presumed and confirmed cases: 21.6 million (2010), 17.6 million (2015), 19.2 million (2021) Children aged under 5 years, percentage of total cases: 40.5% (2010), 29.9% (2015), 34.1% (2021) Children aged under 5 years, deaths:b 25 300 (2010), 10 400 (2015), 7200 (2021) Children aged under 5 years, percentage of total deaths: 36% (2010), 27% (2015), 47% (2021) a Includes malaria endemic countries only; b No data for Madagascar and Mozambique in 2021. ESTIMATED CASES AND DEATHS, 2010–2021 Cases: 55.1 million (2010), 59.8 million (2015), 56.4 million (2021); 2010–2021: 2% increase Deaths: 135 300 (2010), 127 900 (2015), 128 000 (2021); 2010–2021: 5% decrease ACCELERATION TO ELIMINATION Countries with subnational/territorial elimination programme: the United Republic of Tanzania (Zanzibar) THERAPEUTIC EFFICACY STUDIES (CLINICAL AND PARASITOLOGICAL FAILURE AMONG PATIENTS WITH P. FALCIPARUM MALARIA, %) Medicine Study years No. of studies Min. Median Max. Percentile 25 75 AL 2015–2019 49 0.0 1.4 13.9 0.0 3.0 AS-AQ 2016–2018 14 0.0 0.0 2.0 0.0 0.6 DHA-PPQ 2015–2019 13 0.0 0.0 6.0 0.0 1.3 AL: artemether-lumefantrine; AS-AQ: artesunate-amodiaquine; DHA-PPQ: dihydroartemisinin-piperaquine. STATUS OF INSECTICIDE RESISTANCEa PER INSECTICIDE CLASS (2010–2020) AND USE OF EACH CLASS FOR MALARIA VECTOR CONTROL (2020) Nu m be r o f c ou nt rie s 9 6 3 12 LLINsb 10 – – – IRSb 0 2 1 8 0 Pyrethroids OrganophosphatesOrganochlorines Carbamates ■ Resistance confirmed ■ Tested but resistance not confirmed ■ Not monitored a Resistance is considered confirmed when it was detected to one insecticide in the class, in at least one malaria vector from one collection site. b Number of countries that reported using the insecticide class for malaria vector control (2020). Malaria free/ sporadic Not applicable Pf PR2-10 <0.1 0.25 0.5 1 2 >80 4 8 16 32 B. Malaria fundinga by source, 2010–2021 US $ (m illi on ) 2016 2017 2018 2019 2020 2021201520142013201220112010 1 000 750 500 250 1 250 0 ■ Domestic ■ Global Fund ■ World Bank ■ USAID ■ UK ■ Other Global Fund: Global Fund to Fight AIDS, Tuberculosis and Malaria; UK: United Kingdom of Great Britain and Northern Ireland; USAID: United States Agency for International Development. a Excludes patient service delivery costs and out-of-pocket expenditure; no domestic funding data were reported for Kenya and Uganda in 2021. Rwanda Zambia Zimbabwe Malawi United Republic of Tanzania South Sudan Mozambique Uganda Madagascar Kenya Ethiopia US$ 420 6 8 ■ Domestic ■ International C. Malaria fundinga per person at risk, average 2019–2021 a Data are not collected on out-of-pocket expenditure and excluded patient service delivery costs; total funding includes negative disbursements due to under expenditure of international funding. 158 KEY MESSAGES ■ About 381 million people in the 11 countries with high transmission in east and southern Africa are at high risk of malaria. Malaria transmission is almost exclusively due to P. falciparum (except in Ethiopia), and is highly seasonal in Ethiopia, Madagascar and Zimbabwe, and in coastal and highland areas of Kenya. Malaria transmission is stable in most of Malawi, Mozambique, South Sudan, Uganda, the United Republic of Tanzania and Zambia. The HBHI initiative has been initiated in Mozambique and Uganda. ■ The subregion had almost 56 million estimated cases and about 128 000 estimated deaths, representing a 2% increase and 5% decrease compared with 2010, respectively. Three countries accounted for more than 50% of the estimated cases: Uganda (23%), Mozambique (18%) and the United Republic of Tanzania (14%). In the public and private sectors and the community, 56.4 million cases were reported, of which 34.1% were in children aged under 5 years and 53 million (94.9%) were confirmed. The proportion of total cases that were confirmed improved substantially over time, from only 16% in 2010. A significantly lower number of deaths was reported in 2021 (15 300) compared with 2010 (70 700) and 2015 (38 400). ■ Ethiopia, Rwanda and Zimbabwe achieved the GTS target of a 40% reduction in incidence by 2021 compared with the GTS baseline in 2015. Hence, these countries are currently on track to achieve the GTS targets in 2025 and 2030. Although the GTS target was not met in the other countries in the subregion, Kenya, Malawi, Mozambique, the United Republic of Tanzania and Zambia did reduce incidence, but by less than 40% in 2021 compared with 2015. Increases in incidence were seen in Madagascar, South Sudan and Uganda. In nine countries, 50% or more of the population had access to LLINs or IRS in 2021 and eight countries implemented IPTp, with six countries achieving the target of more than 30% of pregnant women attending antenatal care (ANC) receiving three or more doses of IPTp. Mozambique and Uganda implemented SMC for the first time, providing about 200 000 children with at least one dose of SMC per cycle in 2021. ■ Compared with 2020, in 2021, South Sudan had an increase of 74% in the number of reported malaria cases – from about 1.8 million to 3.1 million – the latter being closer to the number of cases that were reported in 2019 (4 million). The decrease in 2020 was likely due to COVID-19 related disruptions. Of concern, the reporting rate in South Sudan in 2021 was only 30%, meaning that the number of cases could be three times higher than the number reported. Reported cases more than halved in Zanzibar (United Republic of Tanzania), from about 14 100 cases in 2020 to 6000 cases in 2021, as the island strengthened and accelerated its efforts towards malaria elimination. Between 2017 and 2021, the number of reported cases in Rwanda decreased from 5.9 million to 1.2 million – a total reduction of 80%. It appears that the COVID-19 pandemic in 2020 did not affect diagnostic services, given that there was a 14% increase in diagnostic tests in 2020 compared with 2019; increases in testing were reported in all countries except Mozambique and Rwanda. ■ Vector resistance to pyrethroids was confirmed in 74% of sites, to organochlorines in 40%, to carbamates in 27% and to organophosphates in 16%. Vector resistance to pyrethroids, organochlorines and carbamates was confirmed in all countries except South Sudan, which did not report resistance monitoring. Eleven countries have developed insecticide resistance monitoring and management plans. ■ Total funding in the subregion has increased by 18% since 2010, showing fluctuations over the past decade. Domestic expenditure represented 17% of total funding in 2021. Funding per person at risk ranged from US$ 1.07 in Ethiopia to US$ 6.17 in Rwanda over a 3-year average. ■ Challenges include frequent epidemics, humanitarian and health emergencies, inadequate response (South Sudan) due to political instability, inadequate funding for interventions and human resources, delays in delivery of critical commodities and weak surveillance systems (including lack of early warning systems for detection of outbreaks) in several countries. D. Share of estimated malaria cases, 2021 I. Change in estimated malaria incidence and mortality rates, 2015–2021 2020 milestone: -40% Ethiopia Zimbabwe Rwanda Zambia United Republic of Tanzania Malawi Kenya Mozambique South Sudan Uganda Madagascar f Reduction Increase p 50%0%-50%-100% 100% ■ Incidence ■ Mortality E. Percentage of population with access to either LLINs or IRS, 2021 Source: ITN coverage model from MAP Uganda Rwanda Mozambique South Sudan Kenya Madagascar Zambia Malawi United Republic of Tanzania Zimbabwe Ethiopia 40200 1008060 J. Total number of suspected malaria cases tested, 2010–2021 201520142013201220112010 2016 2017 2018 2019 2020 2021 150 000 000 125 000 000 100 000 000 75 000 000 50 000 000 25 000 000 0 ■ Mexico 23% 18% 1%3% 5% 7% 6% 6% 8% 9% 14% ■ Uganda ■ Mozambique ■ United Republic of Tanzania ■ Madagascar ■ Malawi ■ Ethiopia ■ Zambia ■ Kenya ■ South Sudan ■ Rwanda ■ Zimbabwe IRS: indoor residual spraying; ITN: insecticide-treated mosquito net; LLIN: long-lasting insecticidal net; MAP: Malaria Atlas Project. F. Estimated number of cases in countries that reduced case incidence by ≥40% in 2021 compared with 2015 201520142013201220112010 2016 2017 2018 2019 2020 2021 20 000 000 16 000 000 12 000 000 8 000 000 4 000 000 0 Baseline (2015) ■ Rwanda ■ Zimbabwe ■ Ethiopia G. Estimated number of cases in countries that reduced case incidence by <40% in 2021 compared with 2015 201520142013201220112010 2016 2017 2018 2019 2020 2021 30 000 000 24 000 000 18 000 000 12 000 000 6 000 000 0 ■ Zambia ■ Kenya ■ Malawi ■ United Republic of Tanzania ■ Mozambique ■ Senegal ■ Togo ■ Sierra Leone ■ Guinea ■ Mali ■ Niger ■ Burkina Faso Baseline (2015) H. Estimated number of cases in countries with an increase or no change in case incidence, 2015–2021 201520142013201220112010 2016 2017 2018 2019 2020 2021 25 000 000 20 000 000 15 000 000 10 000 000 5 000 000 0 ■ Madagascar ■ South Sudan ■ Uganda Baseline (2015) W O RL D M AL AR IA R EP O RT 2 02 2 159 ANNEx 3 - A. WhO AFRICAN REGION, d. COUNTRIES WITh LOW TRANSMISSION IN EAST AND SOUThERN AFRICA B. Malaria fundinga by source, 2010–2021 Global Fund: Global Fund to Fight AIDS, Tuberculosis and Malaria; UK: United Kingdom of Great Britain and Northern Ireland. a Excludes patient service delivery costs and out-of-pocket expenditure; no domestic funding data were reported for Botswana in 2021. A. Confirmed malaria cases per 1000 population, 2021 C. Malaria fundinga per person at risk, average 2019–2021 a Data are not collected on out-of-pocket expenditure and excluded patient service delivery costs; total funding includes negative disbursements due to under expenditure of international funding. EPIDEMIOLOGY Population denominator used to compute incidence and mortality rate: 14 million Parasites: P. falciparum (92%), P. vivax (8%) and other (<1%) Vectors: An. arabiensis, An. funestus s.l., An. funestus s.s., An. gambiae s.l. and An. gambiae s.s. FUNDING (US$), 2010–2021 72.6 million (2010), 27.3 million (2015), 42.0 million (2021); 2010–2021: 42% decrease Proportion of domestic sourcea in 2021: 82% Regional funding mechanisms: Southern Africa Malaria Elimination Eight Initiative a Domestic source excludes patient service delivery costs and out-of-pocket expenditure. No data for Botswana in 2021. INTERVENTIONS, 2010–2021 Countries with ≥80% coverage of at-risk population with either LLINs or IRS in 2021: none Countries with ≥50% coverage of high-risk population with either LLINs or IRS in 2021: the Comoros Percentage of suspected cases tested (reported): 81% (2010), 99% (2015), 99.9% (2021) Number of ACT courses distributed: 575 000 (2010), 366 000 (2015), 131 000 (2021) Number of any antimalarial treatment courses (incl. ACT) distributed: 575 000 (2010), 366 000 (2015), 131 000 (2021) REPORTED CASES AND DEATHS,a 2010–2021 Total (presumed and confirmed) cases: 205 300 (2010), 52 900 (2015), 75 400 (2021) Confirmed cases: 82 500 (2010), 47 700 (2015), 74 900 (2021) Percentage of total cases confirmed: 40.2% (2010), 90.2% (2015), 99.2% (2021) Deaths: 242 (2010), 176 (2015), 92 (2021) Children aged under 5 years, presumed and confirmed cases: 56 400 (2010), 7300 (2015), 8600 (2021) Children aged under 5 years, percentage of total cases: 27.5% (2010), 13.7% (2015), 11.4% (2021) Children aged under 5 years, deaths: 37 (2010), 13 (2015), 2 (2021) Children aged under 5 years, percentage of total deaths: 15% (2010), 7% (2015), 2% (2021) a Includes malaria endemic countries only. ESTIMATED CASES AND DEATHS, 2010–2021 Cases: 133 200 (2010), 86 100 (2015), 129 100 (2021); 2010–2021: 3% decrease Deaths: 347 (2010), 280 (2015), 349 (2021); 2010–2021: 1% increase ACCELERATION TO ELIMINATION Countries with nationwide elimination programme: Botswana, the Comoros, Eswatini, Namibia and South Africa Countries part of the E-2025 initiative: Botswana, the Comoros, Eswatini and South Africa THERAPEUTIC EFFICACY STUDIES (CLINICAL AND PARASITOLOGICAL FAILURE AMONG PATIENTS WITH P. FALCIPARUM MALARIA, %) Medicine Study years No. of studies Min. Median Max. Percentile 25 75 AL 2017–2017 4 0.0 0.0 0.0 0.0 0.0 AS-AQ 2016–2019 8 0.0 3.2 4.7 1.1 4.4 AL: artemether-lumefantrine; AS-AQ: artesunate-amodiaquine. STATUS OF INSECTICIDE RESISTANCEa PER INSECTICIDE CLASS (2010–2020) AND USE OF EACH CLASS FOR MALARIA VECTOR CONTROL (2020) Nu m be r o f c ou nt rie s 4 2 6 LLINsb 1 – – – IRSb 3 2 0 3 0 Pyrethroids OrganophosphatesOrganochlorines Carbamates ■ Resistance confirmed ■ Tested but resistance not confirmed ■ Not monitored a Resistance is considered confirmed when it was detected to one insecticide in the class, in at least one malaria vector from one collection site. b Number of countries that reported using the insecticide class for malaria vector control (2020). Confirmed cases per 1000 population 0 0-0.1 0.1-1 1-10 10-50 50-100 100-200 200-300 >300 Not applicable Note: In the Comoros, 2017 data were used. Botswana Namibia Eswatini South Africa Comoros Eritrea US$ 6420 8 ■ Domestic ■ International US $ (m illi on ) 2016 2017 2018 2019 2020 2021201520142013201220112010 60 45 30 15 75 0 ■ Domestic ■ Global Fund ■ World Bank ■ UK ■ Other 160 KEY MESSAGES ■ About 14 million people in the six countries with low transmission in east and southern Africa are at risk of malaria. About 75 400 cases were reported, of which 11.4% were in children aged under 5 years and 99.2% were confirmed. The proportion of total cases that were confirmed has improved substantially over time, from only 40.2% in 2010. The proportion of all malaria deaths that were in children aged under 5 years decreased significantly, from 15% in 2010 to 2% in 2021. ■ In 2021, there were an estimated 129 000 cases and 349 deaths – a decrease of 3% and an increase of 1% compared with 2010. Eritrea accounted for 84% of all estimated cases in the subregion. South Africa met the GTS target of a 40% reduction in incidence by 2021 compared with the GTS baseline of 2015 as a result of strong political support and advocacy within the country, coupled with increased domestic funding. Incidence also decreased in Eswatini but by less than 40%, largely thanks to improvements in case-based surveillance and the use of data to respond to the contextual factors driving transmission. The GTS target was not met in Botswana, the Comoros, Eritrea and Namibia, where incidence increased. Despite a large decrease in the number of estimated cases in Namibia in 2019 (5705) compared with 2018 (50 217), cases significantly increased again to 20 258 in 2020 and to 21 322 in 2021. During the 2020 and 2021 malaria seasons, Namibia experienced malaria outbreaks that were detected late; the major contributor to this situation was low IRS coverage within malaria transmission zones and reported insecticide resistance to dichloro-diphenyl-trichloroethane (DDT) and deltamethrine. Botswana also had a significant increase in estimated cases in 2020 (1776), which was seven times higher than the estimated cases in 2019 (259). In 2021, estimated cases decreased again to 1067. The Comoros, however, had a 74% decrease in estimated cases in 2020 (4546) compared with 2019 (17 599) but cases increased again to 10 537 in 2021. Compared with 2019 and 2020, cases in Eritrea decreased in 2021 by 54%. In South Africa, cases also decreased by 34% in 2021 compared with 2020. In 2021, reporting completeness in Botswana and South Africa was below 90%. In South Africa during the malaria transmission season, disruptions and restrictions to movement due to multiple lockdowns related to the COVID-19 pandemic limited the ability of mobile teams to carry out case investigations at community level, resulting in low investigation and testing rates. Three countries – Botswana, Eswatini and South Africa – that are part of the E-2025 continue to lead the race towards malaria elimination within the subregion. ■ Vector resistance to pyrethroids was confirmed in 55% of sites, to organochlorines in 33%, to carbamates in 7% and to organophosphates in 8%. Five countries have developed insecticide resistance monitoring and management plans. ■ Funding in the subregion has decreased by 42% since 2010; however, from 2015 to 2020 there were substantial gains in funding. There was a large decrease in funding from 2020 to 2021 (40%), largely due to a 43% decrease in domestic funding, in part because Botswana did not report funding in 2021. Funding per person at risk ranged from US$ 1.86 in Eritrea to US$ 6.25 in Botswana over a 3-year average. Eritrea accounts for 84% of all cases yet has the lowest funding per person at risk in the subregion, highlighting the increasing need for domestic and international funding across all countries to achieve targets set out in the GTS. ■ Challenges include reduction of malaria funding at programme level, inadequate coverage of vector control interventions, bottlenecks in procurement and supply management, high population movement between high and low endemic countries (which increases the risk of importation), insufficient human resource and logistical resources to meet the operational requirements of malaria programmes and resurgence during the past 3 years. D. Share of estimated malaria cases, 2021 H. Change in estimated malaria incidence and mortality rates, 2015–2021 2020 milestone: -40% South Africa Eswatini Namibia Eritrea Botswana Comoros f Reduction Increase p 360%300%240%180%120%60%0%-60%-120% 660%600%540%480%420% 720% ■ Incidence ■ Mortality E. Percentage of population with access to either LLINs or IRS, 2021 Source: ITN coverage model from MAP Comorosa Eritreaa South Africab Eswatinib Namibiab Botswanab 40200 1008060 ■ High risk population ■ Total risk population I. Reported indigenous cases in countries with national elimination activities, 2015 versus 2021 IRS: indoor residual spraying; ITN: insecticide-treated mosquito net; LLIN: long-lasting insecticidal net; MAP: Malaria Atlas Project. a ITN coverage estimated by a model from MAP. b IRS coverage is shown using the population to compute incidence and mortality rate as the denominator. Namibia South Africa Comoros Eswatini Botswana 5 000 10 0000 15 000 ■ 2015 ■ 2021 9162 12 979 1884 10 537 2958 4959 703 284 505 318 ■ Eritrea ■ Namibia ■ Comoros ■ South Africa 2% 8% 17% Botswana, 0.8% Eswatini, 0.2%72% F. Estimated number of cases in countries that reduced case incidence by ≥40% in 2021 compared with 2015 201520142013201220112010 2016 2017 2018 2019 2020 2021 25 000 20 000 15 000 10 000 5 000 0 Baseline (2015) ■ South Africa G. Estimated number of cases in countries that reduced case incidence by <40% in 2021 compared with 2015 201520142013201220112010 2016 2017 2018 2019 2020 2021 1 000 800 600 400 200 0 ■ Eswatini ■ Senegal ■ Togo ■ Sierra Leone ■ Guinea ■ Mali ■ Niger ■ Burkina Faso Baseline (2015) H. Estimated number of cases in countries with an increase or no change in case incidence, 2015–2021 201520142013201220112010 2016 2017 2018 2019 2020 2021 250 000 200 000 150 000 100 000 50 000 0 ■ Botswana ■ Namibia ■ Comoros ■ Eritrea Baseline (2015) W O RL D M AL AR IA R EP O RT 2 02 2 161 ANNEx 3 - B. WhO REGION OF ThE AMERICAS EPIDEMIOLOGY Population denominator used to compute incidence and mortality rate: 142 million Parasites: P. vivax (76%), P. falciparum and mixed (24%) and other (<1%) Vectors: An. albimanus, An. albitarsis, An. aquasalis, An. argyritarsis, An. braziliensis, An. cruzii, An. darlingi, An. neivai, An. nuneztovari, An. pseudopunctipennis and An. punctimacula. FUNDING (US$), 2010–2021 234.3 million (2010), 202.7 million (2015), 137.7 million (2021); 2010–2021: 41% decrease Proportion of domestic sourcea in 2021: 74% Regional funding mechanisms: Regional Malaria Elimination Initiative a Domestic source excludes patient service delivery costs and out-of-pocket expenditure. INTERVENTIONS, 2010–2021 Number of people protected by IRS:a 6.99 million (2010), 3.94 million (2015), 2.37 million (2021) Total LLINs distributed:b 978 000 (2010), 1.14 million (2015), 1.11 million (2021) Number of RDTs distributed:c 83 700 (2010), 533 900 (2015), 824 000 (2021) Number of ACT courses distributed:d 148 400 (2010), 209 400 (2015), 134 700 (2021) Number of any first-line antimalarial treatment courses (incl. ACT) distributed:e 1.14 million (2010), 652 500 (2015), 527 100 (2021) a No data for Peru and the Plurinational State of Bolivia in 2021; b Includes piperonyl butoxide (PBO) nets, G2 nets and Royal Guard nets in 2021; c No data for the Dominican Republic, Haiti, Nicaragua, Peru and the Plurinational State of Bolivia in 2021; d No data for the Dominican Republic, French Guiana and Nicaragua in 2021; e No data for French Guiana in 2021. REPORTED CASES AND DEATHS,a 2010–2021 Total (presumed and confirmed) cases:a 677 400 (2010), 479 000 (2015), 524 200 (2021) Confirmed cases: 677 400 (2010), 479 000 (2015), 524 200 (2021) Percentage of total cases confirmed: 100% (2010), 100% (2015), 100% (2021) Indigenous cases: 677 373 (2010), 443 419 (2015), 478 470 (2021) Imported cases: 11 (2010), 14 881 (2015), 3420 (2021) Introduced cases: 23 (2010), 0 (2015), 4575 (2021) Indigenous deaths: 190 (2010),169 (2015), 126 (2021) a Includes malaria endemic countries only. ESTIMATED CASES AND DEATHS,a 2010–2021 Cases: 818 000 (2010), 573 000 (2015), 597 000 (2021); 2010–2021: 27% decrease Deaths: 502 (2010), 390 (2015), 334 (2021); 2010–2021: 33% decrease a For Bolivia (Plurinational State of), data were submitted after the closure of data analysis (total indigenous cases: 11 017 used in burden estimation instead of 9959 in 2021). ACCELERATION TO ELIMINATION Countries and territories with nationwide elimination programme: Belize, Bolivia (Plurinational State of), Brazil, Colombia, Costa Rica, the Dominican Republic, Ecuador, French Guiana, Guatemala, Guyana, Haiti, Honduras, Mexico, Nicaragua, Panama, Peru, Suriname, Venezuela (Bolivarian Republic of) Countries and territories part of the E-2025 initiative: Belize, Costa Rica, the Dominican Republic, Ecuador, French Guiana, Guatemala, Honduras, Mexico, Panama and Suriname Certification in process: Belize Zero indigenous cases for 3 consecutive years (2019–2021): Belize Certified as malaria free since 2010: Argentina (2019), El Salvador (2021) and Paraguay (2018) THERAPEUTIC EFFICACY STUDIES (CLINICAL AND PARASITOLOGICAL FAILURE AMONG PATIENTS WITH P. FALCIPARUM MALARIA, %) Medicine Study years No. of studies Min. Median Max. Percentile 25 75 AL 2015–2019 2 0 0 0 0 0 AL: artemether-lumefantrine. THERAPEUTIC EFFICACY STUDIES (CLINICAL AND PARASITOLOGICAL FAILURE AMONG PATIENTS WITH P. VIVAX MALARIA, %) Medicine Study years No. of studies Min. Median Max. Percentile 25 75 CQ 2019–2020 1 0.0 0.0 0.0 0.0 0.0 CQ+PQ 2016–2020 3 0.0 0.0 1.2 0.0 1.2 CQ: chloroquine; CQ+PQ: chloroquine+primaquine. STATUS OF INSECTICIDE RESISTANCEa PER INSECTICIDE CLASS (2010–2020) AND USE OF EACH CLASS FOR MALARIA VECTOR CONTROL (2020) Nu m be r o f c ou nt rie s 14 LLINsb 13 – – – IRSb 9 0 3 3 0 Pyrethroids OrganophosphatesOrganochlorines Carbamates ■ Resistance confirmed ■ Tested but resistance not confirmed ■ Not monitored 7 21 a Resistance is considered confirmed when it was detected to one insecticide in the class, in at least one malaria vector from one collection site. b Number of countries that reported using the insecticide class for malaria vector control (2020). A. Confirmed malaria cases per 1000 population, 2021 Confirmed cases per 1000 population 0 0-0.1 0.1-1 1-10 10-50 50-100 >100 Insucient data Active foci Global Fund: Global Fund to Fight AIDS, Tuberculosis and Malaria; USAID: United States Agency for International Development. a Excludes patient service delivery costs and out-of-pocket expenditure; no domestic funding data were reported for the Bolivarian Republic of Venezuela, Ecuador and Haiti in 2021. B. Malaria fundinga by source, 2010–2021 C. Malaria fundinga per person at risk, average 2019–2021 Suriname Mexico Guyana Nicaragua Ecuador El Salvador Panama Brazil Costa Rica Peru Belize Haiti Venezuela (Bolivarian Republic of) Colombia Dominican Republic Guatemala Honduras Bolivia (Plurinational State of) French Guiana US$ 6420 168 2420 28 ■ Domestic ■ International ND US $ (m illi on ) 2016 2017 2018 2019 2020 2021201520142013201220112010 250 200 150 100 50 300 0 ■ Domestic ■ Global Fund ■ USAID ■ Other a Data are not collected on out-of-pocket expenditure and excluded patient service delivery costs. Note: In Colombia, the Dominican Republic, Ecuador, French Guiana and Haiti, 2020 data were used. 162 KEY MESSAGES ■ Eighteen countries including one territory in the WHO Region of the Americas are currently at risk of malaria. Paraguay, Argentina and El Salvador were certified malaria free by WHO in 2018, 2019 and 2021, respectively. Belize reported zero indigenous malaria cases for the third consecutive year in 2021. Three quarters of reported malaria cases in the region are caused by P. vivax. In 2021, the region reported 524 154 malaria cases and 126 indigenous deaths – decreases of 23% and 34% compared with 2010, respectively. Three countries accounted for almost 80% of all estimated cases: Venezuela (Bolivarian Republic of) (34%), Brazil (27%) and Colombia (17%). ■ Ten countries and one territory experienced reductions in the number of reported cases between 2015 and 2021 (% reduction shown in brackets): Belize (100%), Brazil (2%), the Dominican Republic (56%), El Salvador (100%), French Guiana (67%), Guatemala (77%), Haiti (46%), Honduras (53%), Mexico (50%), Peru (73%) and Suriname (80%). All other countries in the region experienced varying levels of increases in reported cases; such increases in 2021 are attributed to outbreaks, population dynamics within the countries and gaps in the malaria response. ■ All of the indigenous cases reported by Guatemala, Mexico, Panama and Suriname were due to P. vivax. Additionally, between 48% and 98% of the indigenous cases were due to P. vivax in Bolivia (Plurinational State of), Brazil, Costa Rica, Ecuador, French Guiana, Guyana, Honduras, Nicaragua, Peru and Venezuela (Bolivarian Republic of). Conversely, all of the indigenous cases reported by the Dominican Republic and Haiti and 52% of the indigenous cases reported in Colombia in 2021 were due to P. falciparum. ■ Nine countries including one territory in the region met the GTS target of reduced case incidence by 40% or more by 2020: Belize, the Dominican Republic, El Salvador, French Guiana, Guatemala, Honduras, Mexico, Peru and Suriname. Two countries, Brazil and Haiti, reduced case incidence but by less than 40%. Eight countries – Bolivia (Plurinational State of), Colombia, Costa Rica, Ecuador, Guyana, Nicaragua, Panama and Venezuela (Bolivarian Republic of) – saw increases in incidence in 2021 compared with 2015. All countries except Bolivia (Plurinational State of), Brazil, Guyana, Haiti and Venezuela (Bolivarian Republic of) experienced a 40% or more reduction in the number of estimated deaths. In 2021, 13 countries reported zero malaria deaths. No data on deaths were reported for French Guiana. ■ Nine countries and one territory in this region are part of the E-2025 initiative: Belize, Costa Rica, the Dominican Republic, Ecuador, French Guiana, Guatemala, Honduras, Mexico, Panama and Suriname. An additional seven countries in Central America with Colombia and the Dominican Republic are taking part in a subregional initiative to eliminate malaria by 2025 (RMEI). In 2021, imported cases accounted for 69% of the cases in Suriname (53/77), 26% of the cases in French Guiana (37/143), 12% of the cases in Costa Rica (27/232), 11% of the cases in Mexico (31/275), 6.5% of the cases in Honduras (107/1657) and 3% of the cases in Ecuador (70/2436). In Brazil and Venezuela (Bolivarian Republic of), 14% and 7% of all cases were classified as relapses, respectively. ■ Malaria prevention in most of the countries relies on IRS, or mass or routine distribution of insecticide- treated mosquito nets (ITNs). Bolivia (Plurinational State of) and Peru introduced the distribution of nets treated with piperonyl butoxide (PBO) in 2019 and 2020, respectively. Vector resistance to pyrethroids was confirmed in 32% of the sites, to organochlorines in 5%, to carbamates in 18% and to organophosphates in 18%. There remain significant gaps in standard resistance monitoring for the insecticide classes commonly used for vector control. Fourteen countries have developed their insecticide resistance monitoring and management plans. ■ Total funding has decreased by an average of 41% since 2010; it peaked in 2011 and then gradually declined over the past decade. From 2020 to 2021, total funding decreased by 6%. The majority of funding stemmed from domestic expenditures, which represented 74% of total funding in 2021. ■ Owing to the COVID-19 pandemic, diagnostic services were disrupted, as shown by the 32% decrease in malaria suspected cases tested in 2020 compared with 2019. In six countries – Brazil, Colombia, Costa Rica, Guyana, Mexico and Panama – although the test positivity rate was higher in 2020 than in 2021, the number of people tested had decreased by at least 25% in the same period. In the Dominican Republic and Mexico, available testing data showed that testing declined in March 2020 and beyond, clearly associated with lockdowns related to the COVID-19 pandemic. The reduction in testing and disruption of services may have contributed to reduction in cases reported by the region in 2020; however, restrictions in mobility seem to have had an effect on transmission. The number of suspects tested increased again in 2021. D. Share of estimated malaria cases, 2021 2020 milestone: -40% Belize French Guiana Guatemala Peru Suriname Honduras Mexico Dominican Republic Haiti Brazil Colombia Guyana Venezuela (Bolivarian Republic of) Bolivia (Plurinational State of)a Ecuadorb Panamab Nicaraguab Costa Ricac f Reduction Increase p 50%0%-50%-100% 100% ■ Incidence ■ Mortality . Dominican Republic Haiti Colombia Nicaragua Honduras Guyana Ecuador Peru Venezuela (Bolivarian Republic of) Brazil Costa Rica Bolivia (Plurinational State of) French Guiana Panama Suriname Guatemala Mexico Belize 100500 100500 P. falciparum and mixed P. vivax Other 2010 2021 Zero indigenous cases 201520142013201220112010 2016 2017 2018 2019 2020 2021 10 000 000 8 000 000 6 000 000 4 000 000 2 000 000 0 ■ Mexico F. Estimated number of cases in countries and areas that reduced case incidence by ≥40% in 2021 compared with 2015 H. Estimated number of cases in countries with an increase or no change in case incidence, 2015–2021 Guatemala Honduras Dominican Republic Ecuador Panama Mexico French Guiana Suriname Belize Costa Rica 3000200010000 50004000 6000 5538 1273 3555 1550 631 284 618 546 2175 4354 517 242 81 22 374 74 9 0 0 189 ■ 2015 ■ 2021 I. Change in estimated malaria incidence and mortality rates, 2015–2021 K. Number of reported indigenous cases in countries with national elimination activities, 2015 versus 2021 J. Total number of suspected malaria cases tested, 2010–2021 ■ Venezuela (Bolivarian Republic of) ■ Brazil ■ Colombia ■ Nicaragua ■ Guyana ■ Peru ■ Haiti ■ Bolivia (Plurinational State of) 4% 5% 4% 3% 4% 17% 27% Honduras, 0.4% Panama, 0.8% Ecuador, 0.4% Guatemala, 0.3% Dominican Republic, <0.1% Mexico, <0.1% Costa Rica, <0.1% French Guiana, <0.1% Suriname, <0.1% 34% 201520142013201220112010 2016 2017 2018 2019 2020 2021 125 000 100 000 75 000 50 000 25 000 0 ■ Belize ■ Mexico ■ Suriname ■ French Guiana ■ Dominican Republic ■ Guatemala ■ Honduras ■ Peru Baseline (2015) G. Estimated number of cases in countries that reduced case incidence by <40% in 2021 compared with 2015 201520142013201220112010 2016 2017 2018 2019 2020 2021 500 000 400 000 300 000 200 000 100 000 0 ■ Haiti ■ Brazil Baseline (2015) 201520142013201220112010 2016 2017 2018 2019 2020 2021 750 000 600 000 450 000 300 000 150 000 0 Baseline (2015) ■ Costa Rica ■ Panama ■ Nicaragua ■ Ecuador ■ Bolivia (Plurinational State of) ■ Guyana ■ Venezuela (Bolivarian Republic of) ■ Colombia a Data were submitted after the closure of data analysis (total indigenous cases: 11 017 used in burden estimation instead of 9959 in 2021); b In these countries, change in case incidence is more than 100%; c This country achieved the 40% reduction in mortality rate in 2015; since then, there has been no change. E. Percentage of Plasmodium species from indigenous cases, 2010 and 2021 Note: El Salvador was certified as malaria free in 2021; Belize has reported zero indigenous cases since 2019. Note: for Bolivia (Plurinational State of), data were submitted after the closure of data analysis (total indigenous cases: 11 017 used in burden estimation instead of 9959 in 2021). Note: for Bolivia (Plurinational State of), data were submitted after the closure of data analysis (total indigenous cases: 11 017 used in burden estimation instead of 9959 in 2021). W O RL D M AL AR IA R EP O RT 2 02 2 163 ANNEx 3 - C. WhO EASTERN MEDITERRANEAN REGION B. Malaria fundinga by source, 2010–2021 Global Fund: Global Fund to Fight AIDS, Tuberculosis and Malaria; UK: United Kingdom of Great Britain and Northern Ireland; USAID: United States Agency for International Development. a Excludes patient service delivery costs and out-of-pocket expenditure; no domestic funding data were reported for Afghanistan and Yemen in 2021. Saudi Arabia Djibouti Iran (Islamic Republic of) Sudan Yemen Somalia Afghanistan Pakistan US$ 30 96 12 ■ Domestic ■ International C. Malaria fundinga per person at risk, average 2019–2021 a Data are not collected on out-of-pocket expenditure and excluded patient service delivery costs. EPIDEMIOLOGY Population denominator used to compute incidence and mortality rate: 534 million Parasites: P. falciparum and mixed (74%), P. vivax (26%) and other (<1%) Vectors: An. annularis, An. arabiensis, An. culicifacies s.l., An. d’thali, An. fluviatilis s.l., An. funestus s.l., An. gambiae s.s., An. hyrcanus, An. maculipennis s.l., An. pulcherrimus, An. rhodesiensis, An. sacharovi, An. sergentii, An. stephensi and An. superpictus s.l. FUNDING (US$), 2010–2021 137.1 million (2010), 168.8 million (2015), 166.5 million (2021); 2010–2021: 21% increase Proportion of domestic sourcea,b in 2021: 25% Regional funding mechanism: Middle East Response (MER) Initiative is supporting malaria intervention in Yemen by Global Fund, in partnership with International Organization for Migration (IOM) as principle recipient and WHO for technical support. a Domestic source excludes patient service delivery costs and out-of-pocket expenditure. b No domestic funding data reported for Afghanistan and Yemen in 2021. INTERVENTIONS, 2010–2021 Number of people protected by IRS:a 6.1 million (2010), 5.1 million (2015), 6.0 million (2021) Total LLINs distributed:b 2.9 million (2010), 5.9 million (2015), 3.0 million (2021) Number of RDTs distributed:c 2.0 million (2010), 6.1 million (2015), 7.7 million (2021) Number of ACT courses distributed:d 2.6 million (2010), 3.2 million (2015), 7.8 million (2021) Number of any first-line antimalarial treatment courses (incl. ACT) distributed:d 2.6 million (2010), 4.0 million (2015), 8.1 million (2021) a No IRS was implemented in Afghanistan; no data reported for Pakistan in 2010; Djibouti reported IRS data in 2021 only; b Includes PBO nets, G2 nets and Royal Guard nets in 2021; c No data reported for the Islamic Republic of Iran, Pakistan and Saudi Arabia in 2010; no data reported for Yemen in 2021; Djibouti reported RDT distributed in 2021 only; d No data reported for Afghanistan, Djibouti and Pakistan in 2020; no data reported for Yemen in 2021. REPORTED CASES AND DEATHS,a 2010–2021 Total (presumed and confirmed) cases: 6.4 million (2010), 5.4 million (2015), 4.8 million (2021) Confirmed cases: 1.2 million (2010), 1.0 million (2015), 2.4 million (2021) Percentage of total cases confirmed: 18.3% (2010), 18.8% (2015), 49.7% (2021) Deaths: 1145 (2010), 1016 (2015), 1725 (2021) a Includes malaria endemic countries only. ESTIMATED CASES AND DEATHS, 2010–2021a Cases: 4.5 million (2010), 4.3 million (2015), 6.2 million (2021); 2010–2021: 38% increase Deaths: 8630 (2010), 8210 (2015), 13 410 (2021); 2010–2021: 55% increase a Estimates for global trend analysis. Country-level subnational analysis is ongoing for decision-making for strategy development for countries and the region. ACCELERATION TO ELIMINATION Countries with nationwide elimination programme: Islamic Republic of Iran and Saudi Arabia Zero indigenous cases for 4 consecutive years (2018–2021): Islamic Republic of Iran Zero indigenous cases in 2021: Saudi Arabia Countries part of the E-2025 initiative: Islamic Republic of Iran and Saudi Arabia Certification in process: Islamic Republic of Iran (request submitted to WHO) Certified as malaria free since 2010: Morocco (2010) THERAPEUTIC EFFICACY STUDIES (CLINICAL AND PARASITOLOGICAL FAILURE AMONG PATIENTS WITH P. FALCIPARUM MALARIA, %) Medicine Study years No. of studies Min. Median Max. Percentile 25 75 AL 2015–2020 18 0.0 0.0 7.9 0.0 2.4 AS+SP 2015–2017 7 0.0 0.0 16.4 0.0 12.3 DHA-PPQ 2015–2020 11 0.0 0.0 2.5 0.0 1.0 AL: artemether-lumefantrine; AS+SP: artesunate+sulfadoxine-pyrimethamine; DHA-PPQ: dihydroartemisinin- piperaquine. STATUS OF INSECTICIDE RESISTANCEa PER INSECTICIDE CLASS (2010–2020) AND USE OF EACH CLASS FOR MALARIA VECTOR CONTROL (2020) Nu m be r o f c ou nt rie s 4 2 6 8 LLINsb 5 – – – IRSb 4 0 1 0 0 Pyrethroids OrganophosphatesOrganochlorines Carbamates ■ Resistance confirmed ■ Tested but resistance not confirmed ■ Not monitored a Resistance is considered confirmed when it was detected to one insecticide in the class, in at least one malaria vector from one collection site. b Number of countries that reported using the insecticide class for malaria vector control (2020). A. Confirmed malaria cases per 1000 population, 2021 Confirmed cases per 1000 population 0 0-0.1 0.1-1 1-10 10-50 50-100 >100 Insucient data Not applicable US $ (m illi on ) 2016 2017 2018 2019 2020 2021201520142013201220112010 200 150 100 50 250 0 ■ Domestic ■ Global Fund ■ World Bank ■ USAID ■ UK ■ Other D. Share of estimated malaria cases, 2021 ■ Sudan ■ Somalia ■ Yemen ■ Pakistan ■ Afghanistan ■ Djibouti 3% 1% 8% 16% 18% 54% Note: Islamic Republic of Iran has had zero indigenous cases since 2018 and Saudi Arabia had zero indigenous cases in 2021. Note: Subnational estimates for Djibouti, Somalia and the Sudan are ongoing with national malaria control programmes. 164 KEY MESSAGES ■ Fourteen countries in the WHO Eastern Mediterranean Region are free of indigenous malaria and are at the stage of prevention of re-establishment of local transmission. There are six high malaria burden countries in the region, and P. falciparum is responsible for 74% of all detected infections. The Islamic Republic of Iran reported zero indigenous cases for the fourth consecutive year in 2021 and Saudi Arabia reported zero indigenous cases for the first time in 2021. These countries undertake continued vigilance for malaria in the general health services, and provide diagnosis and treatment free of charge to all imported cases. ■ Estimated malaria incidence in the region declined between 2010 and 2015 but increased significantly in 2016 and then remained stable for the next 5 years, translating into a 38% increase between 2010 and 2021. The number of estimated malaria deaths shows a similar trend – in this case, an increase of 55% between 2010 and 2021. ■ The Sudan accounted for more than half of the cases estimated for the region. In 2021, the region reported that only about 2.4 million of the 4.8 million cases reported were confirmed (49.7%), although this still represented a significant increase from the 18% confirmation rate reported in 2010 and in 2015. Part of this increase was due to incomplete reporting of presumed cases in Pakistan in previous years. The reported number of deaths increased from 8634 in 2010 to 13 413 in 2021, mainly due to an increase in the Sudan. ■ Based on the estimates from reported figures from Afghanistan and Pakistan, these countries reduced incidence by more than 40% compared with 2015. Djibouti, Somalia, the Sudan and Yemen were off track, with malaria case incidence higher by 40% or more. These estimations need to be verified with ongoing country-level burden estimations, given the various factors that affected the data reported from both countries in recent years. ■ From the latest data available, vector resistance to pyrethroids, organochlorines and organophosphates was confirmed in all countries (except for Saudi Arabia) in 76%, 65% and 44% of the sites tested, respectively. Resistance to carbamates was confirmed in all countries (except for Saudi Arabia, Somalia and Yemen) in 25% of the sites tested. All seven endemic countries developed their insecticide resistance monitoring and management plans. ■ Reported funding increased in the WHO Eastern Mediterranean Region by 21% from 2010 through 2021; it has remained relatively stable since 2015. In 2021, reported domestic funding represented 25% of total funding. ■ Challenges include low coverage of essential interventions in most malaria endemic countries, inadequate funding and dependence on external resources, humanitarian emergencies, difficult operational environments and population displacements, a shortage of skilled technical staff (particularly at subnational level), and weak surveillance and health information systems. Frequent floods – particularly in Pakistan, the Sudan and Yemen – and the increasing spread of An. stephensi in Djibouti, Somalia, the Sudan and Yemen have increased the risk of malaria, particularly in urban and suburban areas. The confirmed presence of HRP2/3 gene deletions in Djibouti and Somalia, and the high probability of the presence of this mutation in the Sudan and possibly in Yemen, is another threat for the region. The confirmed insecticide resistance in many countries to multiple classes of insecticides is another challenge that has increased the cost of vector control interventions. These challenges may have led to an overall increase in cases during the period 2015–2021 in some countries in the region. There is some evidence to suggest that the COVID-19 pandemic had an impact on diagnostic services in endemic countries in 2020 and 2021 compared with 2019. The poor data quality and reporting issues in some countries in the region make it difficult to accurately quantify the impact on diagnostic and treatment services of the COVID-19 pandemic, and of additional political and security challenges that occurred during the same period. E. Percentage of Plasmodium species from indigenous cases, 2010 and 2021 Yemen Sudan Somalia Djibouti Pakistan Afghanistan Saudi Arabia Iran (Islamic Republic of) 100500 100500 P. falciparum and mixed P. vivax Other 2010 2021 Zero indigenous cases Zero indigenous cases Iran (Islamic Republic of) DjiboutiAfghanistan Pakistan Saudi Arabia Somalia Sudan Yemen 9 000 000 7 500 000 6 000 000 4 500 000 3 000 000 1 500 000 0 ■ 2019 ■ 2020 ■ 2021 Iran (Islamic Republic of) Saudi Arabia 150100500 200 167 0 83 ■ 2015 ■ 2021 49 356 0 H. Total number of suspected malaria cases tested, 2019–2021 I. Reported indigenous cases in countries with national elimination activities, 2015 versus 2021 F. Estimated number of cases in countries that reduced case incidence by ≥40% in 2021 compared with 2015 201520142013201220112010 2016 2017 2018 2019 2020 2021 2 500 000 2 000 000 1 500 000 1 000 000 500 000 0 ■ Afghanistan ■ Pakistan Baseline (2015) G. Estimated number of cases in countries with an increase or no change in case incidence, 2015–2021 201520142013201220112010 2016 2017 2018 2019 2020 2021 6 000 000 4 800 000 3 600 000 2 400 000 1 200 000 0 Baseline (2015) ■ Djibouti ■ Somalia ■ Sudan ■ Yemen Note: These estimates need to be verified with ongoing country-level burden estimations. Note: These estimates need to be verified with ongoing country-level burden estimations. W O RL D M AL AR IA R EP O RT 2 02 2 165 ANNEx 3 - D. WhO SOUTh-EAST ASIA REGION B. Malaria fundinga by source, 2010–2021 US $ (m illi on ) 2016 2017 2018 2019 2020 2021201520142013201220112010 200 150 100 50 300 250 0 ■ Domestic ■ Global Fund ■ World Bank ■ USAID ■ UK ■ Other Global Fund: Global Fund to Fight AIDS, Tuberculosis and Malaria; UK: United Kingdom of Great Britain and Northern Ireland; USAID: United States Agency for International Development. a Excludes patient service delivery costs and out-of-pocket expenditure. A. Confirmed malaria cases per 1000 population, 2021 Timor-Leste Bhutan Myanmar Thailand Bangladesh Nepal Democratic People’s Republic of Korea Indonesia India US$ 10 2 3 4 5 ■ Domestic ■ International C. Malaria fundinga per person at risk, average 2019–2021 a Data are not collected on out-of-pocket expenditure and excluded patient service delivery costs; total funding includes negative disbursements due to under expenditure of international funding. EPIDEMIOLOGY Population denominator used to compute incidence and mortality rate: 1.68 billion Parasites: P. falciparum and mixed (55%), P. vivax (44%) and other (1%) Vectors: An. albimanus, An. annularis, An. balabacensis, An. barbirostris, An. culicifacies s.l., An. dirus s.l., An. farauti s.l., An. fluviatilis, An. leteri, An. maculatus s.l., An. minimus s.l., An. peditaeniatus, An. philippinensis, An. pseudowillmori, An. punctulatus s.l., An. sinensis s.l., An. stephensi s.l., An. subpictus s.l., An. sundaicus s.l., An. tessellatus, An. vagus, An. varuna and An. yatsushiroensis. FUNDING (US$), 2010–2021 264.5 million (2010), 212.8 million (2015), 169.1 million (2021); 2010–2021: 36% decrease Proportion of domestic sourcea in 2021: 65% Regional funding mechanisms: Mekong Malaria Elimination (MME) initiative in the Greater Mekong subregion: Myanmar and Thailand a Domestic source excludes patient service delivery costs and out-of-pocket expenditure. INTERVENTIONS, 2010–2021 Number of people protected by IRS:a 57.0 million (2010), 44.0 million (2015), 16.7 million (2021) Total LLINs distributed:b 8.1 million (2010), 14.5 million (2015), 17.7 million (2021) Number of RDTs distributed:c 11.4 million (2010), 23.5 million (2015), 4.3 million (2021) Number of ACT courses distributed:d 3.5 million (2010), 2.8 million (2015), 4.0 million (2021) Number of any first-line antimalarial treatment courses (incl. ACT) distributed: 2.9 million (2010), 2.9 million (2015), 4.3 million (2021) a Data for Bangladesh, the Democratic People's Republic of Korea and Myanmar were not available for 2021; b Includes PBO nets, G2 nets and Royal Guards nets in 2021; data for the Democratic People's Republic of Korea were not available for 2021; c Data for the Democratic People's Republic of Korea, India and Myanmar were not available for 2021; d Data for the Democratic People's Republic of Korea were not available for 2021. REPORTED CASES AND DEATHS,a 2010–2021 Total (presumed and confirmed) cases: 3.1 million (2010), 1.7 million (2015), 559 000 (2021) Confirmed cases: 2.6 million (2010), 1.6 million (2015), 559 000 (2021) Percentage of total cases confirmed: 85.6% (2010), 98.9% (2015), 99.9% (2021) Indigenous cases: 2 640 900 (2010), 1 630 325 (2015), 557 444 (2021) Imported cases: 0 (2010), 10 610 (2015), 1206 (2021) Deaths: 2421 (2010), 620 (2015), 159 (2021) a Includes malaria endemic countries only. ESTIMATED CASES AND DEATHS, 2010–2021 Cases: 24.0 million (2010), 13.2 million (2015), 5.4 million (2021); 2010–2021: 78% decrease Deaths: 38 000 (2010), 24 000 (2015), 9100 (2021); 2010–2021: 76% decrease ACCELERATION TO ELIMINATION Countries with subnational/territorial elimination programme: Bangladesh, India, Indonesia and Myanmar Countries with nationwide elimination programme: Bhutan, the Democratic People’s Republic of Korea, Nepal, Thailand and Timor-Leste Zero indigenous cases in 2021: Timor-Leste Countries part of the E-2025 initiative: Bhutan, the Democratic People's Republic of Korea, Nepal, Thailand and Timor-Leste Certified as malaria free since 2010: Maldives (2015) and Sri Lanka (2016) THERAPEUTIC EFFICACY STUDIES (CLINICAL AND PARASITOLOGICAL FAILURE AMONG PATIENTS WITH P. FALCIPARUM MALARIA, %) Medicine Study years No. of studies Min. Median Max. Percentile 25 75 AL 2015–2020 36 0.0 0.0 3.8 0.0 1.9 AS+SP 2015–2017 14 0.0 0.0 5.6 0.0 1.5 DHA-PPQ 2015–2020 12 0.0 0.0 3.9 0.0 2.0 AL: artemether-lumefantrine; AS+SP: artesunate+sulfadoxine-pyrimethamine; DHA-PPQ: dihydroartemisinin- piperaquine. STATUS OF INSECTICIDE RESISTANCEa PER INSECTICIDE CLASS (2010–2020) AND USE OF EACH CLASS FOR MALARIA VECTOR CONTROL (2020) Nu m be r o f c ou nt rie s 3 6 9 LLINsb 8 – – – IRSb 7 1 2 3 0 ■ Resistance confirmed ■ Tested but resistance not confirmed ■ Not monitored Pyrethroids OrganophosphatesOrganochlorines Carbamates a Resistance is considered confirmed when it was detected to one insecticide in the class, in at least one malaria vector from one collection site. b Number of countries that reported using the insecticide class for malaria vector control (2020). Confirmed cases per 1000 population Note: In Bhutan and the Democratic People's Republic of Korea, 2020 data were used. 0 0-0.1 0.1-1 1-10 10-50 50-100 >100 Insu­cient data Not applicable 166 Da. Share of estimated malaria cases, 2021 KEY MESSAGES ■ Malaria is endemic in nine of the WHO South-East Asia Region’s 11 countries, accounting for 42% of the estimated burden of malaria outside the African Region. In 2021, the region had 5.3 million estimated cases and 9100 estimated deaths – reductions of 78% and 76%, respectively, compared with 2010; these reductions represented the largest decline among all WHO regions. All countries met the GTS 2021 target of a reduction in case incidence of at least 40% compared with 2015, except for Indonesia, where the case incidence reduced by 26%. All countries also met the GTS 2021 target for a reduction in mortality rate of at least 40% compared with 2015, except for Indonesia, where the mortality rate reduced by 19%. Four countries – Bhutan, the Democratic People’s Republic of Korea, Thailand and Timor-Leste – reported zero indigenous deaths. The Maldives and Sri Lanka were certified malaria free in 2015 and 2016, respectively, and continue to maintain their malaria free status. ■ Three countries accounted for 99.7% of the estimated cases in the region, with India being the largest contributor (79%), followed by Indonesia (15%) and Myanmar (5%). Compared with 2020, Myanmar had a significant increase in estimated cases (210 000). In 2021, there were 558 964 cases reported, of which 99.9% were confirmed. Indonesia accounted for the highest proportion of reported cases in the region (55%), followed by India (29%). The gap between reported cases and estimated cases in India in 2021 is due to adjustments made for care seeking and diagnostic testing rates, which were affected in some states as a result of disruption of services due to the COVID-19 pandemic. More than 40% of reported cases in the region are due to P. vivax. In 2021, P. vivax was the dominant species (>50% of local cases) in Bhutan, the Democratic People’s Republic of Korea (100%), Myanmar, Nepal and Thailand, whereas P. falciparum was the dominant species in Bangladesh, India and Indonesia. In Myanmar, the proportion of local cases due to P. falciparum decreased from 93% in 2010 to 19% in 2021. ■ Continuing the declining trend, reported malaria deaths in the region fell to 159 in 2021 – a 93% reduction compared with 2010. India, Indonesia and Myanmar accounted for 57%, 30% and 7% of the total reported deaths in the region, respectively. ■ Three countries – Bhutan, Nepal and Timor-Leste – were identified as having the potential to eliminate malaria by 2020. Among these countries, Timor-Leste almost completed 3 consecutive years of malaria free status, reporting zero indigenous cases in 2018 and 2019, but experiencing a small malaria outbreak in 2020. In 2021, Timor-Leste again reported zero indigenous cases. Bhutan reported six indigenous cases in 2018, followed by just two in 2019, but had an increase in cases to 22 in 2020. In 2021, Bhutan reported nine indigenous cases. Nepal reported 73 indigenous cases in 2020 and 32 indigenous cases in 2021. About half of the reported cases in Nepal were classified as imported. There were no imported cases in the Democratic People’s Republic of Korea and Timor-Leste in 2021, owing to border restrictions as a result of the COVID-19 pandemic. Sri Lanka reported 26 cases in 2021, of which 25 were imported and one was induced. ■ Vector resistance to pyrethroids was confirmed in 49% of the sites, to organochlorines in 80%, to carbamates in 49% and to organophosphates in 62%. Seven countries have developed insecticide resistance monitoring and management plans. ■ Overall funding in the region has decreased by 36% since 2010. Between 2020 and 2021, overall funding decreased by 25%, largely as a result of decreased funding by the Global Fund (52%). In 2021, domestic expenditures made up 65% of total funding. Malaria funding per person at risk ranged from US$ 0.07 in India to US$ 4.24 in Malaysia over a 3-year average, raising concerns because India is the largest contributor to the estimated cases in the region but also receives the lowest amount of funding per person at risk. ■ The COVID-19 pandemic affected all countries in the region in 2020. Owing to the pandemic, diagnostic services were disrupted, shown by the 27% decrease in malaria suspects tested in 2020 compared with 2019. The number of suspects tested increased again in 2020. Similarly, there was evidence of disruption in malaria preventive services of varying degrees across the region. Promisingly, several countries creatively integrated their malaria services with those of the COVID-19 response activities (e.g. screening for both malaria and COVID-19 using an integrated surveillance approach), to the benefit of both programmes. ■ Challenges include decreased funding, the continuing threat of multiple artemisinin-based combination therapy (ACT) failures in the countries of the Greater Mekong subregion (GMS) and vector resistance to pyrethroids. In recent years, Myanmar has had some setbacks with the increase in the number of both P. falciparum and P. vivax cases owing to continued political instability in the area. Bangladesh India Indonesia Bhutan Myanmar Nepal Thailand Democratic People’s Republic of Korea Timor-Leste 100500 100500 P. falciparum and mixed P. vivax Other 2010 2021 Zero indigenous cases 2020 milestone: -40% Timor-Leste Nepal Thailand Bangladesh Bhutan Democratic People’s Republic of Korea India Indonesia Myanmar f Reduction Increase p 50%0%-50%-100% 100% ■ Incidence ■ Mortality F. Estimated number of cases in countries that reduced case incidence by ≥40% in 2021 compared with 2015 201520142013201220112010 2016 2017 2018 2019 2020 2021 25 000 000 20 000 000 15 000 000 10 000 000 5 000 000 0 ■ India Baseline (2015) Thailand Democratic People’s Republic of Korea Nepal Timor-Leste Bhutan 9 0006 0003 0000 12 000 15 000 ■ 2015 ■ 2021 14 265 7022 2357 591 32 80 0 34 9 2426 I. Change in estimated malaria incidence and mortality rates, 2015–2021 K. Reported indigenous cases in countries with national elimination activities, 2015 versus 2021 J. Total number of suspected malaria cases tested, 2010–2021 E. Percentage of Plasmodium species from indigenous cases, 2010 and 2021 ■ India ■ Indonesia ■ Myanmar 5% 15% Bangladesh, 0.2% Democratic People’s Republic of Korea, <0.1% Thailand, <0.1% Nepal, <0.1% Bhutan, <0.1% Note: Timor-Leste had zero indigenous cases in 2021. 79% H. Estimated number of cases in countries with no change in case incidence, 2015–2021 201520142013201220112010 2016 2017 2018 2019 2020 2021 250 000 200 000 150 000 100 000 50 000 0 ■ Bhutan ■ Democratic People’s Republic of Korea ■ Nepal ■ Thailand ■ Bangladesh ■ Timor-Leste Baseline (2015) 201520142013201220112010 2016 2017 2018 2019 2020 2021 200 000 000 160 000 000 120 000 000 80 000 000 40 000 000 0 ■ Mexico Db. Share of reported confirmed cases, 2021 ■ Indonesia ■ India ■ Myanmar 14% 29% Bangladesh, 1.3% Thailand, 0.6% Democratic People’s Republic of Korea, 0.4% Nepal, <0.1%; Sri Lanka, <0.1%; Bhutan, <0.1% 55% Note: Timor-Leste had zero indigenous cases in 2021. 201520142013201220112010 2016 2017 2018 2019 2020 2021 2 000 000 1 600 000 1 200 000 800 000 400 000 0 ■ Indonesia Baseline (2015) 201520142013201220112010 2016 2017 2018 2019 2020 2021 2 000 000 1 600 000 1 200 000 800 000 400 000 0 ■ Myanmar Baseline (2015) G. Estimated number of cases in countries that reduced case incidence by <40% in 2021 compared with 2015 W O RL D M AL AR IA R EP O RT 2 02 2 167 ANNEx 3 - E. WhO WESTERN PACIFIC REGION Global Fund: Global Fund to Fight AIDS, Tuberculosis and Malaria; UK: United Kingdom of Great Britain and Northern Ireland; USAID: United States Agency for International Development. a Excludes patient service delivery costs and out-of-pocket expenditure. EPIDEMIOLOGY Population denominator used to compute incidence and mortality rate: 766 million Parasites: P. falciparum and mixed (68%), P. vivax (32%) and other (<1%) Vectors: An. anthropophagus, An. balabacensis, An. barbirostris s.l., An. dirus s.l., An. donaldi, An. epirotivulus, An. farauti s.l., An. flavirostris, An. jeyporiensis, An. koliensis, An. litoralis, An. maculatus s.l., An. mangyanus, An. minimus s.l., An. punctulatus s.l., An. sinensis s.l. and An. sundaicus s.l. FUNDING (US$), 2010–2021 223.0 million (2010), 154.2 million (2015), 89.5 million (2021); 2010–2021: 60% decrease Proportion of domestic sourcea in 2021: 65% Regional funding mechanisms: Mekong Malaria Elimination (MME) initiative in the Greater Mekong subregion: Cambodia, China (Yunnan), the Lao People’s Democratic Republic and Viet Nam (supported by RAI3e Global Fund) a Domestic source excludes patient service delivery costs and out-of-pocket expenditure. INTERVENTIONS, 2010–2021 Number of people protected by IRS: 2.8 million (2010), 1.6 million (2015), 765 000 (2021) Total LLINs distributed:a 4.4 million (2010), 4.3 million (2015), 4.2 million (2021) Number of RDTs distributed:b 1.6 million (2010), 2.5 million (2015), 5.7 million (2021) Number of ACT courses distributed:c 591 000 (2010), 1.3 million (2015), 2.7 million (2021) Number of any antimalarial treatment courses (incl. ACT) distributed: 963 000 (2010), 1.3 million (2015), 1.9 million (2021) a Includes PBO nets, G2 nets and Royal Guard nets in 2021; b Data were not available for the Solomon Islands in 2021; c Data were not available for the Republic of Korea in 2021. REPORTED CASES AND DEATHS,a 2010–2021 Total (presumed and confirmed) cases: 1.8 million (2010), 810 000 (2015), 903 000 (2021) Confirmed cases: 308 000 (2010), 499 000 (2015), 753 000 (2021) Percentage of total cases confirmed: 17.3% (2010), 61.6% (2015), 83.5% (2021) Indigenous cases: 305 898 (2010), 496 712 (2015), 749 606 (2021) Imported cases: 887 (2010), 599 (2015), 294 (2021) Introduced cases: 108 (2010), 0 (2015), 3 (2021) Deaths:b 912 (2010), 215 (2015), 227 (2021) Indigenous deaths: 891 (2010), 215 (2015), 211 (2021) a Includes malaria endemic countries only; b Includes P. knowlesi deaths. ESTIMATED CASES AND DEATHS, 2010–2021 Cases: 1.7 million (2010), 1.2 million (2015), 1.4 million (2021); 2010–2021: 15% decrease Deaths: 3488 (2010), 2454 (2015), 2649 (2021); 2010–2021: 24% decrease ACCELERATION TO ELIMINATION Countries with subnational/territorial elimination programme: the Philippines Countries with nationwide elimination programme: Cambodia, the Lao People’s Democratic Republic, Malaysia, the Republic of Korea, Vanuatu and Viet Nam Zero indigenous cases for 3 consecutive years (2019, 2020 and 2021): Malaysia Countries part of the E-2025 initiative: Malaysia, the Republic of Korea and Vanuatu Certified as malaria free since 2010: China (2021) THERAPEUTIC EFFICACY STUDIES (CLINICAL AND PARASITOLOGICAL FAILURE AMONG PATIENTS WITH P. FALCIPARUM MALARIA, %) Medicine Study years No. of studies Min. Median Max. Percentile 25 75 AL 2015–2020 12 0.0 0.0 17.2 0.0 0.7 AS-MQ 2015–2020 19 0.0 0.0 1.9 0.0 0.0 AS-PY 2017–2019 8 0.0 1.7 5.1 0.0 2.5 DHA-PPQ 2015–2019 22 0.0 11.8 68.1 0.0 32.4 AL: artemether-lumefantrine; AS-MQ: artesunate-mefloquine; AS-PY: artesunate-pyronaridine; DHA-PPQ: dihydroartemisinin-piperaquine. STATUS OF INSECTICIDE RESISTANCEa PER INSECTICIDE CLASS (2010–2020) AND USE OF EACH CLASS FOR MALARIA VECTOR CONTROL (2020) Nu m be r o f c ou nt rie s 8 6 4 2 10 LLINsb 7 – – – IRSb 5 0 1 0 0 Pyrethroids OrganophosphatesOrganochlorines Carbamates ■ Resistance confirmed ■ Tested but resistance not confirmed ■ Not monitored a Resistance is considered confirmed when it was detected to one insecticide in the class, in at least one malaria vector from one collection site. b Number of countries that reported using the insecticide class for malaria vector control (2020). A. Confirmed malaria cases per 1000 population, 2021 Confirmed cases per 1000 population 0 0-0.1 0.1-1 1-10 10-50 50-100 >100 Insucient data Not applicable Notes: In the Philippines, the Republic of Korea and Vanuatu, 2020 data were used. B. Malaria fundinga by source, 2010–2021 C. Malaria fundinga per person at risk, average 2019–2021 Malaysia Solomon Islands Cambodia Lao People’s Democratic Republic Papua New Guinea Vanuatu Republic of Korea Viet Nam Philippines US$ 421 30 30 40 50 ■ Domestic ■ International US $ (m illi on ) 2016 2017 2018 2019 2020 2021201520142013201220112010 200 150 100 50 250 0 ■ Domestic ■ Global Fund ■ World Bank ■ USAID ■ UK ■ Other a Data are not collected on out-of-pocket expenditure and excluded patient service delivery costs 168 KEY MESSAGES ■ Nine countries in the WHO Western Pacific Region are at risk of malaria, which is predominantly caused by P. falciparum (68%), with P. vivax accounting for just under a third of all reported cases (32%). In 2021, the region had more than 1.4 million estimated malaria cases and 2649 estimated deaths – a 15% and 24% reduction from 2010, respectively. Most cases occurred in Papua New Guinea (87%) which, together with Solomon Islands (11%), comprised 98% of the estimated cases in the region. About 900 000 cases were reported in the public and private sectors and in the community, of which almost 84% were confirmed. This was a significant improvement over 2015, when only 61.6% of cases were confirmed. There were 227 malaria deaths reported in the region in 2021 (with 211 being indigenous); most were in Papua New Guinea. ■ Seven of the 10 malaria endemic countries in the region in 2015 achieved the GTS 2021 target of a 40% or more reduction in case incidence or zero malaria cases compared with 2015: Cambodia, China, the Lao People’s Democratic Republic, Malaysia, the Philippines, the Republic of Korea and Viet Nam. China was certified as malaria free in early 2021 and Malaysia reported zero indigenous cases for the fourth consecutive year in 2021. Countries that have experienced an increase in estimated cases since 2015 are Papua New Guinea and Solomon Islands. There was no change in estimated case incidence in Vanuatu when compared with 2015, when the country was affected by a major cyclone that severely disrupted malaria diagnostic services and care seeking. As a result, malaria cases in 2015 are likely to be underestimated, which confounds assessment of progress towards the GTS targets relative to a 2015 baseline for Vanuatu. In 2016, there was a large increase in reported malaria cases as a result of increased transmission but since then case incidence has decreased significantly (by 87%). All countries reduced the malaria mortality rate by at least 40% by 2021, except Papua New Guinea and Solomon Islands. ■ Malaysia has reported zero indigenous non-zoonotic malaria cases since 2018. However, Malaysia is facing increasing cases of zoonotic malaria due to P. knowlesi, which increased from 1600 cases to over 4000 between 2016 and 2018. Although there have been no indigenous human malaria cases or deaths in Malaysia for the past 4 years, since 2017 a total of 17 125 P. knowlesi cases and 48 deaths have been reported; in 2021 alone, 3575 cases were reported and these cases resulted in 13 deaths. The Republic of Korea continues to face the challenge of malaria transmission among military personnel along the northern border. The Philippines has continued its subnational elimination efforts, having declared 62 provinces out of 81 provinces as malaria free, as of 2021. ■ In the GMS, three countries – Cambodia, the Lao People’s Democratic Republic and Viet Nam – aim to eliminate P. falciparum by 2023, with support from a regional artemisinin-resistance initiative financed by the Global Fund. Two countries in the GMS – Cambodia and Thailand – aim to eliminate all species of malaria by 2025. The remaining GMS countries aim to eliminate all species of malaria by 2030. The percentage of reported indigenous cases in Cambodia due to P. falciparum has fallen significantly, from 55% in 2015 to 8% in 2021, owing to intensified efforts in community outreach and active case detection. ■ Vector resistance to pyrethroids was confirmed in 49% of the sites tested, to organochlorines in 63%, to carbamates in 17% and to organophosphates in 56%. Six malaria endemic countries have developed insecticide resistance monitoring and management plans. ■ Challenges include resurgence of malaria in Solomon Islands and sustained high levels of malaria in Papua New Guinea owing to challenges in health system strengthening and often inadequate surveillance and response activities to halt transmission in the highest burden provinces. Recent efforts are underway to improve access to services and case-based surveillance in the Pacific Island countries, and intensified community efforts to halt malaria transmission in the GMS countries, particularly in Cambodia and the Lao People’s Democratic Republic. Although all countries have reported minor disruptions to implementing malaria interventions due to the COVID-19 pandemic, no major delays to service delivery were reported. D. Share of estimated malaria cases, 2021 E. Percentage of Plasmodium species from indigenous cases, 2010 and 2021 Philippines Papua New Guinea Viet Nam Lao People’s Democratic Republic Solomon Islands Cambodia Vanuatu Republic of Korea Malaysia 100500 100500 P. falciparum and mixed P. vivax Other 2010 2021 Zero indigenous cases ■ Papua New Guinea ■ Solomon Islands ■ Cambodia 1% 11% Lao People’s Democratic Republic, 0.4% Philippines, 0.7% Viet Nam, <0.1% Vanuatu, <0.1% Republic of Korea, <0.1% 87% a There have been no estimated indigenous deaths between 2015 and 2020 in these countries; b Malaria cases in 2015 are likely to be underestimated; this confounds assessment of progress towards the 2020 GTS target relative to a 2015 baseline; c Change in estimated incidence is more than 100%. Note: Countries with zero cases: China and Malaysia. I. Change in estimated malaria incidence and mortality rates, 2015–2021 J. Reported indigenous cases in countries with national elimination activities, 2015 versus 2021 Note: China was certified malaria free in 2021. a Malaria cases in 2015 are likely to be underestimated; this confounds assessment of progress towards the 2020 GTS target relative to a 2015 baseline. 201520142013201220112010 2016 2017 2018 2019 2020 2021 500 000 400 000 300 000 200 000 100 000 0 Baseline (2015) ■ Republic of Korea ■ Malaysia ■ Viet Nam ■ Lao People’s Democratic Republic ■ Philippines ■ Cambodia 201520142013201220112010 2016 2017 2018 2019 2020 2021 25 000 20 000 15 000 10 000 5 000 0 ■ Vanuatua Baseline (2015) 201520142013201220112010 2016 2017 2018 2019 2020 2021 1 750 000 1 400 000 1 050 000 700 000 350 000 0 ■ Solomon Islands ■ Papua New Guinea Baseline (2015) F. Estimated number of cases in countries that reduced case incidence by ≥40% in 2021 compared with 2015 H. Estimated number of cases in countries with an increase or no change in case incidence, 2015–2021 G. Estimated number of cases in countries that reduced case incidence by <40% in 2021 compared with 2015 a Malaria cases in 2015 are likely to be underestimated; this confounds assessment of progress towards the 2020 GTS target relative to a 2015 baseline. Malaysia Viet Nam Cambodia Lao People’s Democratic Republic Philippines Republic of Koreaa Vanuatua,b Papua New Guinea Solomon Islandsc 2020 milestone: -40% f Reduction Increase p 80%60%40%20%0%-20%-40%-60%-80%-100% 100% ■ Incidence ■ Mortality Cambodia Lao People’s Democratic Republic Viet Nam Republic of Korea Vanuatua Malaysia 15 000 30 0000 60 00045 000 75 000 ■ 2015 ■ 2021 4 382 68 109 3 897 36 078 377 9 331 274 627 312 242 0 571 W O RL D M AL AR IA R EP O RT 2 02 2 169 ANNEx 4 – A. POLICY ADOPTION, 2021 WHO region Country/area Insecticide-treated mosquito nets Indoor residual spraying Chemoprevention Testing Treatment ITNs/LLINs are distributed free of charge ITNs/LLINs are distributed through ANC ITNs/LLINs are distributed through EPI/well baby clinic ITNs/LLINs are distributed through mass campaigns IRS is recommended by malaria control programme DDT is used for IRS IPTp is used to prevent malaria during pregnancy SMC or IPTc is used Malaria diagnosis is free of charge in the private sector Malaria diagnosis is free of charge in the public sector RDTs are used at community level G6PD test is recommended before treatment with primaquine ACT for treatment of P. falciparum Pre-referral treatment with quinine or artemether IM or artesunate suppositories Single low dose of primaquine with ACT to reduce transmissibility of P. falciparum1 Primaquine is used for radical treatment of P. vivax cases Directly observed treatment with primaquine is undertaken AFRICAN Angola ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Benin ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Botswana ● ● ● ● ● ● NA NA ● ● ● NA ● ● ● ● ● Burkina Faso ● ● ● ● ● ● ● ● ● ● ● NA ● ● NA NA NA Burundi ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Cabo Verde NA NA NA NA ● ● NA NA – ● ● NA ● ● ● ● ● Cameroon ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Central African Republic ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Chad ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Comoros ● ● ● ● ● ● ● NA – ● ● NA ● ● ● ● ● Congo ● ● ● ● ● ● ● ● ● ● ● NA ● ● NA NA NA Côte d'Ivoire ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Democratic Republic of the Congo ● ● ● ● ● ● ● ● ● ● ● NA ● ● NA NA NA Equatorial Guinea ● ● ● ● ● ● ● ● – ● ● ● ● ● ● ● ● Eritrea ● ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● Eswatini NA NA NA ● ● ● NA NA ● ● ● ● ● ● ● ● ● Ethiopia ● ● ● ● ● ● ● NA ● ● ● ● ● ● ● ● ● Gabon ● ● ● ● ● ● ● ● ● ● ● NA ● ● NA NA NA Gambia ● ● ● ● ● ● ● ● ● ● ● NA ● ● NA NA NA Ghana ● ● ● ● ● ● ● ● NA ● ● ● ● ● ● ● NA Guinea ● ● ● ● ● ● ● ● ● ● ● NA ● ● ● NA NA Guinea-Bissau ● ● ● ● ● ● ● ● – ● ● ● ● ● ● ● ● Kenya ● ● ● ● ● ● ● ● ● ● ● NA ● ● ● ● NA Liberia ● ● ● ● ● ● ● ● – ● ● ● ● ● ● ● ● Madagascar ● ● ● ● ● ● ● NA ● ● ● ● ● ● ● ● ● Malawi ● ● ● ● ● ● ● ● ● ● ● NA ● ● ● NA ● Mali ● ● ● ● ● ● ● ● ● ● ● NA ● ● ● NA NA Mauritania ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Mayotte – – – – – – NA NA – ● ● ● ● ● ● ● ● Mozambique ● ● ● ● ● ● ● ● – ● ● ● ● ● ● ● ● Namibia ● NA NA ● ● ● NA NA – ● ● ● ● ● ● ● ● Niger ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Nigeria ● ● ● ● ● ● ● ● – ● ● ● ● ● ● NA NA Rwanda ● ● ● ● ● ● NA NA ● ● ● NA ● ● ● ● NA Sao Tome and Principe ● ● ● ● ● ● ● NA ● ● ● ● ● ● ● ● ● Senegal ● ● ● ● ● ● ● ● ● ● ● NA ● ● ● NA NA Sierra Leone ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● South Africa ● ● ● ● ● ● ● NA ● ● ● ● ● ● ● ● ● South Sudan2 ● ● ● ● ● ● ● ● NA ● ● NA ● ● NA NA NA Togo ● ● ● ● ● ● ● ● – ● ● – ● ● ● ● ● Uganda ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● United Republic of Tanzania3 Mainland ● ● ● ● ● ● ● ● ● ● ● NA ● ● ● NA NA Zanzibar ● ● ● ● ● ● NA ● ● ● ● ● ● ● ● ● ● Zambia ● ● ● ● ● ● ● ● ● ● ● NA ● ● NA NA NA Zimbabwe ● ● ● ● ● ● ● NA ● ● ● ● ● ● ● ● ● AMERICAS Belize ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Bolivia (Plurinational State of) ● ● ● ● ● ● NA NA – ● ● ● ● – ● ● ● Brazil ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Colombia ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Costa Rica ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Dominican Republic ● ● ● ● ● ● NA NA ● ● ● ● ● NA ● ● ● Ecuador ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● 170 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – A. POLICY ADOPTION, 2021 WHO region Country/area Insecticide-treated mosquito nets Indoor residual spraying Chemoprevention Testing Treatment ITNs/LLINs are distributed free of charge ITNs/LLINs are distributed through ANC ITNs/LLINs are distributed through EPI/well baby clinic ITNs/LLINs are distributed through mass campaigns IRS is recommended by malaria control programme DDT is used for IRS IPTp is used to prevent malaria during pregnancy SMC or IPTc is used Malaria diagnosis is free of charge in the private sector Malaria diagnosis is free of charge in the public sector RDTs are used at community level G6PD test is recommended before treatment with primaquine ACT for treatment of P. falciparum Pre-referral treatment with quinine or artemether IM or artesunate suppositories Single low dose of primaquine with ACT to reduce transmissibility of P. falciparum1 Primaquine is used for radical treatment of P. vivax cases Directly observed treatment with primaquine is undertaken AFRICAN Angola ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Benin ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Botswana ● ● ● ● ● ● NA NA ● ● ● NA ● ● ● ● ● Burkina Faso ● ● ● ● ● ● ● ● ● ● ● NA ● ● NA NA NA Burundi ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Cabo Verde NA NA NA NA ● ● NA NA – ● ● NA ● ● ● ● ● Cameroon ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Central African Republic ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Chad ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Comoros ● ● ● ● ● ● ● NA – ● ● NA ● ● ● ● ● Congo ● ● ● ● ● ● ● ● ● ● ● NA ● ● NA NA NA Côte d'Ivoire ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Democratic Republic of the Congo ● ● ● ● ● ● ● ● ● ● ● NA ● ● NA NA NA Equatorial Guinea ● ● ● ● ● ● ● ● – ● ● ● ● ● ● ● ● Eritrea ● ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● Eswatini NA NA NA ● ● ● NA NA ● ● ● ● ● ● ● ● ● Ethiopia ● ● ● ● ● ● ● NA ● ● ● ● ● ● ● ● ● Gabon ● ● ● ● ● ● ● ● ● ● ● NA ● ● NA NA NA Gambia ● ● ● ● ● ● ● ● ● ● ● NA ● ● NA NA NA Ghana ● ● ● ● ● ● ● ● NA ● ● ● ● ● ● ● NA Guinea ● ● ● ● ● ● ● ● ● ● ● NA ● ● ● NA NA Guinea-Bissau ● ● ● ● ● ● ● ● – ● ● ● ● ● ● ● ● Kenya ● ● ● ● ● ● ● ● ● ● ● NA ● ● ● ● NA Liberia ● ● ● ● ● ● ● ● – ● ● ● ● ● ● ● ● Madagascar ● ● ● ● ● ● ● NA ● ● ● ● ● ● ● ● ● Malawi ● ● ● ● ● ● ● ● ● ● ● NA ● ● ● NA ● Mali ● ● ● ● ● ● ● ● ● ● ● NA ● ● ● NA NA Mauritania ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Mayotte – – – – – – NA NA – ● ● ● ● ● ● ● ● Mozambique ● ● ● ● ● ● ● ● – ● ● ● ● ● ● ● ● Namibia ● NA NA ● ● ● NA NA – ● ● ● ● ● ● ● ● Niger ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Nigeria ● ● ● ● ● ● ● ● – ● ● ● ● ● ● NA NA Rwanda ● ● ● ● ● ● NA NA ● ● ● NA ● ● ● ● NA Sao Tome and Principe ● ● ● ● ● ● ● NA ● ● ● ● ● ● ● ● ● Senegal ● ● ● ● ● ● ● ● ● ● ● NA ● ● ● NA NA Sierra Leone ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● South Africa ● ● ● ● ● ● ● NA ● ● ● ● ● ● ● ● ● South Sudan2 ● ● ● ● ● ● ● ● NA ● ● NA ● ● NA NA NA Togo ● ● ● ● ● ● ● ● – ● ● – ● ● ● ● ● Uganda ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● United Republic of Tanzania3 Mainland ● ● ● ● ● ● ● ● ● ● ● NA ● ● ● NA NA Zanzibar ● ● ● ● ● ● NA ● ● ● ● ● ● ● ● ● ● Zambia ● ● ● ● ● ● ● ● ● ● ● NA ● ● NA NA NA Zimbabwe ● ● ● ● ● ● ● NA ● ● ● ● ● ● ● ● ● AMERICAS Belize ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Bolivia (Plurinational State of) ● ● ● ● ● ● NA NA – ● ● ● ● – ● ● ● Brazil ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Colombia ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Costa Rica ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Dominican Republic ● ● ● ● ● ● NA NA ● ● ● ● ● NA ● ● ● Ecuador ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● 171 ANNEx 4 – A. POLICY ADOPTION, 2021 WHO region Country/area Insecticide-treated mosquito nets Indoor residual spraying Chemoprevention Testing Treatment ITNs/LLINs are distributed free of charge ITNs/LLINs are distributed through ANC ITNs/LLINs are distributed through EPI/well baby clinic ITNs/LLINs are distributed through mass campaigns IRS is recommended by malaria control programme DDT is used for IRS IPTp is used to prevent malaria during pregnancy SMC or IPTc is used Malaria diagnosis is free of charge in the private sector Malaria diagnosis is free of charge in the public sector RDTs are used at community level G6PD test is recommended before treatment with primaquine ACT for treatment of P. falciparum Pre-referral treatment with quinine or artemether IM or artesunate suppositories Single low dose of primaquine with ACT to reduce transmissibility of P. falciparum1 Primaquine is used for radical treatment of P. vivax cases Directly observed treatment with primaquine is undertaken AMERICAS French Guiana ● ● ● ● ● ● NA NA – ● ● ● ● ● ● ● ● Guatemala ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Guyana ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Haiti ● ● ● ● ● ● NA NA ● ● ● ● ● NA ● ● ● Honduras ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Mexico ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Nicaragua ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Panama ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Peru ● ● ● ● ● ● NA NA – ● ● ● ● ● ● ● ● Suriname ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Venezuela (Bolivarian Republic of) ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● EASTERN MEDITERRANEAN Afghanistan ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Djibouti ● ● ● ● ● ● NA NA – ● ● ● ● ● ● ● ● Iran (Islamic Republic of) ● ● ● ● ● ● NA NA – ● ● ● ● ● ● ● ● Pakistan ● ● ● ● ● ● NA NA NA ● NA ● ● NA ● ● ● Saudi Arabia ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Somalia ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Sudan ● ● ● ● ● ● ● NA ● ● ● ● ● ● ● ● ● Yemen ● ● ● ● ● ● NA NA NA ● ● ● ● ● ● ● ● SOUTH‑EAST ASIA Bangladesh ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Bhutan ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Democratic People's Republic of Korea ● ● ● ● ● ● NA NA NA ● NA ● NA NA NA ● ● India ● ● ● ● ● ● NA NA NA ● ● ● ● ● ● ● NA Indonesia ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Myanmar ● ● ● ● ● ● NA NA ● ● ● NA ● ● ● ● ● Nepal ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Thailand ● ● NA ● ● ● NA NA ● ● ● ● ● ● ● ● ● Timor-Leste ● ● ● ● ● ● NA NA – ● ● ● ● ● ● ● ● WESTERN PACIFIC Cambodia ● ● ● ● ● ● NA NA – ● ● ● ● ● ● ● ● Lao People's Democratic Republic ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Malaysia ● ● ● ● ● ● NA NA ● ● NA NA ● ● ● ● ● Papua New Guinea ● ● ● ● ● ● ● NA ● ● ● ● ● ● ● ● ● Philippines ● ● ● ● ● ● NA NA ● ● ● NA ● ● ● ● ● Republic of Korea ● NA NA ● ● ● NA NA NA ● ● ● ● NA NA ● ● Solomon Islands ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● NA Vanuatu ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Viet Nam ● NA NA ● ● ● NA NA NA ● ● ● ● ● ● ● ● ACT: artemisinin-based combination therapy; ANC: antenatal care; DDT: dichloro-diphenyl-trichloroethane; EPI: Expanded Programme on Immunization; G6PD: glucose-6-phosphate dehydrogenase; IM: intramuscular; IPTc: intermittent preventive treatment of malaria in children; IPTp: intermittent preventive treatment of malaria in pregnancy; IRS: indoor residual spraying; ITN: insecticide-treated mosquito net; LLIN: long-lasting insecticidal net; P.: Plasmodium; RDT: rapid diagnostic test; SMC: seasonal malaria chemoprevention; WHO: World Health Organization. 1 Single dose of primaquine (0.75 mg base/kg) for countries in the WHO Region of the Americas. 2 In May 2013, South Sudan was reassigned to the WHO African Region (WHA resolution 66.21, https://apps.who.int/gb/ebwha/pdf_files/ WHA66/A66_R21-en.pdf). 3 Where national data for the United Republic of Tanzania are unavailable, refer to Mainland and Zanzibar. ● = Policy exists and has been implemented this year. ● = Policy exists but is not implemented this year or no data exist to support implementation.  = Policy does not exist.  = Policy discontinued.  = Both microscopy and RDT are free.  = Diagnosis is free but diagnostic test was not specified. Data as of 24 November 2022  = Only microscopy is free.  = Only RDT is free.  = Neither microscopy nor RDT are free. NA = Policy not applicable. – = Question not answered and there is no information from previous years. 172 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – A. POLICY ADOPTION, 2021 WHO region Country/area Insecticide-treated mosquito nets Indoor residual spraying Chemoprevention Testing Treatment ITNs/LLINs are distributed free of charge ITNs/LLINs are distributed through ANC ITNs/LLINs are distributed through EPI/well baby clinic ITNs/LLINs are distributed through mass campaigns IRS is recommended by malaria control programme DDT is used for IRS IPTp is used to prevent malaria during pregnancy SMC or IPTc is used Malaria diagnosis is free of charge in the private sector Malaria diagnosis is free of charge in the public sector RDTs are used at community level G6PD test is recommended before treatment with primaquine ACT for treatment of P. falciparum Pre-referral treatment with quinine or artemether IM or artesunate suppositories Single low dose of primaquine with ACT to reduce transmissibility of P. falciparum1 Primaquine is used for radical treatment of P. vivax cases Directly observed treatment with primaquine is undertaken AMERICAS French Guiana ● ● ● ● ● ● NA NA – ● ● ● ● ● ● ● ● Guatemala ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Guyana ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Haiti ● ● ● ● ● ● NA NA ● ● ● ● ● NA ● ● ● Honduras ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Mexico ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Nicaragua ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Panama ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Peru ● ● ● ● ● ● NA NA – ● ● ● ● ● ● ● ● Suriname ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Venezuela (Bolivarian Republic of) ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● EASTERN MEDITERRANEAN Afghanistan ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Djibouti ● ● ● ● ● ● NA NA – ● ● ● ● ● ● ● ● Iran (Islamic Republic of) ● ● ● ● ● ● NA NA – ● ● ● ● ● ● ● ● Pakistan ● ● ● ● ● ● NA NA NA ● NA ● ● NA ● ● ● Saudi Arabia ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Somalia ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Sudan ● ● ● ● ● ● ● NA ● ● ● ● ● ● ● ● ● Yemen ● ● ● ● ● ● NA NA NA ● ● ● ● ● ● ● ● SOUTH‑EAST ASIA Bangladesh ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Bhutan ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Democratic People's Republic of Korea ● ● ● ● ● ● NA NA NA ● NA ● NA NA NA ● ● India ● ● ● ● ● ● NA NA NA ● ● ● ● ● ● ● NA Indonesia ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Myanmar ● ● ● ● ● ● NA NA ● ● ● NA ● ● ● ● ● Nepal ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Thailand ● ● NA ● ● ● NA NA ● ● ● ● ● ● ● ● ● Timor-Leste ● ● ● ● ● ● NA NA – ● ● ● ● ● ● ● ● WESTERN PACIFIC Cambodia ● ● ● ● ● ● NA NA – ● ● ● ● ● ● ● ● Lao People's Democratic Republic ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Malaysia ● ● ● ● ● ● NA NA ● ● NA NA ● ● ● ● ● Papua New Guinea ● ● ● ● ● ● ● NA ● ● ● ● ● ● ● ● ● Philippines ● ● ● ● ● ● NA NA ● ● ● NA ● ● ● ● ● Republic of Korea ● NA NA ● ● ● NA NA NA ● ● ● ● NA NA ● ● Solomon Islands ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● NA Vanuatu ● ● ● ● ● ● NA NA ● ● ● ● ● ● ● ● ● Viet Nam ● NA NA ● ● ● NA NA NA ● ● ● ● ● ● ● ● ACT: artemisinin-based combination therapy; ANC: antenatal care; DDT: dichloro-diphenyl-trichloroethane; EPI: Expanded Programme on Immunization; G6PD: glucose-6-phosphate dehydrogenase; IM: intramuscular; IPTc: intermittent preventive treatment of malaria in children; IPTp: intermittent preventive treatment of malaria in pregnancy; IRS: indoor residual spraying; ITN: insecticide-treated mosquito net; LLIN: long-lasting insecticidal net; P.: Plasmodium; RDT: rapid diagnostic test; SMC: seasonal malaria chemoprevention; WHO: World Health Organization. 1 Single dose of primaquine (0.75 mg base/kg) for countries in the WHO Region of the Americas. 2 In May 2013, South Sudan was reassigned to the WHO African Region (WHA resolution 66.21, https://apps.who.int/gb/ebwha/pdf_files/ WHA66/A66_R21-en.pdf). 3 Where national data for the United Republic of Tanzania are unavailable, refer to Mainland and Zanzibar. ● = Policy exists and has been implemented this year. ● = Policy exists but is not implemented this year or no data exist to support implementation.  = Policy does not exist.  = Policy discontinued.  = Both microscopy and RDT are free.  = Diagnosis is free but diagnostic test was not specified. Data as of 24 November 2022  = Only microscopy is free.  = Only RDT is free.  = Neither microscopy nor RDT are free. NA = Policy not applicable. – = Question not answered and there is no information from previous years. 173 ANNEx 4 – B. ANTIMALARIAL DRUG POLICY, 2021 WHO region Country/area P. falciparum P. vivax Uncomplicated unconfirmed Uncomplicated confirmed Severe Prevention during pregnancy Treatment AFRICAN Angola AL AL AS SP(IPT) AL+PQ Benin – AL AS SP(IPT) NA Botswana – AL+PQ AS NA AL+PQ Burkina Faso AL AL AS SP(IPT) NA Burundi AL AL ART; AS SP(IPT) NA Cabo Verde – AL+PQ; AS+AQ+PQ QN NA – Cameroon NA AL; AS-PYR; AS+AQ; DHA-PPQ AM; AS; QN SP(IPT) NA Central African Republic AL AL AS SP(IPT) NA Chad AL; AS+AQ AL; AS+AQ ART; AS; QN SP(IPT) NA Comoros AL AL; AL+PQ AS SP(IPT) NA Congo AS+AQ AS+AQ AS SP(IPT) NA Côte d'Ivoire AL; AS+AQ AL; AS+AQ QN SP(IPT) NA Democratic Republic of the Congo AS+AQ AL; AS-PYR; AS+AQ AS; QN SP(IPT) NA Equatorial Guinea AS+AQ AL; AS+AQ AS SP(IPT) NA Eritrea AS+AQ AS+AQ+PQ AS – AS+AQ+PQ Eswatini – AL-PQ AS NA PQ Ethiopia AL+PQ AL-PQ AS+AL+PQ CQ CQ+PQ Gabon AL; AS+AQ AL; AS+AQ AS SP(IPT) NA Gambia AL AL AS SP(IPT) NA Ghana AL; AS+AQ; DHA-PPQ AL; AS+AQ AM; AS; QN SP(IPT) AL+PQ; AS+AQ+PQ; DHA-PPQ+PQ Guinea AL AL AS-QN; AS+AL SP(IPT) NA Guinea-Bissau AL AL QN SP(IPT) NA Kenya AL AL AS SP(IPT) AL-PQ Liberia AL; AS+AQ AL; AS+AQ AM; AS; QN SP(IPT) NA Madagascar AS+AQ AS+AQ+PQ ART SP(IPT) AS+AQ Malawi AL AL AS SP(IPT) NA Mali AL AL-PQ AS SP(IPT) AL Mauritania AS+AQ AS+AQ+PQ AS SP(IPT) AS+AQ+PQ Mayotte – – – – – Mozambique AL AS+AQ AS SP(IPT) NA Namibia AL AL-PQ QN NA AL-PQ Niger AL AL-PQ AS; QN SP(IPT) NA Nigeria AL; AS+AQ AL; AS-PYR; AS+AQ; DHA-PPQ AS SP(IPT) NA Rwanda AL AL AS; QN NA ACT+PQ Sao Tome and Principe – AS+MQ+PQ AS SP(IPT) NA Senegal NA AL; AS+AQ+PQ AS SP(IPT) NA Sierra Leone AL AL; AS+AQ AM; AS; QN SP(IPT) NA South Africa AL AL-PQ AS; QN – AL-PQ South Sudan1 AS+AQ AS+AQ AM; AS; QN SP(IPT) AS+AQ+PQ Togo AL; AS+AQ AL; AS+AQ AS; AM; QN SP(IPT) NA Uganda – AL AS SP(IPT) AL-PQ United Republic of Tanzania – – – – – Mainland AL AL-PQ AM; AS; QN SP(IPT) NA Zanzibar NA AS+AQ+PQ AS NA PQ Zambia AL AL AS SP(IPT) NA Zimbabwe – AL-PQ AS SP(IPT) NA AMERICAS Belize – – – NA – Bolivia (Plurinational State of) – AL+PQ AS NA CQ+PQ Brazil – AL+PQ; AS+MQ+PQ AS CQ CQ+PQ Colombia AL+PQ AL; AL-PQ AS CQ CQ+PQ Costa Rica CQ+PQ CQ+PQ AS CQ CQ+PQ 174 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – B. ANTIMALARIAL DRUG POLICY, 2021 WHO region Country/area P. falciparum P. vivax Uncomplicated unconfirmed Uncomplicated confirmed Severe Prevention during pregnancy Treatment AMERICAS Dominican Republic – CQ+PQ AS NA CQ+PQ Ecuador AL+AM AL+AM+PQ AS NA CQ+PQ French Guiana – AL+PQ AS QN CQ+PQ Guatemala – CQ+PQ AL; CQ; QN NA CQ+PQ Guyana – AL+PQ AM; AS-QN; QN+CL NA CQ+PQ Haiti – CQ+PQ AS NA CQ+PQ Honduras – CQ+PQ AS NA CQ+PQ Mexico NA AL; AL-PQ AL; AL+PQ; AS NA CQ+PQ Nicaragua – CQ+PQ AS NA CQ+PQ Panama AL-PQ AL+PQ AS CQ CQ+PQ Peru – AS+MQ+PQ AS NA CQ+PQ Suriname – AL+PQ AS NA CQ+PQ Venezuela (Bolivarian Republic of) – AL+PQ – NA CQ+PQ EASTERN MEDITERRANEAN Afghanistan CQ AL+PQ AM; AS; QN NA CQ+PQ Djibouti AL AL+PQ AS NA AL+PQ Iran (Islamic Republic of) – AS+SP+PQ AS; QN NA CQ+PQ Pakistan CQ AL-PQ AS NA CQ+PQ Saudi Arabia NA AS+SP+PQ AS+AM+QN NA CQ+PQ Somalia AL AL-PQ AS SP(IPT) AL+PQ Sudan – AL QN+AS SP(IPT) AL+PQ Yemen AS+SP AS+SP+PQ AS; QN NA CQ+PQ SOUTH‑EAST ASIA Bangladesh NA AL+PQ AS+AL+PQ NA CQ+PQ Bhutan AL-PQ AL-PQ AM; AS; QN NA CQ+PQ Democratic People's Republic of Korea NA NA NA NA CQ+PQ India NA AL+PQ; AS+SP+PQ AM; AS; QN NA CQ+PQ Indonesia – DHA-PPQ AS NA DHA+PPQ+PQ Myanmar AL+PQ AL+PQ AM; AS; QN NA CQ+PQ Nepal AL AL-PQ AS NA CQ+PQ Thailand – DHA-PPQ+PQ AS; QN NA CQ+PQ Timor-Leste AL+PQ AL+PQ AS; QN NA AL+PQ WESTERN PACIFIC Cambodia AS+MQ AS+MQ+PQ AM; AS NA AS+MQ+PQ Lao People's Democratic Republic – AL+PQ AS NA AL-PQ Malaysia NA AL-PQ AS NA AL+PQ Papua New Guinea AL AL-PQ AM; AS SP(IPT) AL+PQ Philippines – AL+PQ AS NA AL+PQ Republic of Korea AL AL AL NA CQ+PQ Solomon Islands AL+PQ AL; AL+PQ AS+AL+PQ CQ AL+PQ Vanuatu – AL-PQ AS CQ PQ Viet Nam AS-PYR; DHA-PPQ DHA-PPQ AS NA CQ+PQ Data as of 28 October 2022 ACT: artemisinin-based combination therapy; AL: artemether-lumefantrine; AM: artemether; AQ: amodiaquine; ART: artemisinin; AS: artesunate; CL: clindamycin; CQ: chloroquine; DHA: dihydroartemisinin; IPT: intermittent preventive treatment of malaria; MQ: mefloquine; NA: not applicable; P.: Plasmodium; PPQ: piperaquine; PQ: primaquine; PYR: pyronaridine; QN: quinine; SP: sulfadoxine- pyrimethamine; WHO: World Health Organization. “–” refers to data not available. 1 In May 2013, South Sudan was reassigned to the WHO African Region (WHA resolution 66.21, https://apps.who.int/gb/ebwha/pdf_files/ WHA66/A66_R21-en.pdf). 175 ANNEx 4 – C. FUNDING FOR MALARIA CONTROL, 2019–2021 WHO region Country/area Year Contributions reported by donors Contributions reported by countries Global Fund¹ PMI/USAID² World Bank³ United Kingdom4 Government (NMP) Global Fund World Bank PMI/USAID Other bilaterals WHO UNICEF Other contributions7 AFRICAN Angola 2019 5 300 409 23 191 738 – – 1 850 026 2 864 156 – 20 000 000 – – – – 2020 6 170 967 19 789 520 – – 2 496 965 – – 22 000 000 – – – – 2021 2 328 851 19 000 000 – – 15 437 196 – – 19 000 000 – – – – Benin 2019 14 939 190 17 920 889 – – 11 479 489 15 167 653 0 2 435 941 0 0 0 0 2020 12 917 281 17 706 413 – – 217 252 19 234 523 0 3 267 868 0 0 0 0 2021 12 141 226 16 500 000 – – 1 475 597 6 969 348 – 13 915 191 – 65 372 – 43 978 Botswana 2019 285 055 – – – 2 580 459 219 328 0 0 0 – 0 0 2020 138 958 – – – 28 180 263 – – – – – – – 2021 347 610 – – – – – – – – – – – Burkina Faso 2019 35 071 984 26 354 248 12 370 892 – 12 571 349 5 66 864 802 6 473 917 20 960 657 – 107 706 546 944 – 2020 40 459 954 27 080 396 1 409 246 – 57 406 172 27 553 483 42 623 18 844 577 – 52 206 333 334 8 289 677 2021 66 757 381 27 500 000 1 409 246 – 46 732 479 23 061 297 0 14 739 304 – 17 066 331 713 7 443 864 Burundi 2019 32 833 267 8 433 359 – – 4 563 477 24 301 509 – 4 734 719 – 159 500 372 925 – 2020 21 644 596 8 332 430 – – 10 230 986 489 – – – 11 959 75 337 – 2021 34 574 830 7 500 000 – – 2 395 043 532 091 – – – 185 887 82 603 – Cabo Verde 2019 – – – – 547 281 116 809 – – – 82 598 – – 2020 – – – – 630 624 182 196 – – – 11 497 – – 2021 – – – – 823 803 305 597 – – – 7 747 – 841 304 Cameroon 2019 33 082 524 23 718 823 – – 64 509 432 6 33 828 144 0 21 148 951 0 0 0 0 2020 15 509 982 24 476 512 – 152 394 45 529 320 24 499 314 0 27 157 756 0 0 0 0 2021 19 899 167 22 500 000 – 152 394 44 171 440 27 717 241 0 22 434 482 0 0 0 0 Central African Republic 2019 11 855 065 – – – 162 822 6 16 631 715 – – – 199 800 656 890 – 2020 14 599 693 – – – 172 846 15 452 952 – – – 50 000 – 1 273 044 2021 19 395 525 – – – 51 960 15 068 – – – 20 000 – – Chad 2019 40 139 163 – – – 0 – – – – – – – 2020 13 717 911 – – – 0 – – – – – – – 2021 24 286 670 – – – – – – – – – – – Comoros 2019 1 592 918 – – – 120 896 5 824 954 – – – – – – 2020 2 007 101 – – – 50 278 1 968 573 – – – – 1 932 – 2021 1 256 457 – – – 322 003 1 658 731 0 0 38 604 15 627 – – Congo 2019 10 841 019 – – – 1 360 219 6 689 800 0 0 0 67 741 0 15 000 2020 5 373 274 – – – 18 826 146 6 12 660 948 0 7 200 0 0 0 15 000 2021 5 077 624 – – – – – – – – – – – Côte d’Ivoire 2019 60 074 073 26 354 248 – – 6 428 287 60 947 905 – 21 342 862 – 5 984 60 980 2 500 000 2020 47 183 774 26 038 843 – – 18 144 667 33 908 462 – 25 000 000 – – – – 2021 42 348 291 25 000 000 – – 13 921 781 22 074 036 0 25 000 000 0 8 211 5 076 318 0 Democratic Republic of the Congo 2019 124 338 786 52 708 496 – 788 010 1 504 555 112 504 296 0 41 897 052 – 148 208 802 250 – 2020 158 030 595 57 285 454 – 1 842 905 1 486 548 141 146 584 0 39 293 479 0 412 688 0 32 000 000 2021 143 878 029 54 500 000 – 1 842 905 1 427 241 104 336 236 0 70 289 620 0 328 000 0 32 000 000 Equatorial Guinea 2019 -230 482 – – – 3 324 311 5 – – – – – – – 2020 – – – – 3 353 293 5 – – – – – – – 2021 – – – – 3 219 511 5 – – – – – – – Eritrea 2019 9 427 262 – – – 423 307 5 4 788 233 0 0 0 120 000 0 0 2020 4 649 703 – – – 426 998 5 12 302 113 – – – 0 – – 2021 4 210 626 – – – 409 962 5 3 133 493 – – – – – – Eswatini 2019 881 581 – – – 883 848 1 005 542 0 0 0 10 613 0 0 2020 589 281 – – – 916 776 736 128 0 0 0 10 613 0 0 2021 1 260 814 – – – 841 829 1 434 576 0 0 0 – 0 0 Ethiopia 2019 28 113 549 37 950 117 – – 24 148 647 26 083 562 – 18 000 000 – – – 122 344 828 2020 15 193 760 37 495 933 – – 27 736 303 27 356 758 – 32 000 000 – – – – 2021 23 136 737 36 000 000 – – 26 856 645 22 014 812 – 36 000 000 – – – – 176 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – C. FUNDING FOR MALARIA CONTROL, 2019–2021 WHO region Country/area Year Contributions reported by donors Contributions reported by countries Global Fund¹ PMI/USAID² World Bank³ United Kingdom4 Government (NMP) Global Fund World Bank PMI/USAID Other bilaterals WHO UNICEF Other contributions7 AFRICAN Angola 2019 5 300 409 23 191 738 – – 1 850 026 2 864 156 – 20 000 000 – – – – 2020 6 170 967 19 789 520 – – 2 496 965 – – 22 000 000 – – – – 2021 2 328 851 19 000 000 – – 15 437 196 – – 19 000 000 – – – – Benin 2019 14 939 190 17 920 889 – – 11 479 489 15 167 653 0 2 435 941 0 0 0 0 2020 12 917 281 17 706 413 – – 217 252 19 234 523 0 3 267 868 0 0 0 0 2021 12 141 226 16 500 000 – – 1 475 597 6 969 348 – 13 915 191 – 65 372 – 43 978 Botswana 2019 285 055 – – – 2 580 459 219 328 0 0 0 – 0 0 2020 138 958 – – – 28 180 263 – – – – – – – 2021 347 610 – – – – – – – – – – – Burkina Faso 2019 35 071 984 26 354 248 12 370 892 – 12 571 349 5 66 864 802 6 473 917 20 960 657 – 107 706 546 944 – 2020 40 459 954 27 080 396 1 409 246 – 57 406 172 27 553 483 42 623 18 844 577 – 52 206 333 334 8 289 677 2021 66 757 381 27 500 000 1 409 246 – 46 732 479 23 061 297 0 14 739 304 – 17 066 331 713 7 443 864 Burundi 2019 32 833 267 8 433 359 – – 4 563 477 24 301 509 – 4 734 719 – 159 500 372 925 – 2020 21 644 596 8 332 430 – – 10 230 986 489 – – – 11 959 75 337 – 2021 34 574 830 7 500 000 – – 2 395 043 532 091 – – – 185 887 82 603 – Cabo Verde 2019 – – – – 547 281 116 809 – – – 82 598 – – 2020 – – – – 630 624 182 196 – – – 11 497 – – 2021 – – – – 823 803 305 597 – – – 7 747 – 841 304 Cameroon 2019 33 082 524 23 718 823 – – 64 509 432 6 33 828 144 0 21 148 951 0 0 0 0 2020 15 509 982 24 476 512 – 152 394 45 529 320 24 499 314 0 27 157 756 0 0 0 0 2021 19 899 167 22 500 000 – 152 394 44 171 440 27 717 241 0 22 434 482 0 0 0 0 Central African Republic 2019 11 855 065 – – – 162 822 6 16 631 715 – – – 199 800 656 890 – 2020 14 599 693 – – – 172 846 15 452 952 – – – 50 000 – 1 273 044 2021 19 395 525 – – – 51 960 15 068 – – – 20 000 – – Chad 2019 40 139 163 – – – 0 – – – – – – – 2020 13 717 911 – – – 0 – – – – – – – 2021 24 286 670 – – – – – – – – – – – Comoros 2019 1 592 918 – – – 120 896 5 824 954 – – – – – – 2020 2 007 101 – – – 50 278 1 968 573 – – – – 1 932 – 2021 1 256 457 – – – 322 003 1 658 731 0 0 38 604 15 627 – – Congo 2019 10 841 019 – – – 1 360 219 6 689 800 0 0 0 67 741 0 15 000 2020 5 373 274 – – – 18 826 146 6 12 660 948 0 7 200 0 0 0 15 000 2021 5 077 624 – – – – – – – – – – – Côte d’Ivoire 2019 60 074 073 26 354 248 – – 6 428 287 60 947 905 – 21 342 862 – 5 984 60 980 2 500 000 2020 47 183 774 26 038 843 – – 18 144 667 33 908 462 – 25 000 000 – – – – 2021 42 348 291 25 000 000 – – 13 921 781 22 074 036 0 25 000 000 0 8 211 5 076 318 0 Democratic Republic of the Congo 2019 124 338 786 52 708 496 – 788 010 1 504 555 112 504 296 0 41 897 052 – 148 208 802 250 – 2020 158 030 595 57 285 454 – 1 842 905 1 486 548 141 146 584 0 39 293 479 0 412 688 0 32 000 000 2021 143 878 029 54 500 000 – 1 842 905 1 427 241 104 336 236 0 70 289 620 0 328 000 0 32 000 000 Equatorial Guinea 2019 -230 482 – – – 3 324 311 5 – – – – – – – 2020 – – – – 3 353 293 5 – – – – – – – 2021 – – – – 3 219 511 5 – – – – – – – Eritrea 2019 9 427 262 – – – 423 307 5 4 788 233 0 0 0 120 000 0 0 2020 4 649 703 – – – 426 998 5 12 302 113 – – – 0 – – 2021 4 210 626 – – – 409 962 5 3 133 493 – – – – – – Eswatini 2019 881 581 – – – 883 848 1 005 542 0 0 0 10 613 0 0 2020 589 281 – – – 916 776 736 128 0 0 0 10 613 0 0 2021 1 260 814 – – – 841 829 1 434 576 0 0 0 – 0 0 Ethiopia 2019 28 113 549 37 950 117 – – 24 148 647 26 083 562 – 18 000 000 – – – 122 344 828 2020 15 193 760 37 495 933 – – 27 736 303 27 356 758 – 32 000 000 – – – – 2021 23 136 737 36 000 000 – – 26 856 645 22 014 812 – 36 000 000 – – – – 177 ANNEx 4 – C. FUNDING FOR MALARIA CONTROL, 2019–2021 WHO region Country/area Year Contributions reported by donors Contributions reported by countries Global Fund¹ PMI/USAID² World Bank³ United Kingdom4 Government (NMP) Global Fund World Bank PMI/USAID Other bilaterals WHO UNICEF Other contributions7 AFRICAN Gabon 2019 0 – – – 420 789 5 0 – – 1 885 319 – – – 2020 -28 247 – – – 83 574 5 – – – – 2 000 – 6 000 2021 – – – – 314 372 0 0 0 44 064 24 141 15 195 – Gambia 2019 3 596 861 – – – 1 268 631 6 3 940 063 – – – 68 000 90 000 288 646 2020 5 849 843 – – – 961 649 5 – – – – – – – 2021 12 725 069 – – – 1 100 546 15 802 151 – – – – – – Ghana 2019 37 709 188 29 516 758 – 1 541 341 11 350 527 5 28 442 224 0 22 448 510 0 300 000 0 0 2020 37 953 867 29 163 504 – 768 125 11 350 527 5 60 415 856 0 28 000 000 0 300 000 0 0 2021 57 828 600 28 000 000 – 768 125 11 980 643 35 593 618 0 28 000 000 0 300 000 0 0 Guinea 2019 30 552 991 15 812 549 541 430 – 1 002 595 25 261 667 0 15 000 000 0 39 000 0 – 2020 16 545 338 16 664 859 237 996 – 4 584 656 0 0 15 000 000 0 0 0 – 2021 38 261 447 15 000 000 237 996 – 8 242 874 0 0 15 000 000 0 0 0 – Guinea-Bissau 2019 5 075 144 – – – 0 540 184 296 – – – – – – 2020 20 940 261 – – – 0 – – – – – – – 2021 2 410 409 – – – – – – – – – – – Kenya 2019 35 235 911 36 895 947 – – 6 924 320 14 497 642 – 34 000 000 – – – – 2020 17 075 208 34 892 049 – 253 730 6 230 802 6 48 427 650 0 34 000 000 0 0 0 0 2021 28 871 917 33 500 000 – 253 730 – – – – – – – – Liberia 2019 6 740 547 14 758 379 – – 20 684 910 11 500 991 0 12 000 000 0 0 0 0 2020 9 639 183 14 581 752 – – 20 684 910 5 – – – – – – – 2021 21 895 302 14 000 000 – – 19 859 668 5 – – – – – – – Madagascar 2019 6 746 680 27 408 418 – – 0 18 378 714 – 26 000 000 – 50 000 – – 2020 34 982 363 27 080 396 – – 7 674 17 500 000 0 26 000 000 – 40 000 – – 2021 16 146 415 26 000 000 – – 6 998 30 712 141 – – – 45 200 – – Malawi 2019 15 247 795 25 300 078 – – 334 921 12 768 682 – – – 150 000 – – 2020 41 672 855 24 997 289 – – 292 899 162 082 558 0 24 000 000 – 0 300 000 – 2021 65 321 494 24 000 000 – – 130 047 16 143 765 0 24 000 000 0 0 300 000 0 Mali 2019 22 239 041 26 354 248 11 392 131 – 1 342 820 19 414 667 1 085 642 25 000 000 0 24 083 2 420 7 224 2020 28 102 642 26 038 843 2 082 893 – 6 613 663 9 401 568 3 682 999 25 000 000 – 103 223 4 356 515 5 579 2021 10 908 873 26 500 000 2 082 893 – 6 226 039 16 113 605 0 25 000 000 0 33 766 1 588 792 0 Mauritania 2019 77 186 – – – 131 548 175 296 – – – – – – 2020 513 030 – – – 1 241 048 5 3 172 626 – – – – – – 2021 – – – – – – – – – – – – Mozambique 2019 53 652 975 30 570 928 – – 1 948 730 62 708 218 – 29 000 000 39 548 431 414 944 1 102 707 17 667 110 2020 92 398 106 30 205 057 – – 14 352 922 84 260 635 – 29 000 000 1 102 477 67 741 2 051 725 – 2021 26 216 859 29 000 000 – – 1 470 927 15 473 237 – 29 000 000 1 138 630 – – 2 525 488 Namibia 2019 651 913 – – – 11 725 576 3 377 753 0 0 0 100 000 0 150 000 2020 1 400 924 – – – 10 750 723 1 055 154 – – – 100 000 – 150 000 2021 -3 350 – – – 11 353 994 1 160 669 – – – – – – Niger 2019 22 171 167 18 975 058 8 053 102 – 1 404 583 6 16 329 651 6 319 943 18 000 000 0 86 206 693 054 0 2020 38 684 153 18 747 967 8 231 430 – 2 533 067 42 538 813 5 666 648 18 000 000 – 372 600 382 247 20 000 2021 44 490 085 19 000 000 8 231 430 – 35 781 195 28 962 663 0 18 000 000 0 68 000 0 0 Nigeria 2019 121 528 650 73 791 894 4 121 091 2 658 598 10 313 996 5 131 373 863 – 70 000 000 – – – – 2020 106 146 698 80 199 635 5 413 630 4 365 830 6 354 340 5 116 796 451 – 70 000 000 – – – – 2021 126 299 731 74 000 000 5 413 630 4 365 830 6 100 829 5 11 464 767 – – – – – – Rwanda 2019 36 398 537 18 975 058 – – 30 472 953 18 517 439 – 18 000 000 – – – – 2020 25 008 985 20 831 074 – – 32 139 073 29 647 540 – 18 000 000 – – – – 2021 29 320 994 19 500 000 – – 30 547 621 22 490 626 – 18 000 000 – – – – Sao Tome and Principe 2019 -537 155 0 – – 123 550 517 594 0 0 3 322 449 126 121 52 141 0 2020 0 0 – – 89 918 164 173 – – – 75 939 4 186 0 2021 – 0 – – – – – – – – – – 178 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – C. FUNDING FOR MALARIA CONTROL, 2019–2021 WHO region Country/area Year Contributions reported by donors Contributions reported by countries Global Fund¹ PMI/USAID² World Bank³ United Kingdom4 Government (NMP) Global Fund World Bank PMI/USAID Other bilaterals WHO UNICEF Other contributions7 AFRICAN Gabon 2019 0 – – – 420 789 5 0 – – 1 885 319 – – – 2020 -28 247 – – – 83 574 5 – – – – 2 000 – 6 000 2021 – – – – 314 372 0 0 0 44 064 24 141 15 195 – Gambia 2019 3 596 861 – – – 1 268 631 6 3 940 063 – – – 68 000 90 000 288 646 2020 5 849 843 – – – 961 649 5 – – – – – – – 2021 12 725 069 – – – 1 100 546 15 802 151 – – – – – – Ghana 2019 37 709 188 29 516 758 – 1 541 341 11 350 527 5 28 442 224 0 22 448 510 0 300 000 0 0 2020 37 953 867 29 163 504 – 768 125 11 350 527 5 60 415 856 0 28 000 000 0 300 000 0 0 2021 57 828 600 28 000 000 – 768 125 11 980 643 35 593 618 0 28 000 000 0 300 000 0 0 Guinea 2019 30 552 991 15 812 549 541 430 – 1 002 595 25 261 667 0 15 000 000 0 39 000 0 – 2020 16 545 338 16 664 859 237 996 – 4 584 656 0 0 15 000 000 0 0 0 – 2021 38 261 447 15 000 000 237 996 – 8 242 874 0 0 15 000 000 0 0 0 – Guinea-Bissau 2019 5 075 144 – – – 0 540 184 296 – – – – – – 2020 20 940 261 – – – 0 – – – – – – – 2021 2 410 409 – – – – – – – – – – – Kenya 2019 35 235 911 36 895 947 – – 6 924 320 14 497 642 – 34 000 000 – – – – 2020 17 075 208 34 892 049 – 253 730 6 230 802 6 48 427 650 0 34 000 000 0 0 0 0 2021 28 871 917 33 500 000 – 253 730 – – – – – – – – Liberia 2019 6 740 547 14 758 379 – – 20 684 910 11 500 991 0 12 000 000 0 0 0 0 2020 9 639 183 14 581 752 – – 20 684 910 5 – – – – – – – 2021 21 895 302 14 000 000 – – 19 859 668 5 – – – – – – – Madagascar 2019 6 746 680 27 408 418 – – 0 18 378 714 – 26 000 000 – 50 000 – – 2020 34 982 363 27 080 396 – – 7 674 17 500 000 0 26 000 000 – 40 000 – – 2021 16 146 415 26 000 000 – – 6 998 30 712 141 – – – 45 200 – – Malawi 2019 15 247 795 25 300 078 – – 334 921 12 768 682 – – – 150 000 – – 2020 41 672 855 24 997 289 – – 292 899 162 082 558 0 24 000 000 – 0 300 000 – 2021 65 321 494 24 000 000 – – 130 047 16 143 765 0 24 000 000 0 0 300 000 0 Mali 2019 22 239 041 26 354 248 11 392 131 – 1 342 820 19 414 667 1 085 642 25 000 000 0 24 083 2 420 7 224 2020 28 102 642 26 038 843 2 082 893 – 6 613 663 9 401 568 3 682 999 25 000 000 – 103 223 4 356 515 5 579 2021 10 908 873 26 500 000 2 082 893 – 6 226 039 16 113 605 0 25 000 000 0 33 766 1 588 792 0 Mauritania 2019 77 186 – – – 131 548 175 296 – – – – – – 2020 513 030 – – – 1 241 048 5 3 172 626 – – – – – – 2021 – – – – – – – – – – – – Mozambique 2019 53 652 975 30 570 928 – – 1 948 730 62 708 218 – 29 000 000 39 548 431 414 944 1 102 707 17 667 110 2020 92 398 106 30 205 057 – – 14 352 922 84 260 635 – 29 000 000 1 102 477 67 741 2 051 725 – 2021 26 216 859 29 000 000 – – 1 470 927 15 473 237 – 29 000 000 1 138 630 – – 2 525 488 Namibia 2019 651 913 – – – 11 725 576 3 377 753 0 0 0 100 000 0 150 000 2020 1 400 924 – – – 10 750 723 1 055 154 – – – 100 000 – 150 000 2021 -3 350 – – – 11 353 994 1 160 669 – – – – – – Niger 2019 22 171 167 18 975 058 8 053 102 – 1 404 583 6 16 329 651 6 319 943 18 000 000 0 86 206 693 054 0 2020 38 684 153 18 747 967 8 231 430 – 2 533 067 42 538 813 5 666 648 18 000 000 – 372 600 382 247 20 000 2021 44 490 085 19 000 000 8 231 430 – 35 781 195 28 962 663 0 18 000 000 0 68 000 0 0 Nigeria 2019 121 528 650 73 791 894 4 121 091 2 658 598 10 313 996 5 131 373 863 – 70 000 000 – – – – 2020 106 146 698 80 199 635 5 413 630 4 365 830 6 354 340 5 116 796 451 – 70 000 000 – – – – 2021 126 299 731 74 000 000 5 413 630 4 365 830 6 100 829 5 11 464 767 – – – – – – Rwanda 2019 36 398 537 18 975 058 – – 30 472 953 18 517 439 – 18 000 000 – – – – 2020 25 008 985 20 831 074 – – 32 139 073 29 647 540 – 18 000 000 – – – – 2021 29 320 994 19 500 000 – – 30 547 621 22 490 626 – 18 000 000 – – – – Sao Tome and Principe 2019 -537 155 0 – – 123 550 517 594 0 0 3 322 449 126 121 52 141 0 2020 0 0 – – 89 918 164 173 – – – 75 939 4 186 0 2021 – 0 – – – – – – – – – – 179 ANNEx 4 – C. FUNDING FOR MALARIA CONTROL, 2019–2021 WHO region Country/area Year Contributions reported by donors Contributions reported by countries Global Fund¹ PMI/USAID² World Bank³ United Kingdom4 Government (NMP) Global Fund World Bank PMI/USAID Other bilaterals WHO UNICEF Other contributions7 AFRICAN Senegal 2019 12 198 895 25 300 078 – – 9 930 281 9 005 006 0 24 000 000 0 0 0 14 567 962 2020 10 932 309 23 434 958 – – 41 130 896 11 880 855 0 21 818 182 1 478 320 0 0 6 246 030 2021 943 823 25 500 000 – – 73 322 881 16 233 123 0 22 500 000 0 0 0 37 370 100 Sierra Leone 2019 1 282 546 15 812 549 – – 135 588 7 522 931 – 15 000 000 – 70 000 2 059 4 779 2020 828 004 15 623 306 – – 102 753 5 – – 15 000 000 – – – – 2021 2 434 083 16 000 000 – – 98 654 5 – – 14 500 000 – – – – South Africa 2019 – – – – 20 294 067 6 591 498 0 0 0 45 000 0 1 132 611 2020 – – – 161 901 20 633 960 624 227 0 0 0 0 0 0 2021 – – – 161 901 21 644 538 5 5 504 950 0 0 0 0 0 0 South Sudan8 2019 13 056 781 0 – 12 706 586 1 127 852 17 047 017 3 124 679 0 3 755 637 0 – – 2020 14 541 849 0 – 5 600 052 1 989 688 5 8 131 978 – – – – – – 2021 38 956 168 0 – 5 600 052 1 910 308 5 5 044 295 – – – – – – Togo 2019 9 133 478 – 788 396 – 381 822 – – – – – – – 2020 17 028 218 – – – 2 897 413 – – – – – – – 2021 16 885 154 – – – – – – – – – – – Uganda 2019 41 431 927 34 787 607 – 12 448 819 7 678 069 58 333 000 0 33 000 000 14 389 262 – 1 254 438 705 940 2020 89 670 942 36 454 380 – 6 019 790 7 586 178 76 941 854 0 33 000 000 6 014 987 – – – 2021 63 840 992 34 000 000 – 6 019 790 – – – 33 000 000 – – – – United Republic of Tanzania 2019 57 839 973 46 383 476 – 97 938 78 220 595 27 145 381 0 9 871 122 10 000 57 875 0 0 2020 91 283 184 43 745 255 – – 80 526 713 – 0 1 034 687 0 0 0 10 000 2021 98 361 078 42 000 000 – – 81 266 372 2 569 360 0 1 180 198 0 0 0 6 883 Mainland 2019 – – – – 78 114 720 25 110 093 0 8 774 918 0 57 875 0 0 2020 – – – – 80 444 879 – 0 – 0 0 0 0 2021 – – – – 81 215 121 – 0 – 0 0 0 0 Zanzibar 2019 – – – – 105 875 2 035 288 0 1 096 204 10 000 0 0 0 2020 – – – – 81 834 0 0 1 034 687 0 0 0 10 000 2021 – – – – 51 251 2 569 360 0 1 180 198 0 0 0 6 883 Zambia 2019 25 007 812 31 625 097 367 255 199 103 16 171 488 17 019 922 – 30 000 000 – 300 000 – 5 330 000 2020 42 667 131 31 246 611 540 211 – 15 977 949 47 613 297 – 30 000 000 – 300 000 – – 2021 38 647 166 30 000 000 540 211 – 14 094 553 29 446 626 – 30 000 000 – 250 000 – – Zimbabwe 2019 18 240 345 15 812 549 – – 3 969 213 25 931 599 – 11 208 498 – 140 000 – – 2020 33 410 312 15 623 306 – – 1 856 205 12 796 329 – 12 000 000 – – – – 2021 49 085 307 15 000 000 – – 1 750 520 42 425 446 – 11 208 490 – – – – AMERICAS Belize 2019 – – – – 265 651 0 0 11 058 0 0 0 0 2020 – – – – 253 993 20 554 0 0 0 0 0 0 2021 – – – – 209 639 41 109 – – – – – – Bolivia (Plurinational State of) 2019 867 337 – – – 308 716 1 191 940 0 0 0 27 891 0 0 2020 1 928 329 – – – 156 233 1 269 187 – – – – – – 2021 1 316 081 – – – 336 667 – – – – – – – Brazil 2019 0 – – – 58 448 223 6 0 0 154 641 – – – – 2020 – – – – 55 558 301 6 – – 13 000 – – – – 2021 – – – – 41 066 648 6 – – 40 000 – – – – Colombia 2019 0 – – – 6 324 464 0 0 269 661 0 – 0 – 2020 – – – – 5 550 166 0 0 – 0 – 0 1 066 811 2021 – – – – 2 036 333 – – 84 662 – – – 795 345 Costa Rica 2019 – – – – 5 270 850 6 0 0 7 991 0 22 842 0 0 2020 – – – – 208 687 0 0 0 0 56 000 0 8 000 2021 – – – – 106 406 0 0 83 093 0 14 000 0 24 577 180 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – C. FUNDING FOR MALARIA CONTROL, 2019–2021 WHO region Country/area Year Contributions reported by donors Contributions reported by countries Global Fund¹ PMI/USAID² World Bank³ United Kingdom4 Government (NMP) Global Fund World Bank PMI/USAID Other bilaterals WHO UNICEF Other contributions7 AFRICAN Senegal 2019 12 198 895 25 300 078 – – 9 930 281 9 005 006 0 24 000 000 0 0 0 14 567 962 2020 10 932 309 23 434 958 – – 41 130 896 11 880 855 0 21 818 182 1 478 320 0 0 6 246 030 2021 943 823 25 500 000 – – 73 322 881 16 233 123 0 22 500 000 0 0 0 37 370 100 Sierra Leone 2019 1 282 546 15 812 549 – – 135 588 7 522 931 – 15 000 000 – 70 000 2 059 4 779 2020 828 004 15 623 306 – – 102 753 5 – – 15 000 000 – – – – 2021 2 434 083 16 000 000 – – 98 654 5 – – 14 500 000 – – – – South Africa 2019 – – – – 20 294 067 6 591 498 0 0 0 45 000 0 1 132 611 2020 – – – 161 901 20 633 960 624 227 0 0 0 0 0 0 2021 – – – 161 901 21 644 538 5 5 504 950 0 0 0 0 0 0 South Sudan8 2019 13 056 781 0 – 12 706 586 1 127 852 17 047 017 3 124 679 0 3 755 637 0 – – 2020 14 541 849 0 – 5 600 052 1 989 688 5 8 131 978 – – – – – – 2021 38 956 168 0 – 5 600 052 1 910 308 5 5 044 295 – – – – – – Togo 2019 9 133 478 – 788 396 – 381 822 – – – – – – – 2020 17 028 218 – – – 2 897 413 – – – – – – – 2021 16 885 154 – – – – – – – – – – – Uganda 2019 41 431 927 34 787 607 – 12 448 819 7 678 069 58 333 000 0 33 000 000 14 389 262 – 1 254 438 705 940 2020 89 670 942 36 454 380 – 6 019 790 7 586 178 76 941 854 0 33 000 000 6 014 987 – – – 2021 63 840 992 34 000 000 – 6 019 790 – – – 33 000 000 – – – – United Republic of Tanzania 2019 57 839 973 46 383 476 – 97 938 78 220 595 27 145 381 0 9 871 122 10 000 57 875 0 0 2020 91 283 184 43 745 255 – – 80 526 713 – 0 1 034 687 0 0 0 10 000 2021 98 361 078 42 000 000 – – 81 266 372 2 569 360 0 1 180 198 0 0 0 6 883 Mainland 2019 – – – – 78 114 720 25 110 093 0 8 774 918 0 57 875 0 0 2020 – – – – 80 444 879 – 0 – 0 0 0 0 2021 – – – – 81 215 121 – 0 – 0 0 0 0 Zanzibar 2019 – – – – 105 875 2 035 288 0 1 096 204 10 000 0 0 0 2020 – – – – 81 834 0 0 1 034 687 0 0 0 10 000 2021 – – – – 51 251 2 569 360 0 1 180 198 0 0 0 6 883 Zambia 2019 25 007 812 31 625 097 367 255 199 103 16 171 488 17 019 922 – 30 000 000 – 300 000 – 5 330 000 2020 42 667 131 31 246 611 540 211 – 15 977 949 47 613 297 – 30 000 000 – 300 000 – – 2021 38 647 166 30 000 000 540 211 – 14 094 553 29 446 626 – 30 000 000 – 250 000 – – Zimbabwe 2019 18 240 345 15 812 549 – – 3 969 213 25 931 599 – 11 208 498 – 140 000 – – 2020 33 410 312 15 623 306 – – 1 856 205 12 796 329 – 12 000 000 – – – – 2021 49 085 307 15 000 000 – – 1 750 520 42 425 446 – 11 208 490 – – – – AMERICAS Belize 2019 – – – – 265 651 0 0 11 058 0 0 0 0 2020 – – – – 253 993 20 554 0 0 0 0 0 0 2021 – – – – 209 639 41 109 – – – – – – Bolivia (Plurinational State of) 2019 867 337 – – – 308 716 1 191 940 0 0 0 27 891 0 0 2020 1 928 329 – – – 156 233 1 269 187 – – – – – – 2021 1 316 081 – – – 336 667 – – – – – – – Brazil 2019 0 – – – 58 448 223 6 0 0 154 641 – – – – 2020 – – – – 55 558 301 6 – – 13 000 – – – – 2021 – – – – 41 066 648 6 – – 40 000 – – – – Colombia 2019 0 – – – 6 324 464 0 0 269 661 0 – 0 – 2020 – – – – 5 550 166 0 0 – 0 – 0 1 066 811 2021 – – – – 2 036 333 – – 84 662 – – – 795 345 Costa Rica 2019 – – – – 5 270 850 6 0 0 7 991 0 22 842 0 0 2020 – – – – 208 687 0 0 0 0 56 000 0 8 000 2021 – – – – 106 406 0 0 83 093 0 14 000 0 24 577 181 ANNEx 4 – C. FUNDING FOR MALARIA CONTROL, 2019–2021 WHO region Country/area Year Contributions reported by donors Contributions reported by countries Global Fund¹ PMI/USAID² World Bank³ United Kingdom4 Government (NMP) Global Fund World Bank PMI/USAID Other bilaterals WHO UNICEF Other contributions7 AMERICAS Dominican Republic 2019 0 – – – 2 699 469 0 0 313 661 0 322 922 0 186 078 2020 – – – – 1 254 339 0 0 0 0 10 440 0 415 233 2021 – – – – 919 887 – – 67 380 – 10 000 – 621 664 Ecuador 2019 0 – – – 2 820 454 6 0 0 71 420 0 76 400 0 0 2020 – – – – 1 153 024 0 0 40 000 0 31 000 – 33 000 2021 – – – – – – – 68 178 – 55 000 – – French Guiana 2019 – – – – – 0 0 0 0 – 0 – 2020 – – – – – 0 0 0 0 – 0 – 2021 – – – – – 0 0 0 0 16 340 0 – Guatemala 2019 653 275 – – – 1 347 222 520 837 – 76 014 – 110 535 – – 2020 2 941 532 – – – 3 300 192 2 984 711 – – – 11 122 – 1 025 373 2021 1 454 733 – – – 3 433 146 – – – – – – 236 936 Guyana 2019 79 794 – – – 771 827 299 843 0 1 000 000 0 140 000 0 0 2020 311 162 – – – 637 049 421 050 0 28 415 0 0 0 0 2021 1 508 692 – – – 8 830 132 6 485 999 – 901 000 – – – 207 741 Haiti 2019 6 365 257 – – – 2 408 523 6 6 006 513 0 10 445 0 266 004 – 203 638 2020 8 032 623 – – – 0 – 0 131 147 0 75 612 – 123 742 2021 7 984 314 – – – – 3 590 047 – 144 000 – – – – Honduras 2019 1 628 562 – – – 572 743 1 511 759 0 67 612 595 460 2 613 0 621 496 2020 2 016 988 – – – 565 889 926 108 0 0 0 45 451 0 – 2021 683 331 – – – 543 312 1 438 564 – – – 100 000 – 1 903 289 Mexico 2019 – – – – 39 029 833 0 0 41 177 0 59 429 0 0 2020 – – – – 8 449 764 0 0 0 0 0 0 0 2021 – – – – 7 465 765 0 0 0 0 20 100 0 0 Nicaragua 2019 3 135 894 – – – 6 487 924 2 313 411 – 100 400 000 13 408 – 15 020 2020 1 665 930 – – – 7 193 840 1 607 911 – – – 15 235 – 444 514 2021 1 681 870 – – – 7 659 140 1 722 062 – – – – – 1 474 595 Panama 2019 – – – – 6 729 161 475 156 0 32 085 668 596 62 342 0 79 896 2020 – – – – 6 041 011 418 786 0 9 058 0 – 0 44 949 2021 – – – – 6 236 013 198 000 0 20 930 0 99 636 0 179 472 Peru 2019 – – – – 3 912 630 6 0 0 193 079 0 – 0 0 2020 – – – – 15 237 676 – – 51 143 – – – – 2021 – – – – 21 730 670 – – 35 661 – – – – Suriname 2019 690 835 – – – 1 356 092 695 291 – 46 808 – 5 000 – 30 000 2020 951 828 – – – 1 533 114 849 957 – – 15 000 – – 65 000 2021 975 225 – – – 1 471 949 849 957 – 76 565 15 000 – – 63 835 Venezuela (Bolivarian Republic of) 2019 – – – – 0 – – – – 147 419 – – 2020 13 250 391 – – – 0 – – – – 39 384 – – 2021 12 515 489 – – – – – – – – – – – EASTERN MEDITERRANEAN Afghanistan 2019 10 751 612 – – – 0 7 759 216 – – – 80 885 – – 2020 6 501 085 – – – 0 11 733 984 – – – 19 367 – – 2021 9 312 416 – – – – 12 150 115 – – – 46 378 – – Djibouti 2019 1 112 797 – 27 103 – 1 536 115 6 171 627 – – – 406 776 – – 2020 1 130 197 – 240 660 – 507 285 6 – 0 0 0 0 0 – 2021 – – 240 660 – 1 545 223 6 1 349 614 0 – 0 153 658 0 – Iran (Islamic Republic of) 2019 -110 959 – – – 3 088 718 0 0 0 0 38 000 0 0 2020 0 – – – 2 812 195 0 0 0 0 156 373 – – 2021 – – – – 112 000 0 0 0 0 923 878 0 85 000 182 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – C. FUNDING FOR MALARIA CONTROL, 2019–2021 WHO region Country/area Year Contributions reported by donors Contributions reported by countries Global Fund¹ PMI/USAID² World Bank³ United Kingdom4 Government (NMP) Global Fund World Bank PMI/USAID Other bilaterals WHO UNICEF Other contributions7 AMERICAS Dominican Republic 2019 0 – – – 2 699 469 0 0 313 661 0 322 922 0 186 078 2020 – – – – 1 254 339 0 0 0 0 10 440 0 415 233 2021 – – – – 919 887 – – 67 380 – 10 000 – 621 664 Ecuador 2019 0 – – – 2 820 454 6 0 0 71 420 0 76 400 0 0 2020 – – – – 1 153 024 0 0 40 000 0 31 000 – 33 000 2021 – – – – – – – 68 178 – 55 000 – – French Guiana 2019 – – – – – 0 0 0 0 – 0 – 2020 – – – – – 0 0 0 0 – 0 – 2021 – – – – – 0 0 0 0 16 340 0 – Guatemala 2019 653 275 – – – 1 347 222 520 837 – 76 014 – 110 535 – – 2020 2 941 532 – – – 3 300 192 2 984 711 – – – 11 122 – 1 025 373 2021 1 454 733 – – – 3 433 146 – – – – – – 236 936 Guyana 2019 79 794 – – – 771 827 299 843 0 1 000 000 0 140 000 0 0 2020 311 162 – – – 637 049 421 050 0 28 415 0 0 0 0 2021 1 508 692 – – – 8 830 132 6 485 999 – 901 000 – – – 207 741 Haiti 2019 6 365 257 – – – 2 408 523 6 6 006 513 0 10 445 0 266 004 – 203 638 2020 8 032 623 – – – 0 – 0 131 147 0 75 612 – 123 742 2021 7 984 314 – – – – 3 590 047 – 144 000 – – – – Honduras 2019 1 628 562 – – – 572 743 1 511 759 0 67 612 595 460 2 613 0 621 496 2020 2 016 988 – – – 565 889 926 108 0 0 0 45 451 0 – 2021 683 331 – – – 543 312 1 438 564 – – – 100 000 – 1 903 289 Mexico 2019 – – – – 39 029 833 0 0 41 177 0 59 429 0 0 2020 – – – – 8 449 764 0 0 0 0 0 0 0 2021 – – – – 7 465 765 0 0 0 0 20 100 0 0 Nicaragua 2019 3 135 894 – – – 6 487 924 2 313 411 – 100 400 000 13 408 – 15 020 2020 1 665 930 – – – 7 193 840 1 607 911 – – – 15 235 – 444 514 2021 1 681 870 – – – 7 659 140 1 722 062 – – – – – 1 474 595 Panama 2019 – – – – 6 729 161 475 156 0 32 085 668 596 62 342 0 79 896 2020 – – – – 6 041 011 418 786 0 9 058 0 – 0 44 949 2021 – – – – 6 236 013 198 000 0 20 930 0 99 636 0 179 472 Peru 2019 – – – – 3 912 630 6 0 0 193 079 0 – 0 0 2020 – – – – 15 237 676 – – 51 143 – – – – 2021 – – – – 21 730 670 – – 35 661 – – – – Suriname 2019 690 835 – – – 1 356 092 695 291 – 46 808 – 5 000 – 30 000 2020 951 828 – – – 1 533 114 849 957 – – 15 000 – – 65 000 2021 975 225 – – – 1 471 949 849 957 – 76 565 15 000 – – 63 835 Venezuela (Bolivarian Republic of) 2019 – – – – 0 – – – – 147 419 – – 2020 13 250 391 – – – 0 – – – – 39 384 – – 2021 12 515 489 – – – – – – – – – – – EASTERN MEDITERRANEAN Afghanistan 2019 10 751 612 – – – 0 7 759 216 – – – 80 885 – – 2020 6 501 085 – – – 0 11 733 984 – – – 19 367 – – 2021 9 312 416 – – – – 12 150 115 – – – 46 378 – – Djibouti 2019 1 112 797 – 27 103 – 1 536 115 6 171 627 – – – 406 776 – – 2020 1 130 197 – 240 660 – 507 285 6 – 0 0 0 0 0 – 2021 – – 240 660 – 1 545 223 6 1 349 614 0 – 0 153 658 0 – Iran (Islamic Republic of) 2019 -110 959 – – – 3 088 718 0 0 0 0 38 000 0 0 2020 0 – – – 2 812 195 0 0 0 0 156 373 – – 2021 – – – – 112 000 0 0 0 0 923 878 0 85 000 183 ANNEx 4 – C. FUNDING FOR MALARIA CONTROL, 2019–2021 WHO region Country/area Year Contributions reported by donors Contributions reported by countries Global Fund¹ PMI/USAID² World Bank³ United Kingdom4 Government (NMP) Global Fund World Bank PMI/USAID Other bilaterals WHO UNICEF Other contributions7 EASTERN MEDITERRANEAN Pakistan 2019 15 689 389 – – – 2 575 963 14 600 000 – – – 296 000 – – 2020 12 419 596 – – – 3 337 764 11 858 304 – – – 149 566 – – 2021 13 354 832 – – – 3 384 559 13 601 348 0 0 0 – 0 0 Saudi Arabia 2019 – – – – 31 625 097 0 0 0 0 0 0 0 2020 – – – – 31 246 611 0 0 0 0 0 0 0 2021 – – – – 30 000 000 0 0 0 0 0 0 0 Somalia 2019 4 476 727 – – – 126 606 9 474 797 0 0 0 73 840 0 0 2020 10 720 350 – – – 168 872 9 515 651 0 0 0 12 450 0 – 2021 13 499 724 – – – 243 202 21 735 290 0 0 0 28 680 0 0 Sudan 2019 46 691 035 0 – – 0 – – – – – – – 2020 49 457 120 0 – – 0 51 917 466 – – – 50 000 – – 2021 72 118 821 0 – – 6 976 922 12 748 399 – – – 1 249 098 – – Yemen 2019 -59 460 – 18 722 777 – 0 6 123 238 – – – – – – 2020 0 – 4 374 526 – 0 7 203 048 – – – – – – 2021 – – 4 374 526 – – 2 162 232 – – – – – – SOUTH‑EAST ASIA Bangladesh 2019 5 698 899 – – – 2 777 488 7 082 673 0 0 0 100 000 0 0 2020 14 163 659 – – – 2 570 721 6 15 561 791 0 0 0 44 600 0 0 2021 6 724 656 – – – 2 468 160 5 22 847 944 146 0 0 78 781 0 0 Bhutan 2019 404 334 – – – 265 503 418 069 0 0 0 40 391 0 121 212 2020 1 365 552 – – – 180 189 530 814 0 0 0 31 728 0 114 285 2021 461 051 – – – 131 396 397 061 – – – – – 94 108 Democratic People’s Republic of Korea 2019 0 – – – 2 330 875 0 0 0 0 700 000 0 0 2020 -937 319 – – – 2 333 393 0 0 0 0 – 434 830 – 2021 – – – – 2 269 000 513 556 – – – – – – India 2019 23 239 183 – – – 77 632 329 31 242 857 0 0 0 – 0 – 2020 17 887 121 – – – 65 819 304 22 618 171 0 0 0 – 0 – 2021 26 086 463 – – – 63 193 385 5 35 570 992 – – – – – – Indonesia 2019 18 437 183 – – – 25 926 315 6 25 652 637 – – – 100 000 782 076 – 2020 14 638 310 – – – 17 800 386 6 21 448 055 – – – 100 000 234 343 12 687 804 2021 11 689 248 – – – 17 090 224 5 14 063 928 – – – 100 000 234 343 – Myanmar 2019 31 025 213 10 541 699 – 565 217 11 726 459 6 40 110 516 – 10 000 000 610 000 50 000 – – 2020 33 283 346 10 415 537 – 369 356 13 903 341 6 28 727 247 – 10 000 000 3 367 484 50 000 – – 2021 – 10 000 000 – 369 356 13 348 655 5 – – – – 5 000 – – Nepal 2019 1 608 903 – – – 647 126 2 727 909 0 621 652 0 40 000 0 0 2020 1 689 666 – – – 3 713 285 1 862 647 0 0 0 40 000 0 0 2021 1 275 647 – – – 4 461 471 1 485 187 0 0 0 40 000 0 0 Thailand 2019 12 148 078 3 162 510 – – 6 096 378 8 872 808 – 1 047 408 – 70 000 – 37 710 2020 12 943 523 – – – 6 013 838 8 247 913 0 885 845 0 87 663 0 27 514 2021 – – – – 3 666 021 8 379 275 – 1 156 640 – 15 362 – 347 033 Timor-Leste 2019 2 431 858 0 – – 2 405 053 5 2 281 466 – – – 40 000 – 256 000 2020 3 117 505 0 – – 3 171 788 – – – – 60 000 – 21 340 2021 1 115 703 0 – – 3 045 247 5 – – – – 40 000 – 10 414 184 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – C. FUNDING FOR MALARIA CONTROL, 2019–2021 WHO region Country/area Year Contributions reported by donors Contributions reported by countries Global Fund¹ PMI/USAID² World Bank³ United Kingdom4 Government (NMP) Global Fund World Bank PMI/USAID Other bilaterals WHO UNICEF Other contributions7 EASTERN MEDITERRANEAN Pakistan 2019 15 689 389 – – – 2 575 963 14 600 000 – – – 296 000 – – 2020 12 419 596 – – – 3 337 764 11 858 304 – – – 149 566 – – 2021 13 354 832 – – – 3 384 559 13 601 348 0 0 0 – 0 0 Saudi Arabia 2019 – – – – 31 625 097 0 0 0 0 0 0 0 2020 – – – – 31 246 611 0 0 0 0 0 0 0 2021 – – – – 30 000 000 0 0 0 0 0 0 0 Somalia 2019 4 476 727 – – – 126 606 9 474 797 0 0 0 73 840 0 0 2020 10 720 350 – – – 168 872 9 515 651 0 0 0 12 450 0 – 2021 13 499 724 – – – 243 202 21 735 290 0 0 0 28 680 0 0 Sudan 2019 46 691 035 0 – – 0 – – – – – – – 2020 49 457 120 0 – – 0 51 917 466 – – – 50 000 – – 2021 72 118 821 0 – – 6 976 922 12 748 399 – – – 1 249 098 – – Yemen 2019 -59 460 – 18 722 777 – 0 6 123 238 – – – – – – 2020 0 – 4 374 526 – 0 7 203 048 – – – – – – 2021 – – 4 374 526 – – 2 162 232 – – – – – – SOUTH‑EAST ASIA Bangladesh 2019 5 698 899 – – – 2 777 488 7 082 673 0 0 0 100 000 0 0 2020 14 163 659 – – – 2 570 721 6 15 561 791 0 0 0 44 600 0 0 2021 6 724 656 – – – 2 468 160 5 22 847 944 146 0 0 78 781 0 0 Bhutan 2019 404 334 – – – 265 503 418 069 0 0 0 40 391 0 121 212 2020 1 365 552 – – – 180 189 530 814 0 0 0 31 728 0 114 285 2021 461 051 – – – 131 396 397 061 – – – – – 94 108 Democratic People’s Republic of Korea 2019 0 – – – 2 330 875 0 0 0 0 700 000 0 0 2020 -937 319 – – – 2 333 393 0 0 0 0 – 434 830 – 2021 – – – – 2 269 000 513 556 – – – – – – India 2019 23 239 183 – – – 77 632 329 31 242 857 0 0 0 – 0 – 2020 17 887 121 – – – 65 819 304 22 618 171 0 0 0 – 0 – 2021 26 086 463 – – – 63 193 385 5 35 570 992 – – – – – – Indonesia 2019 18 437 183 – – – 25 926 315 6 25 652 637 – – – 100 000 782 076 – 2020 14 638 310 – – – 17 800 386 6 21 448 055 – – – 100 000 234 343 12 687 804 2021 11 689 248 – – – 17 090 224 5 14 063 928 – – – 100 000 234 343 – Myanmar 2019 31 025 213 10 541 699 – 565 217 11 726 459 6 40 110 516 – 10 000 000 610 000 50 000 – – 2020 33 283 346 10 415 537 – 369 356 13 903 341 6 28 727 247 – 10 000 000 3 367 484 50 000 – – 2021 – 10 000 000 – 369 356 13 348 655 5 – – – – 5 000 – – Nepal 2019 1 608 903 – – – 647 126 2 727 909 0 621 652 0 40 000 0 0 2020 1 689 666 – – – 3 713 285 1 862 647 0 0 0 40 000 0 0 2021 1 275 647 – – – 4 461 471 1 485 187 0 0 0 40 000 0 0 Thailand 2019 12 148 078 3 162 510 – – 6 096 378 8 872 808 – 1 047 408 – 70 000 – 37 710 2020 12 943 523 – – – 6 013 838 8 247 913 0 885 845 0 87 663 0 27 514 2021 – – – – 3 666 021 8 379 275 – 1 156 640 – 15 362 – 347 033 Timor-Leste 2019 2 431 858 0 – – 2 405 053 5 2 281 466 – – – 40 000 – 256 000 2020 3 117 505 0 – – 3 171 788 – – – – 60 000 – 21 340 2021 1 115 703 0 – – 3 045 247 5 – – – – 40 000 – 10 414 185 ANNEx 4 – C. FUNDING FOR MALARIA CONTROL, 2019–2021 WHO region Country/area Year Contributions reported by donors Contributions reported by countries Global Fund¹ PMI/USAID² World Bank³ United Kingdom4 Government (NMP) Global Fund World Bank PMI/USAID Other bilaterals WHO UNICEF Other contributions7 WESTERN PACIFIC Cambodia 2019 19 089 838 10 541 699 – – 957 972 5 108 350 – 8 714 000 420 000 – – 0 2020 20 339 822 10 415 537 – – 979 633 4 690 075 – 8 649 000 520 000 – – 5 633 2021 – 10 000 000 – – 930 575 5 000 100 – 8 034 662 520 000 246 523 – 0 Lao People’s Democratic Republic 2019 6 485 880 – – – 979 276 5 327 000 0 686 183 0 1 039 774 0 1 301 618 2020 7 390 821 – – – 500 779 5 157 000 0 903 988 0 – 0 551 020 2021 – – – – 1 645 898 3 718 049 0 859 788 0 711 542 0 1 278 402 Malaysia 2019 – – – – 51 461 892 0 0 0 0 0 0 0 2020 – – – – 50 249 077 0 0 0 0 0 0 0 2021 – – – – 48 770 844 0 0 0 0 0 0 0 Papua New Guinea 2019 10 755 826 – – – 51 233 8 831 155 – – 1 474 700 95 000 – – 2020 9 892 594 – – – 52 078 94 632 334 – – – 52 000 – – 2021 14 973 712 – – – 55 500 11 996 849 – – – 45 000 – – Philippines 2019 3 228 103 – – – 2 586 685 3 412 622 0 0 0 0 0 0 2020 4 651 619 – – – 5 102 712 5 150 000 0 0 0 0 0 0 2021 1 855 744 – – – 4 392 228 3 918 641 – – – – – – Republic of Korea 2019 – – – – 758 994 0 0 0 0 0 0 0 2020 – – – – 840 265 0 0 0 0 0 0 0 2021 – – – – 850 719 0 0 0 0 0 0 0 Solomon Islands 2019 2 065 385 – – – 316 166 717 728 0 0 455 000 37 607 0 0 2020 539 463 – – – 116 143 121 522 – – 578 144 23 400 – – 2021 2 171 036 – – – 276 950 1 227 347 – – 13 600 14 025 – – Vanuatu 2019 – – – – 191 174 182 877 0 0 0 178 245 0 0 2020 – – – – 109 890 218 935 – – – 166 293 – – 2021 – – – – 17 400 6 329 022 0 0 0 – 0 – Viet Nam 2019 17 354 398 – – – 1 708 089 10 221 830 0 0 0 333 000 0 385 000 2020 18 490 748 – – – 2 018 602 9 366 317 – – – – – 858 369 2021 – – – – 1 542 014 2 418 471 – – – – – 755 652 Global Fund: Global Fund to Fight AIDS, Tuberculosis and Malaria; NGO: nongovernmental organization; NMP: national malaria programme; PMI: United States President’s Malaria Initiative; United Kingdom: United Kingdom of Great Britain and Northern Ireland government; UNICEF: United Nations Children’s Fund; USAID: United States Agency for International Development; WHO: World Health Organization. “–” refers to data not available. 1 Source: Global Fund. 2 Source: www.foreignassistance.gov. 3 Source: Organisation for Economic Co-operation and Development (OECD) creditor reporting system (CRS) database. 4 Source: Final UK aid spend. 5 WHO NMP funding estimates. Data as of 24 November 2022 6 Budget not expenditure. 7 Other contributions as reported by countries: NGOs, foundations, etc. 8 South Sudan became an independent state on 9 July 2011 and a Member State of WHO on 27 September 2011. South Sudan and the Sudan have distinct epidemiological profiles comprising high transmission and low transmission areas, respectively. For this reason, data up to June 2011 from the Sudanese high transmission areas (10 southern states which correspond to contemporary South Sudan) and low transmission areas (15 northern states which correspond to contemporary Sudan) are reported separately. Note: Negative disbursements reflect recovery of funds on behalf of the financing organization. Note: All contributions reported by donors are displayed in US 2021 constant dollars. 186 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – C. FUNDING FOR MALARIA CONTROL, 2019–2021 WHO region Country/area Year Contributions reported by donors Contributions reported by countries Global Fund¹ PMI/USAID² World Bank³ United Kingdom4 Government (NMP) Global Fund World Bank PMI/USAID Other bilaterals WHO UNICEF Other contributions7 WESTERN PACIFIC Cambodia 2019 19 089 838 10 541 699 – – 957 972 5 108 350 – 8 714 000 420 000 – – 0 2020 20 339 822 10 415 537 – – 979 633 4 690 075 – 8 649 000 520 000 – – 5 633 2021 – 10 000 000 – – 930 575 5 000 100 – 8 034 662 520 000 246 523 – 0 Lao People’s Democratic Republic 2019 6 485 880 – – – 979 276 5 327 000 0 686 183 0 1 039 774 0 1 301 618 2020 7 390 821 – – – 500 779 5 157 000 0 903 988 0 – 0 551 020 2021 – – – – 1 645 898 3 718 049 0 859 788 0 711 542 0 1 278 402 Malaysia 2019 – – – – 51 461 892 0 0 0 0 0 0 0 2020 – – – – 50 249 077 0 0 0 0 0 0 0 2021 – – – – 48 770 844 0 0 0 0 0 0 0 Papua New Guinea 2019 10 755 826 – – – 51 233 8 831 155 – – 1 474 700 95 000 – – 2020 9 892 594 – – – 52 078 94 632 334 – – – 52 000 – – 2021 14 973 712 – – – 55 500 11 996 849 – – – 45 000 – – Philippines 2019 3 228 103 – – – 2 586 685 3 412 622 0 0 0 0 0 0 2020 4 651 619 – – – 5 102 712 5 150 000 0 0 0 0 0 0 2021 1 855 744 – – – 4 392 228 3 918 641 – – – – – – Republic of Korea 2019 – – – – 758 994 0 0 0 0 0 0 0 2020 – – – – 840 265 0 0 0 0 0 0 0 2021 – – – – 850 719 0 0 0 0 0 0 0 Solomon Islands 2019 2 065 385 – – – 316 166 717 728 0 0 455 000 37 607 0 0 2020 539 463 – – – 116 143 121 522 – – 578 144 23 400 – – 2021 2 171 036 – – – 276 950 1 227 347 – – 13 600 14 025 – – Vanuatu 2019 – – – – 191 174 182 877 0 0 0 178 245 0 0 2020 – – – – 109 890 218 935 – – – 166 293 – – 2021 – – – – 17 400 6 329 022 0 0 0 – 0 – Viet Nam 2019 17 354 398 – – – 1 708 089 10 221 830 0 0 0 333 000 0 385 000 2020 18 490 748 – – – 2 018 602 9 366 317 – – – – – 858 369 2021 – – – – 1 542 014 2 418 471 – – – – – 755 652 Global Fund: Global Fund to Fight AIDS, Tuberculosis and Malaria; NGO: nongovernmental organization; NMP: national malaria programme; PMI: United States President’s Malaria Initiative; United Kingdom: United Kingdom of Great Britain and Northern Ireland government; UNICEF: United Nations Children’s Fund; USAID: United States Agency for International Development; WHO: World Health Organization. “–” refers to data not available. 1 Source: Global Fund. 2 Source: www.foreignassistance.gov. 3 Source: Organisation for Economic Co-operation and Development (OECD) creditor reporting system (CRS) database. 4 Source: Final UK aid spend. 5 WHO NMP funding estimates. Data as of 24 November 2022 6 Budget not expenditure. 7 Other contributions as reported by countries: NGOs, foundations, etc. 8 South Sudan became an independent state on 9 July 2011 and a Member State of WHO on 27 September 2011. South Sudan and the Sudan have distinct epidemiological profiles comprising high transmission and low transmission areas, respectively. For this reason, data up to June 2011 from the Sudanese high transmission areas (10 southern states which correspond to contemporary South Sudan) and low transmission areas (15 northern states which correspond to contemporary Sudan) are reported separately. Note: Negative disbursements reflect recovery of funds on behalf of the financing organization. Note: All contributions reported by donors are displayed in US 2021 constant dollars. 187 ANNEx 4 – D. COMMODITIES DISTRIBUTION AND COVERAGE, 2019–2021 WHO region Country/area Year No. of LLINs delivered Modelled percentage of population with access to an ITN No. of people protected by IRS No. of RDTs distributed Any first-line treatment courses delivered (including ACT) No. of malaria cases treated with any first-line treatment courses (including ACT) ACT treatment courses delivered No. of malaria cases treated with ACT AFRICAN Angola 2019 15 293 686 27.5 – – 2 477 124 5 575 259 2 477 124 5 575 259 2020 528 563 15.5 44 633 7 570 498 2 575 738* 5 797 209 2 575 738+ 5 797 209 2021 875 247 16.8 620 815 6 481 900 4 215 061 7 793 251 4 215 061 7 793 251 Benin 2019 505 670 29.9 1 077 411 3 984 677 4 455 581 2 353 657 4 455 581 2 353 657 2020 13 103 447 68.5 1 104 928 4 202 384 3 966 505* 2 095 303 3 966 505+ 2 095 303 2021 662 290 64.0 927 007 2 298 798 2 985 960 2 985 960 2 985 960 2 985 960 Botswana 2019 19 932 – 154 663 2 526 3 198 272 3 198 272 2020 80 525 – 152 560 – 11 205* 953 11 205+ 953 2021 – – 24 620 – – – – – Burkina Faso 2019 1 145 782 66.5 587 248 – 12 428 300 11 223 002 12 428 300 11 223 002 2020 1 017 084 69.4 508 017 12 936 865 11 336 876* 10 237 424 11 336 876+ 10 237 424 2021 1 353 233 52.0 591 249 9 794 711 8 892 174 11 030 942 8 892 174 11 030 942 Burundi 2019 7 528 556 50.0 725 449 – 9 338 611 8 444 710 9 271 032 8 444 710 2020 729 431 65.2 1 243 848 7 773 268 4 743 324* 4 289 288 4 708 998+ 4 289 288 2021 802 309 66.9 914 778 9 563 340 11 404 672 5 953 811 5 693 645 5 693 645 Cabo Verde 2019 – – 302 520 7 867 40 40 40 40 2020 – – 233 171 4 399 10 10 10# 10 2021 – – 241 552 – 21 21 21 21 Cameroon 2019 8 860 653 69.3 – 2 082 527 1 905 965 1 834 114 1 905 965 1 157 011 2020 2 270 567 73.5 – 2 840 269 1 849 716$ 1 433 934 1 849 716+ 1 122 865 2021 1 024 236 68.4 – 2 027 275 1 816 440 1 439 118 1 816 440 1 359 417 Central African Republic 2019 103 848 74.7 – 2 764 293 5 753 501 2 654 215 5 640 687 2 602 171 2020 2 635 388 69.1 – – 4 293 758* 1 980 804 3 773 875+ 1 740 970 2021 121 607 62.0 – 2 736 457 3 753 972 2 223 562 3 753 972 2 223 562 Chad 2019 613 700 19.4 – 1 788 730 1 665 212 1 665 212 1 595 351 1 595 351 2020 9 138 032 50.2 1 707 286 2 340 650 1 806 225ˆ 1 806 225 1 452 420# 1 452 420 2021 731 254 59.8 613 037 – 1 012 958 1 012 958 1 012 958 1 012 958 Comoros 2019 16 244 47.3 – – 11 593 11 593 11 593 11 593 2020 462 154 71.6 57 658 – 4 546$ 4 546 4 546# 4 546 2021 30 891 78.5 127 487 31 467 10 547 9 609 10 547 9 609 Congo 2019 2 648 456 74.8 – – 200 000 427 959 200 000 233 389 2020 1 488 86.3 – – 48 459 103 692 48 459# 103 692 2021 36 873 76.0 – – 187 940 161 693 187 940 161 693 Côte d’Ivoire 2019 1 410 391 60.6 – 6 456 625 4 657 570 5 200 350 4 657 570 5 200 350 2020 1 579 505 54.9 193 935 4 837 781 4 365 387 4 469 333 4 365 387 4 365 387 2021 21 733 998 81.2 – 7 338 750 7 073 535 6 422 581 7 073 535 6 234 917 Democratic Republic of the Congo 2019 20 710 146 65.8 – 26 963 687 18 853 210 18 853 210 18 853 209 18 853 209 2020 20 620 187 65.8 – 28 054 832 19 192 708 19 192 708 19 192 707# 19 192 707 2021 22 579 391 67.5 – 26 740 915 19 260 604 18 535 664 19 260 604 18 535 664 Equatorial Guinea 2019 14 843 47.4 61 561 54 340 15 769 – 15 769 – 2020 – 42.5 – – – – – – 2021 – 36.8 – – – – – – Eritrea 2019 124 225 55.8 437 194 388 395 207 150 65 697 207 150 65 697 2020 621 094 48.3 466 238 505 675 118 350 73 419 118 350+ 73 419 2021 69 347 41.8 444 318 437 525 89 680 42 056 89 680 42 056 Eswatini 2019 – – 15 066 72 369 586 586 516 580 2020 – – 53 517 104 325 279 316 270 316 2021 – – 67 346 52 400 484 606 474 606 Ethiopia 2019 15 754 582 26.2 7 782 034 8 190 815 11 931 656 1 015 792 5 070 567 836 293 2020 6 517 480 25.7 6 349 834 7 055 575 17 135 346* 1 458 804 7 258 381+ 1 197 131 2021 7 897 450 14.6 6 690 048 7 004 725 7 090 882 1 144 562 5 725 330 1 126 731 Gabon 2019 3 000 15.6 – 250 117 126 117 126 117 126 117 126 2020 – 14.7 – 1 250 30 819$ 30 819 30 819# 30 819 2021 – 13.8 – 38 093 255 700 255 700 103 866 103 866 188 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – D. COMMODITIES DISTRIBUTION AND COVERAGE, 2019–2021 WHO region Country/area Year No. of LLINs delivered Modelled percentage of population with access to an ITN No. of people protected by IRS No. of RDTs distributed Any first-line treatment courses delivered (including ACT) No. of malaria cases treated with any first-line treatment courses (including ACT) ACT treatment courses delivered No. of malaria cases treated with ACT AFRICAN Gambia 2019 1 115 780 60.6 507 872 505 895 66 899 53 386 66 899 53 385 2020 71 469 53.9 477 032 525 505 90 603$ 72 300 90 603+ 72 300 2021 262 065 47.6 423 511 773 375 151 189 72 247 151 045 72 247 Ghana 2019 2 924 717 78.9 1 986 408 12 866 700 4 208 875 6 164 160 4 208 875 6 164 160 2020 2 957 388 59.0 2 214 552 – 7 037 451* 5 174 075 7 037 451+ 5 174 075 2021 17 845 229 63.8 2 128 109 10 663 060 5 948 832 5 728 505 5 948 832 5 728 505 Guinea 2019 8 964 940 69.6 – 2 857 744 2 053 442 1 646 493 2 053 442 1 646 493 2020 837 395 76.0 – – 2 187 117$ 1 831 203 2 187 117+ 1 792 653 2021 945 879 49.9 – 3 803 960 3 497 974 2 358 447 3 497 974 2 358 447 Guinea-Bissau 2019 102 586 37.7 – 325 690 155 848 155 848 140 478 140 478 2020 1 294 976 24.8 – – 152 900* 152 900 137 821# 137 821 2021 73 488 21.7 – – 168 720ˆ 168 720 152 081+ 152 081 Kenya 2019 1 797 075 61.2 2 011 860 4 179 875 8 285 622 5 259 988 7 247 430 5 004 487 2020 1 349 895 46.2 1 792 495 7 223 850 9 293 158* 5 899 605 8 543 728+ 5 899 605 2021 17 912 956 57.4 2 083 177 5 930 410 4 428 207 3 618 614 4 169 317 3 618 614 Liberia 2019 197 736 60.5 – 536 915 2 108 721 1 004 895 2 108 721 732 322 2020 – 26.1 – – 1 790 171$ 621 695 1 790 171+ 621 695 2021 2 943 429 59.1 – 2 158 290 785 485 835 087 785 485 785 485 Madagascar 2019 1 078 541 66.8 1 640 183 2 899 007 975 587 1 008 480 975 587 1 008 480 2020 1 720 923 49.8 981 936 1 950 471 1 613 457$ 1 667 856 1 613 457 1 667 856 2021 13 669 688 57.3 885 814 4 345 213 1 918 587 1 947 787 1 918 587 1 921 755 Malawi 2019 1 064 495 71.1 1 456 138 – 6 638 406 5 089 716 6 638 406 5 089 716 2020 926 690 43.2 2 379 659 16 258 123 7 957 086$ 7 095 977 7 957 086+ 7 095 977 2021 9 134 777 54.1 2 407 351 – 8 433 158 6 723 831 8 433 158 6 723 831 Mali 2019 4 005 010 74.5 690 793 3 656 317 5 543 127 2 846 438 5 543 127 2 826 112 2020 8 680 286 84.5 503 043 2 927 529 3 516 929 2 629 557 3 516 929 1 793 074 2021 3 113 190 85.1 233 663 3 267 184 1 924 709 3 153 865 1 924 709 2 126 004 Mauritania 2019 22 470 10.4 – – – – – – 2020 1 632 858 24.6 – – 760$ 760 760# 760 2021 10 029 43.1 – – 439 943 342 20 760 342 Mayotte 2019 – – – – – – – – 2020 – – – – – – – – 2021 – – – – – – – – Mozambique 2019 6 614 068 52.8 6 303 792 21 365 400 16 867 851 10 742 632 16 867 851 10 742 632 2020 24 534 223 65.3 6 880 851 21 425 892 17 808 682 11 132 323 17 808 682 11 132 323 2021 1 606 570 59.6 6 484 733 24 986 825 18 756 461 9 979 416 18 756 461 9 979 416 Namibia 2019 – – 149 306 247 425 3 404 3 404 3 404 3 404 2020 – – 1 017 366 – 13 636ˆ 13 636 13 636# 13 636 2021 – – 191 819 – 13 738 13 738 13 738 13 738 Niger 2019 1 427 735 76.6 – 5 831 287 3 211 243 3 015 081 3 211 243 3 015 081 2020 8 595 289 84.3 – – 3 678 980$ 3 350 428 3 678 980+ 3 350 428 2021 5 356 900 89.2 – 689 230 5 064 124 3 251 475 5 064 124 3 251 475 Nigeria 2019 32 360 674 48.0 – 26 312 300 38 240 771 21 252 650 38 240 771 21 252 650 2020 33 619 587 44.6 – 15 593 375 17 892 696 19 902 369 17 892 696 19 902 369 2021 16 401 333 51.0 – 40 314 426 40 314 426 34 236 521 40 314 426 34 236 521 Rwanda 2019 536 637 40.1 4 532 103 4 904 370 4 231 880 3 566 544 4 215 120 3 545 251 2020 6 218 906 60.1 4 951 899 – 1 243 100$ 1 048 450 1 243 100+ 1 048 450 2021 753 171 65.4 5 070 159 – 262 633 221 509 259 921 219 221 Sao Tome and Principe 2019 16 260 – 53 401 221 450 2 457 2 457 2 457 2 457 2020 11 107 – 69 902 – 1 772 1 772 1 772# 1 772 2021 8 143 – 27 174 156 757 2 515 2 515 2 515 2 515 Senegal 2019 9 373 577 62.7 51 652 2 552 381 1 073 464 354 708 971 497 339 598 2020 581 648 82.4 793 026 3 324 538 1 347 665$ 445 313 1 347 665 442 413 2021 1 342 541 57.7 803 093 4 736 075 1 177 667 536 850 1 177 667 520 738 189 ANNEx 4 – D. COMMODITIES DISTRIBUTION AND COVERAGE, 2019–2021 WHO region Country/area Year No. of LLINs delivered Modelled percentage of population with access to an ITN No. of people protected by IRS No. of RDTs distributed Any first-line treatment courses delivered (including ACT) No. of malaria cases treated with any first-line treatment courses (including ACT) ACT treatment courses delivered No. of malaria cases treated with ACT AFRICAN Sierra Leone 2019 492 622 49.5 – 3 930 606 4 751 000 2 813 086 4 751 000 2 404 286 2020 4 807 339 68.1 – 429 467 1 302 117* 658 948 1 302 117+ 658 948 2021 452 249 69.6 629 659 – 6 706 736* 3 394 004 6 706 736+ 3 394 004 South Africa 2019 – – 1 477 420 879 625 10 592$ 13 833 10 592 13 833 2020 – – 1 830 342 435 600 16 028$ 20 190 16 028# 16 028 2021 – – 1 704 233 58 750 16 853$ 16 853 16 853 16 853 South Sudan1 2019 713 717 33.6 344 242 – 4 308 214 122 665 4 308 214 122 665 2020 4 273 644 52.5 – 280 150 220 548 822 563 195 878 822 563 2021 1 685 771 59.3 263 856 – 1 618 709ˆ 1 618 709 1 618 709# 1 618 709 Togo 2019 407 911 65.7 – 2 957 298 2 889 270 2 284 746 1 499 012 2 266 412 2020 6 265 275 83.8 – – 2 169 770$ 1 729 667 1 134 827 1 715 787 2021 453 410 88.9 – – 1 401 474* 1 772 292 732 995+ 1 099 350 Uganda 2019 1 855 163 60.6 4 478 754 30 979 775 17 706 390 28 847 873 17 706 390 9 728 920 2020 25 700 519 69.8 4 671 960 36 746 550 26 674 975* 30 962 280 26 674 975+ 14 656 782 2021 1 410 031 74.5 4 466 905 – 31 746 773 13 522 685 31 746 773 14 772 475 United Republic of Tanzania2 2019 6 968 606 53.8 2 989 048 26 058 455 8 487 473 6 385 075 8 485 301 6 385 075 2020 19 684 506 56.1 2 510 463 41 180 225 22 636 877* 7 759 228 10 314 557+ 7 759 228 2021 8 264 964 50.7 2 869 266 27 850 825 11 665 860 5 874 588 11 665 860 5 868 238 Mainland 2019 6 745 132 – 2 507 920 25 699 255 8 479 635 6 378 890 8 479 635 6 378 890 2020 19 386 472 – 2 285 089 40 821 350 10 314 557* 7 759 228 10 314 557+ 7 759 228 2021 7 513 065 – 2 655 998 27 850 825 11 665 860 5 868 238 11 665 860 5 868 238 Zanzibar 2019 223 474 – 481 128 359 200 7 838 6 963 5 666 6 185 2020 298 034 – 225 374 358 875 4 900 – – – 2021 751 899 – 213 268 – – 6 350 – – Zambia 2019 1 024 635 48.2 11 767 404 17 737 525 19 134 471 19 134 471 19 134 471 19 134 471 2020 6 179 374 45.3 11 157 421 28 988 900 7 473 255 7 473 255 7 473 255# 7 473 255 2021 12 147 773 57.0 9 480 413 37 006 526 21 379 380 21 379 380 21 379 380 21 379 380 Zimbabwe 2019 2 160 175 37.8 3 164 344 1 445 007 2 446 203 304 309 2 446 203 304 309 2020 727 377 40.3 3 528 051 2 932 248 443 164$ 443 164 443 164# 443 164 2021 1 227 112 36.4 3 113 471 2 289 531 133 926 133 926 133 926 133 926 AMERICAS Belize 2019 0 – 43 497 – 2 2 0 0 2020 0 – 45 100 0 0 0 0 0 2021 5 753 – 42 901 – 0 0 0 0 Bolivia (Plurinational State of) 2019 27 000 – 29 228 36 800 9 357 9 357 9 357 9 357 2020 91 700 – 29 228 – 12 093$ 12 093 12 093# 12 093 2021 23 500 – – – 9 959 9 959 9 959 9 959 Brazil 2019 – – 84 126 102 275 491 126 491 126 74 360 74 360 2020 173 850 – 148 897 148 200 172 047 172 047 23 691# 23 691 2021 144 250 – 202 715 154 050 148 240 137 289 34 952# 34 952 Colombia 2019 336 432 – 141 000 25 000 80 418 39 891 39 891 39 891 2020 336 432 – 242 748 153 867 81 368 39 879 39 879 39 879 2021 208 296 – 245 984 95 867 73 974 36 531 36 531 36 531 Costa Rica 2019 – – – – – – – – 2020 – – 6 895 5 175 141ˆ 141 – – 2021 3 300 – 27 756 – 504ˆ 504 12# 12 Dominican Republic 2019 – – 35 220 55 000 1 314 1 314 7 7 2020 11 500 – 37 211 – 829ˆ 829 – – 2021 28 500 – 107 375 – 291ˆ 291 – – Ecuador 2019 31 271 – 698 292 73 425 5 030 1 909 2 650 265 2020 4 983 – 17 276 41 968 5 272* 2 001 2430+ 243 2021 43 159 – 1 435 556 54 450 5 380 2 450 1 493 499 190 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – D. COMMODITIES DISTRIBUTION AND COVERAGE, 2019–2021 WHO region Country/area Year No. of LLINs delivered Modelled percentage of population with access to an ITN No. of people protected by IRS No. of RDTs distributed Any first-line treatment courses delivered (including ACT) No. of malaria cases treated with any first-line treatment courses (including ACT) ACT treatment courses delivered No. of malaria cases treated with ACT AMERICAS French Guiana 2019 – – – – – – – – 2020 – – – – – – – – 2021 – – – – – – – – Guatemala 2019 128 982 – 4 091 61 275 2ˆ 2 2# 2 2020 197 944 – 13 386 – 1 233* 1 058 – – 2021 381 291 – 12 401 161 675 1 265 1 265 0 0 Guyana 2019 1 759 – – 37 800 20 313 20 313 7 052 7 052 2020 1 816 – – 22 175 32 958 32 958 13 980 13 980 2021 95 058 – – 26 622 36 844 36 844 14 010 14 010 Haiti 2019 19 063 – – 293 200 10 418 10 418 – – 2020 971 530 – – – 21 856 21 856 – – 2021 19 858 – – – 9 513 9 513 – – Honduras 2019 32 091 – 335 928 18 754 383 – 45# 45 2020 20 760 – 181 715 10 350 913 – 12# 12 2021 18 863 – 124 554 14 605 1 657 – 12# 12 Mexico 2019 19 001 – 83 581 – 631 641 12 12 2020 13 301 – 72 759 – 369 369 0 0 2021 17 673 – 142 618 4 500 275 275 0 0 Nicaragua 2019 228 589 – 139 795 63 500 35 649 13 226 57 – 2020 61 520 – 226 731 – 68 813* 25 530 702 – 2021 61 766 – 47 948 – 23 323 8 653 1 521 – Panama 2019 4 337 – 12 806 30 725 – – 3 3 2020 – – 12 492 2 500 1 538 1 582 5 5 2021 – – 20 719 40 000 3 539 3 539 0 0 Peru 2019 – – 59 438 204 000 51 289 51 289 4 724 4 724 2020 93 067 – – – 34 721* 34 721 3 198# 3 198 2021 20 267 – – – 18 140 18 140 3 595+ 3 595 Suriname 2019 6 847 – – 20 625 202 202 202 202 2020 6 864 – – 17 250 236ˆ 236 127# 127 2021 10 059 – – 14 625 76 76 8 8 Venezuela (Bolivarian Republic of) 2019 249 411 – – 250 000 398 285 398 285 90 153 90 153 2020 73 605 – – 115 417 231 384ˆ 231 384 48 292# 48 292 2021 36 362 – – 257 626 194 057 194 057 32 582 32 582 EASTERN MEDITERRANEAN Afghanistan 2019 1 336 070 – – 714 700 180 992$ 169 504 180 992 118 145 2020 3 140 845 – – 337 840 153 403$ 103 466 153 403+ 100 136 2021 195 273 – – 468 330 11 681$ 84 873 11 681 84 817 Djibouti 2019 218 650 21.0 37 663 335 625 148 890 47 691 148 890 47 691 2020 145 392 12.2 28 496 268 147 215 507* 69 029 215 507+ 69 029 2021 19 984 10.4 116 961 100 000 76 380 56 081 76 380 56 081 Iran (Islamic Republic of) 2019 – – 59 869 7 737 1 192 8 139 158 2020 10 543 – 73 846 – 1 051 6 491+ 126 2021 6 044 – 47 762 25 025 2 856 999 2 856 151 Pakistan 2019 2 413 275 – 1 657 670 3 895 000 290 170 413 533 57 781 90 178 2020 1 515 426 – 120 610 3 616 500 428 738 347 500 99 425 347 500 2021 147 880 – 307 272 3 721 655 422 798 365 626 123 617 87 825 Saudi Arabia 2019 – – 225 510 135 000 25 000 2 152 15 000 1 515 2020 – – 129 105 165 000 37 930* 3 265 31 990+ 3 231 2021 – – 95 754 165 000 30 390* 2 616 20 030+ 2 023 Somalia 2019 388 766 18.0 82 720 974 700 174 030 38 112 174 030 38 112 2020 1 565 552 20.6 283 665 554 500 109 490$ 23 978 109 490 27 333 2021 79 895 21.0 80 622 647 500 180 840 – 180 840 – 191 ANNEx 4 – D. COMMODITIES DISTRIBUTION AND COVERAGE, 2019–2021 WHO region Country/area Year No. of LLINs delivered Modelled percentage of population with access to an ITN No. of people protected by IRS No. of RDTs distributed Any first-line treatment courses delivered (including ACT) No. of malaria cases treated with any first-line treatment courses (including ACT) ACT treatment courses delivered No. of malaria cases treated with ACT EASTERN MEDITERRANEAN Sudan 2019 9 288 816 59.8 3 886 652 7 246 975 4 297 167 4 297 167 4 297 167 4 297 167 2020 5 003 295 59.8 3 901 092 4 849 600 69 300 703 4 863 826 69 300 703 4 863 826 2021 791 079 58.5 3 870 477 2 590 000 7 343 701 3 708 843 7 373 701 3 700 843 Yemen 2019 612 884 – 1 982 284 907 425 458 103 – 42 698 – 2020 855 693 – 762 755 – – – – – 2021 1 769 759 – 1 499 738 – – – – – SOUTH‑EAST ASIA Bangladesh 2019 727 253 – 98 786 756 573 17 225 17 225 15 099 15 099 2020 1 316 909 – – 805 166 6 130 6 130 4 885# 4 885 2021 961 156 – – 823 336 7 294 7 294 5 340 5 340 Bhutan 2019 13 906 – 118 730 29 100 235 42 235 11 2020 122 670 – 122 406 42 675 – 54 31+ 8 2021 13 294 – 120 166 28 345 656 23 0 5 Democratic People’s Republic of Korea 2019 30 928 – – 458 743 4 000 1 869 – – 2020 – – 402 861 354 097 3 893* 1 819 – – 2021 – – – – 2 357 1 101 – – India 2019 22 410 000 – 30 363 425 – 12 641 952 338 494 12 641 952 338 494 2020 25 221 045 – 23 950 862 20 000 000 4 447 618$ 186 532 4 447 618+ 119 087 2021 16 197 740 – 16 159 858 – 335 062 161 753 335 062 101 566 Indonesia 2019 200 990 – 164 192 1 980 775 1 174 186 234 381 1 174 186 234 381 2020 3 448 169 – 38 332 613 300 1 208 253$ 241 181 1 208 253+ 241 181 2021 50 350 – 131 818 2 894 125 3 583 329 299 148 3 583 329 299 148 Myanmar 2019 11 046 312 – 4 361 2 652 010 51 779 53 003 51 779 23 623 2020 569 016 – 17 381 2 944 555 34 132$ 15 572 34 132+ 15 572 2021 160 688 – – – 33 157* 15 127 33 157+ 15 127 Nepal 2019 162 409 – 263 000 205 636 13 621 710 3 522 63 2020 72 561 – 41 235 202 300 8 249* 430 2 180+ 39 2021 134 085 – 43 500 325 075 15 647 391 9 539 92 Thailand 2019 80 000 – 489 105 303 613 31 276 3 904 11 976 536 2020 102 150 – 219 162 69 912 26 766* 3 341 3 910+ 175 2021 130 873 – 159 097 144 925 361 459 3 254 4 505 86 Timor-Leste 2019 97 586 – 175 654 249 750 1 070 9 1 070 9 2020 176 785 – 116 949 137 668 1 664* 14 1 664+ 14 2021 52 886 – 76 574 122 955 0 0 0 0 192 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – D. COMMODITIES DISTRIBUTION AND COVERAGE, 2019–2021 WHO region Country/area Year No. of LLINs delivered Modelled percentage of population with access to an ITN No. of people protected by IRS No. of RDTs distributed Any first-line treatment courses delivered (including ACT) No. of malaria cases treated with any first-line treatment courses (including ACT) ACT treatment courses delivered No. of malaria cases treated with ACT WESTERN PACIFIC Cambodia 2019 555 962 – – 923 375 98 965 31 883 98 965 31 883 2020 1 205 286 – – 1 069 200 37 143 9 176 37 143 9 176 2021 603 528 – – 851 240 100 483 4 270 100 483 4 270 Lao People’s Democratic Republic 2019 1 085 527 – 3 333 1 371 367 21 071 6 551 21 071 6 550 2020 100 518 – 2 333 1 667 795 11 251* 3 498 11 185+ 3 477 2021 46 353 – 5 606 749 710 16 252 3 852 16 252 3 870 Malaysia 2019 112 054 – 323 208 – 3 933 3 933 3 933 3 923 2020 123 115 – 305 688 – 2 830 2 830 2 829# 2 829 2021 119 584 – 324 018 – 3 676 3 676 3 663 3 663 Papua New Guinea 2019 1 476 976 – – 2 454 525 1 323 042 788 796 1 323 042 788 796 2020 1 579 301 – 200 3 139 420 1 258 396$ 750 439 1 258 396 750 254 2021 1 626 031 – – 3 398 965 1 368 984 1 324 753 2 222 000 1 324 753 Philippines 2019 695 691 – 731 696 370 700 49 359 5 435 16 857 4 845 2020 329 412 – 476 804 77 645 54 372* 5 987 20 830+ 4 890 2021 205 704 – 497 569 262 895 36 318 3 999 15 910 3 735 Republic of Korea 2019 – – – 20 000 196 196 – – 2020 – – – – 386ˆ 385 – – 2021 – – – – 294 294 – – Solomon Islands 2019 297 010 – – 484 750 230 880 83 733 230 880 83 364 2020 7 530 – – 275 000 239 064$ 87 477 239 064+ 86 319 2021 182 926 – – – 328587$ 118 643 328 587 118 643 Vanuatu 2019 80 623 – – 4 490 7 235 571 579 571 2020 – – – 59 825 6 285* 496 503+ 496 2021 37 090 – – 2 164 42 501 304 42 501 304 Viet Nam 2019 31 740 – 696 751 472 173 31 348 5 892 27 704 3 134 2020 53 155 – 433 405 531 795 9 220* 1 733 7 231+ 818 2021 1 486 700 – 262 297 389 745 9 569 1 799 193 – Data as of 1 November 2022 ACT: artemisinin-based combination therapy; IRS: indoor residual spraying; ITN: insecticide-treated mosquito net; LLIN: long-lasting insecticidal net; RDT: rapid diagnostic test; WHO: World Health Organization. “–” refers to data not available. 1 In May 2013, South Sudan was reassigned to the WHO African Region (WHA resolution 66.21, https://apps.who.int/gb/ebwha/pdf_files/ WHA66/A66_R21-en.pdf). 2 Where national data for the United Republic of Tanzania are unavailable, refer to Mainland and Zanzibar. * Any first-line courses delivered (including ACT) are calculated. ˆ The number of malaria cases treated with any first-line treatment courses (including ACT) has been used as a proxy for any first-line treatment courses delivered (including ACT), or the country reports the number of patients treated rather than the number of treatment courses delivered. $ ACT treatment courses delivered are used to replace missing data for any first-line treatment courses delivered (including ACT). + ACT treatment courses delivered are calculated. # The number of malaria cases treated with ACT has been used as a proxy for ACT treatment courses delivered, or the country reports the number of patients treated rather than the number of treatment courses delivered. Note: Similar adjustments were made for 2019 where deliveries of first-line treatment courses and ACT were missing. 193 ANNEx 4 – Ea. hOUSEhOLD SURVEY RESULTS, 2017–2021, COMPILED BY STATCOMPILER WHO region Country/area Source % of households % of population % of ITNs % of pregnant women % of children <5 years % of children <5 years with fever in the past 2 weeks with at least one ITN with at least one ITN for every two persons who stayed in the household the previous night with IRS in the past 12 months with at least one ITN and/or IRS in the past 12 months with at least one ITN for every two persons and/or IRS in the past 12 months with access to an ITN who slept under an ITN last night that were used last night who slept under an ITN who took 3+ doses of IPTp who slept under an ITN with moderate or severe anaemia with a positive RDT with a positive microscopy blood smear for whom advice or treatment was sought who had blood taken from a finger or heel for testing who took antimalarial drugs who took an ACT among those who received any antimalarial AFRICAN Benin 2017–18 DHS 91.5 60.5 8.7 92.0 63.8 77.2 71.1 73.4 79.3 13.7 76.3 43.8 36.3 39.1 53.1 17.7 17.5 37.0 Burkina Faso 2017–18 MIS 75.3 32.8 – – – 54.5 44.1 76.0 58.2 57.7 54.4 50.1 20.2 16.9 73.5 48.8 51.1 79.4 Cameroon 2018 DHS 73.4 40.7 – – – 58.5 53.9 76.2 61.0 31.9 59.8 31.0 24.0 – 61.0 21.4 32.7 21.2 Gambia 2019–20 DHS 77.3 36.3 – – – 60.8 37.8 55.0 44.2 52.2 44.0 20.7 0.4 – 64.2 27.3 3.5 46.7 Ghana 2019 MIS 73.7 51.8 5.8 75.0 54.7 66.7 43.2 50.1 48.7 61.0 54.1 27.9 23.0 14.1 69.0 34.1 45.9 84.5 Guinea 2018 DHS 43.9 16.7 – – – 30.7 22.7 64.0 28.1 35.7 26.6 43.8 – – 62.3 20.5 24.8 18.2 Guinea 2021 MIS 63.3 22.0 – – – 41.9 33.4 72.0 39.4 50.3 38.2 45.5 33.7 17.4 61.1 28.0 31.4 38.0 Kenya 2020 MIS 49.0 28.7 – – – 39.6 34.9 80.2 39.8 22.0 42.0 21.3 4.4 3.0 63.6 35.5 20.2 91.0 Liberia 2019–20 DHS 54.7 25.2 – – – 39.7 39.0 74.7 46.5 40.3 44.3 41.6 – – 80.9 49.0 52.1 41.2 Madagascar 2021 DHS 69.1 30.1 – – – 48.4 48.8 77.3 54.9 31.0 55.6 20.2 7.5 – 44.6 19.9 15.3 54.7 Malawi 2017 MIS 82.1 41.7 – – – 63.1 55.4 76.8 62.5 41.1 67.5 37.1 36.0 24.3 54.4 37.6 29.4 96.4 Mali 2018 DHS 89.8 54.8 – – – 75.2 72.9 88.7 83.7 28.3 79.1 56.7 18.9 – 52.8 16.4 18.7 31.0 Mauritania 2019–21 DHS 32.2 8.0 – – – 19.5 10.9 42.0 11.7 10.2 11.9 54.9 1.1 – 31.4 5.8 15.3 19.0 Mozambique 2018 MIS 82.2 51.2 – – – 68.5 68.4 85.4 76.4 40.6 72.7 55.2 38.9 – 68.6 47.9 32.7 98.6 Nigeria 2018 DHS 60.6 29.8 – – – 47.5 43.2 80.6 58.0 16.6 52.2 41.1 36.2 22.6 72.8 13.8 43.5 52.0 Rwanda 2017 MIS 84.1 55.1 19.6 89.2 66.9 71.9 63.9 71.0 68.5 – 68.0 – 11.8 7.2 55.6 38.1 19.6 98.7 Rwanda 2019–20 DHS 66.4 34.3 – – – 50.8 47.7 78.0 56.1 – 55.6 15.2 2.7 0.9 62.3 40.7 8.1 92.4 Senegal 2017 DHS 84.2 50.4 4.2 84.5 52.3 72.8 56.9 68.6 61.8 22.0 60.7 41.8 0.9 0.4 51.4 16.1 4.7 65.5 Senegal 2018 DHS 76.6 39.0 2.1 76.8 40.1 62.2 51.6 74.5 55.7 22.4 56.4 – – – 52.8 13.8 5.1 24.0 Senegal 2019 DHS 81.0 56.8 2.4 81.5 57.7 74.2 62.5 68.2 68.1 19.6 65.4 – – – 50.0 15.7 1.4 – Senegal 2020–21 MIS 75.3 33.8 3.3 76.3 36.1 57.8 46.4 81.4 52.5 37.7 46.5 67.3 – – 63.0 21.7 2.7 1.7 Sierra Leone 2019 DHS 67.9 25.0 – – – 46.8 50.6 89.5 63.8 35.7 59.1 37.9 – – 75.4 61.3 55.9 31.9 United Republic of Tanzania 2017 MIS 77.9 45.4 – – – 62.5 52.2 66.7 51.4 25.8 54.6 30.5 7.3 – 75.4 43.1 36.2 89.4 Togo 2017 MIS 85.2 71.4 – – – 82.3 62.5 52.3 69.0 41.7 69.7 47.8 43.9 28.3 55.9 29.3 31.1 76.3 Uganda 2018–19 MIS 83.0 53.9 10.1 84.2 58.7 71.5 59.2 74.3 65.4 41.0 60.3 25.0 18.2 9.8 87.0 50.7 62.5 87.7 Zambia 2018 DHS 78.3 40.9 35.3 83.3 60.4 59.9 46.4 64.2 48.9 58.7 51.6 29.5 – – 77.2 63.0 34.9 96.9 194 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – Ea. hOUSEhOLD SURVEY RESULTS, 2017–2021, COMPILED BY STATCOMPILER WHO region Country/area Source % of households % of population % of ITNs % of pregnant women % of children <5 years % of children <5 years with fever in the past 2 weeks with at least one ITN with at least one ITN for every two persons who stayed in the household the previous night with IRS in the past 12 months with at least one ITN and/or IRS in the past 12 months with at least one ITN for every two persons and/or IRS in the past 12 months with access to an ITN who slept under an ITN last night that were used last night who slept under an ITN who took 3+ doses of IPTp who slept under an ITN with moderate or severe anaemia with a positive RDT with a positive microscopy blood smear for whom advice or treatment was sought who had blood taken from a finger or heel for testing who took antimalarial drugs who took an ACT among those who received any antimalarial AFRICAN Benin 2017–18 DHS 91.5 60.5 8.7 92.0 63.8 77.2 71.1 73.4 79.3 13.7 76.3 43.8 36.3 39.1 53.1 17.7 17.5 37.0 Burkina Faso 2017–18 MIS 75.3 32.8 – – – 54.5 44.1 76.0 58.2 57.7 54.4 50.1 20.2 16.9 73.5 48.8 51.1 79.4 Cameroon 2018 DHS 73.4 40.7 – – – 58.5 53.9 76.2 61.0 31.9 59.8 31.0 24.0 – 61.0 21.4 32.7 21.2 Gambia 2019–20 DHS 77.3 36.3 – – – 60.8 37.8 55.0 44.2 52.2 44.0 20.7 0.4 – 64.2 27.3 3.5 46.7 Ghana 2019 MIS 73.7 51.8 5.8 75.0 54.7 66.7 43.2 50.1 48.7 61.0 54.1 27.9 23.0 14.1 69.0 34.1 45.9 84.5 Guinea 2018 DHS 43.9 16.7 – – – 30.7 22.7 64.0 28.1 35.7 26.6 43.8 – – 62.3 20.5 24.8 18.2 Guinea 2021 MIS 63.3 22.0 – – – 41.9 33.4 72.0 39.4 50.3 38.2 45.5 33.7 17.4 61.1 28.0 31.4 38.0 Kenya 2020 MIS 49.0 28.7 – – – 39.6 34.9 80.2 39.8 22.0 42.0 21.3 4.4 3.0 63.6 35.5 20.2 91.0 Liberia 2019–20 DHS 54.7 25.2 – – – 39.7 39.0 74.7 46.5 40.3 44.3 41.6 – – 80.9 49.0 52.1 41.2 Madagascar 2021 DHS 69.1 30.1 – – – 48.4 48.8 77.3 54.9 31.0 55.6 20.2 7.5 – 44.6 19.9 15.3 54.7 Malawi 2017 MIS 82.1 41.7 – – – 63.1 55.4 76.8 62.5 41.1 67.5 37.1 36.0 24.3 54.4 37.6 29.4 96.4 Mali 2018 DHS 89.8 54.8 – – – 75.2 72.9 88.7 83.7 28.3 79.1 56.7 18.9 – 52.8 16.4 18.7 31.0 Mauritania 2019–21 DHS 32.2 8.0 – – – 19.5 10.9 42.0 11.7 10.2 11.9 54.9 1.1 – 31.4 5.8 15.3 19.0 Mozambique 2018 MIS 82.2 51.2 – – – 68.5 68.4 85.4 76.4 40.6 72.7 55.2 38.9 – 68.6 47.9 32.7 98.6 Nigeria 2018 DHS 60.6 29.8 – – – 47.5 43.2 80.6 58.0 16.6 52.2 41.1 36.2 22.6 72.8 13.8 43.5 52.0 Rwanda 2017 MIS 84.1 55.1 19.6 89.2 66.9 71.9 63.9 71.0 68.5 – 68.0 – 11.8 7.2 55.6 38.1 19.6 98.7 Rwanda 2019–20 DHS 66.4 34.3 – – – 50.8 47.7 78.0 56.1 – 55.6 15.2 2.7 0.9 62.3 40.7 8.1 92.4 Senegal 2017 DHS 84.2 50.4 4.2 84.5 52.3 72.8 56.9 68.6 61.8 22.0 60.7 41.8 0.9 0.4 51.4 16.1 4.7 65.5 Senegal 2018 DHS 76.6 39.0 2.1 76.8 40.1 62.2 51.6 74.5 55.7 22.4 56.4 – – – 52.8 13.8 5.1 24.0 Senegal 2019 DHS 81.0 56.8 2.4 81.5 57.7 74.2 62.5 68.2 68.1 19.6 65.4 – – – 50.0 15.7 1.4 – Senegal 2020–21 MIS 75.3 33.8 3.3 76.3 36.1 57.8 46.4 81.4 52.5 37.7 46.5 67.3 – – 63.0 21.7 2.7 1.7 Sierra Leone 2019 DHS 67.9 25.0 – – – 46.8 50.6 89.5 63.8 35.7 59.1 37.9 – – 75.4 61.3 55.9 31.9 United Republic of Tanzania 2017 MIS 77.9 45.4 – – – 62.5 52.2 66.7 51.4 25.8 54.6 30.5 7.3 – 75.4 43.1 36.2 89.4 Togo 2017 MIS 85.2 71.4 – – – 82.3 62.5 52.3 69.0 41.7 69.7 47.8 43.9 28.3 55.9 29.3 31.1 76.3 Uganda 2018–19 MIS 83.0 53.9 10.1 84.2 58.7 71.5 59.2 74.3 65.4 41.0 60.3 25.0 18.2 9.8 87.0 50.7 62.5 87.7 Zambia 2018 DHS 78.3 40.9 35.3 83.3 60.4 59.9 46.4 64.2 48.9 58.7 51.6 29.5 – – 77.2 63.0 34.9 96.9 195 ANNEx 4 – Ea. hOUSEhOLD SURVEY RESULTS, 2017–2021, COMPILED BY STATCOMPILER WHO region Country/area Source % of households % of population % of ITNs % of pregnant women % of children <5 years % of children <5 years with fever in the past 2 weeks with at least one ITN with at least one ITN for every two persons who stayed in the household the previous night with IRS in the past 12 months with at least one ITN and/or IRS in the past 12 months with at least one ITN for every two persons and/or IRS in the past 12 months with access to an ITN who slept under an ITN last night that were used last night who slept under an ITN who took 3+ doses of IPTp who slept under an ITN with moderate or severe anaemia with a positive RDT with a positive microscopy blood smear for whom advice or treatment was sought who had blood taken from a finger or heel for testing who took antimalarial drugs who took an ACT among those who received any antimalarial EASTERN MEDITERRANEAN Jordan 2017–18 DHS – – – – – – – – – – – 10.6 – – 68.4 – – – Pakistan 2017–18 DHS 3.6 0.6 5.1 8.4 5.7 2.0 0.2 11.6 0.4 – 0.4 – – – 81.4 – 9.2 3.3 SOUTH‑EAST ASIA India 2019–21 DHS 7.9 4.0 – – – 5.8 3.8 58.5 4.2 – 4.4 38.8 – – 79.9 13.6 25.2 6.0 WESTERN PACIFIC Papua New Guinea 2016–18 DHS 68.5 45.2 – – – 57.9 46.0 67.9 49.0 23.5 51.5 – – – 49.5 24.6 21.3 3.3 Philippines 2017 DHS – – – – – – – – – – – – – – 54.5 – – – ACT: artemisinin-based combination therapy; DHS: demographic and health survey; IPTp: intermittent preventive treatment of malaria in pregnancy; IRS: indoor residual spraying; ITN: insecticide-treated mosquito net; MIS: malaria indicator survey; RDT: rapid diagnostic test; WHO: World Health Organization. “–” refers to not applicable or data not available. Sources: Nationally representative household survey data from DHS and MIS, compiled through STATcompiler – https://www.statcompiler.com/. Data as of 24 October 2022 196 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – Ea. hOUSEhOLD SURVEY RESULTS, 2017–2021, COMPILED BY STATCOMPILER WHO region Country/area Source % of households % of population % of ITNs % of pregnant women % of children <5 years % of children <5 years with fever in the past 2 weeks with at least one ITN with at least one ITN for every two persons who stayed in the household the previous night with IRS in the past 12 months with at least one ITN and/or IRS in the past 12 months with at least one ITN for every two persons and/or IRS in the past 12 months with access to an ITN who slept under an ITN last night that were used last night who slept under an ITN who took 3+ doses of IPTp who slept under an ITN with moderate or severe anaemia with a positive RDT with a positive microscopy blood smear for whom advice or treatment was sought who had blood taken from a finger or heel for testing who took antimalarial drugs who took an ACT among those who received any antimalarial EASTERN MEDITERRANEAN Jordan 2017–18 DHS – – – – – – – – – – – 10.6 – – 68.4 – – – Pakistan 2017–18 DHS 3.6 0.6 5.1 8.4 5.7 2.0 0.2 11.6 0.4 – 0.4 – – – 81.4 – 9.2 3.3 SOUTH‑EAST ASIA India 2019–21 DHS 7.9 4.0 – – – 5.8 3.8 58.5 4.2 – 4.4 38.8 – – 79.9 13.6 25.2 6.0 WESTERN PACIFIC Papua New Guinea 2016–18 DHS 68.5 45.2 – – – 57.9 46.0 67.9 49.0 23.5 51.5 – – – 49.5 24.6 21.3 3.3 Philippines 2017 DHS – – – – – – – – – – – – – – 54.5 – – – ACT: artemisinin-based combination therapy; DHS: demographic and health survey; IPTp: intermittent preventive treatment of malaria in pregnancy; IRS: indoor residual spraying; ITN: insecticide-treated mosquito net; MIS: malaria indicator survey; RDT: rapid diagnostic test; WHO: World Health Organization. “–” refers to not applicable or data not available. Sources: Nationally representative household survey data from DHS and MIS, compiled through STATcompiler – https://www.statcompiler.com/. Data as of 24 October 2022 197 ANNEx 4 - Eb. hOUSEhOLD SURVEY RESULTS, 2017–2021, COMPILED ThROUGh WhO CALCULATIONS WHO region Country Survey Fever prevalence Health sector where treatment was sought Diagnostic testing coverage in each health sector Diagnostic testing coverage in each health sector Antimalarial treatment coverage in each health sector ACT use among antimalarial treatment in each health sector O ve ra ll Pu bl ic ex clu di ng co m m un ity h ea lth w or ke rs Co m m un ity h ea lth w or ke rs Fo rm al m ed ica l pr iva te e xc lu di ng ph ar m ac ie s Ph ar m ac ie s o r ac cr ed ite d dr ug st or es In fo rm al p riv at e No tr ea tm en t se ek in g Tr ai ne d pr ov id er Pu bl ic ex clu di ng co m m un ity h ea lth w or ke rs Co m m un ity h ea lth w or ke rs Fo rm al m ed ica l pr iva te e xc lu di ng ph ar m ac ie s Ph ar m ac ie s or a cc re di te d dr ug st or es In fo rm al p riv at e Tr ai ne d pr ov id er Pu bl ic ex clu di ng co m m un ity h ea lth w or ke rs Co m m un ity h ea lth w or ke rs Fo rm al m ed ica l pr iva te e xc lu di ng ph ar m ac ie s Ph ar m ac ie s o r ac cr ed ite d dr ug st or es Se lf- tre at m en t No tr ea tm en t se ek in g Tr ai ne d pr ov id er Pu bl ic Pr iva te In fo rm al p riv at e AFRICAN Benin 2017 DHS 20(18, 21) 22 (20, 24) 0 (0, 0) 9 (8, 11) 9 (8, 11) 14 (12, 16) 46 (43, 49) 40 (37, 43) 52 (47, 57) – 30 (23, 38) 9 (6, 14) 8 (5, 12) 37 (33, 40) 38 (34, 44) – 34 (27, 41) 23 (17, 30) 12 (9, 17) 7 (5, 9) 34 (30, 37) 44 (36, 52) 31 (24, 39) 40 (26, 55) Burkina Faso 2017 MIS 20(19, 22) 71 (67, 75) 1 (0, 1) 1 (1, 4) 0 (0, 1) 2 (1, 3) 26 (22, 30) 73 (69, 76) 66 (61, 70) – – – 25 (13, 42) 66 (61, 70) 69 (64, 73) – – – 62 (37, 82) 10 (7, 14) 68 (64, 72) 80 (76, 83) 82 (54, 95) – Cameroon 2018 DHS 16(14, 17) 20 (17, 23) 1 (0, 1) 12 (9, 15) 12 (9, 14) 21 (18, 24) 37 (33, 41) 43 (39, 47) 52 (44, 61) – 54 (43, 65) 11 (7, 19) 8 (5, 15) 42 (36, 48) 58 (49, 66) – 48 (38, 58) 33 (24, 43) 46 (38, 54) 12 (9, 16) 48 (43, 54) 25 (17, 35) 21 (15, 27) 15 (8, 27) Gambia 2019 DHS 15(14, 17) 45 (41, 49) 0 (0, 1) 7 (5, 10) 13 (10, 16) 1 (0, 2) 35 (31, 39) 64 (60, 68) 42 (37, 48) – 54 (33, 73) 27 (19, 36) – 40 (35, 45) 5 (3, 8) – 5 (1, 18) 6 (3, 14) – 1 (0, 4) 5 (3, 7) 71 (51, 85) – – Ghana 2019 MIS 30(27, 33) 34 (30, 38) 0 (0, 1) 20 (17, 24) 14 (10, 18) 3 (1, 5) 30 (26, 35) 67 (63, 71) 78 (72, 83) – 30 (22, 39) 8 (4, 16) 3 (0, 28) 50 (45, 55) 63 (56, 70) – 55 (46, 63) 57 (44, 69) 50 (24, 76) 18 (14, 24) 59 (54, 65) 88 (80, 93) 86 (77, 92) – Guinea 2021 MIS 23(21, 25) 43 (37, 48) 4 (3, 6) 6 (4, 10) 5 (3, 7) 7 (5, 10) 38 (33, 43) 56 (51, 61) 54 (48, 60) 42 (26, 61) 35 (21, 52) 7 (2, 19) 7 (3, 18) 47 (42, 53) 55 (48, 61) 70 (49, 85) 26 (15, 42) 23 (12, 40) 24 (14, 38) 9 (6, 13) 50 (44, 56) 53 (46, 60) 53 (36, 70) – Kenya 2020 MIS 17(16, 19) 43 (36, 51) 0 (0, 1) 9 (6, 14) 11 (9, 15) 1 (0, 3) 36 (30, 42) 63 (57, 69) 60 (49, 70) – 54 (33, 74) 5 (2, 13) – 49 (41, 57) 4 (2, 9) – 8 (3, 19) 4 (1, 15) – 1 (0, 2) 5 (3, 8) – – – Liberia 2019 DHS 26(23, 28) 39 (35, 44) 0 (0, 0) 14 (11, 17) 26 (22, 31) 7 (5, 9) 19 (16, 22) 76 (72, 79) 82 (77, 87) – 66 (54, 77) 25 (19, 32) 38 (25, 53) 60 (55, 65) 72 (67, 76) – 62 (51, 72) 42 (31, 53) 76 (67, 83) 22 (17, 29) 59 (54, 64) 40 (33, 46) 47 (38, 56) 34 (19, 52) Madagascar 2021 DHS 12(11, 13) 35 (32, 38) 0 (0, 0) 8 (6, 10) 1 (0, 1) 2 (2, 3) 55 (51, 58) 43 (40, 47) 46 (41, 51) – 26 (17, 37) – 4 (1, 11) 42 (38, 46) 32 (27, 37) – 13 (7, 22) – 10 (7, 14) 6 (4, 9) 28 (24, 32) 55 (46, 63) – – Malawi 2017 MIS 40(38, 43) 38 (34, 43) 3 (2, 5) 6 (4, 8) 2 (1, 4) 7 (5, 10) 46 (41, 51) 48 (43, 52) 76 (70, 82) – 76 (61, 86) 10 (2, 38) 4 (1, 14) 73 (67, 78) 55 (48, 62) – 55 (39, 69) 22 (4, 64) 21 (9, 41) 7 (5, 11) 54 (48, 61) 98 (94, 99) 97 (81, 99) – Mali 2018 DHS 16(15, 17) 24 (21, 27) 3 (2, 5) 2 (1, 3) 7 (5, 9) 23 (19, 27) 42 (38, 46) 36 (33, 39) 46 (39, 53) 37 (22, 56) 16 (5, 38) 8 (3, 17) 5 (3, 9) 36 (30, 42) 61 (54, 68) 56 (38, 72) 29 (11, 57) 17 (9, 30) 5 (2, 10) 4 (3, 6) 50 (44, 57) 35 (27, 43) 20 (9, 38) – Mauritania 2020 DHS 17(16, 18) 25 (22, 28) 0 (0, 1) 2 (1, 3) 4 (3, 5) 2 (1, 2) 68 (65, 71) 31 (28, 34) 11 (8, 15) – 25 (11, 49) 16 (7, 34) 2 (0, 14) 13 (10, 17) 48 (42, 54) – 60 (22, 89) 51 (35, 66) – 2 (2, 4) 49 (43, 55) 21 (15, 29) 15 (6, 35) – Mozambique 2018 MIS 31(28, 35) 64 (57, 70) 4 (2, 7) 0 (0, 1) 0 (0, 1) 1 (1, 3) 31 (26, 37) 68 (62, 73) 72 (67, 76) 41 (13, 76) – – – 70 (65, 74) 47 (40, 53) 57 (44, 70) – – – 10 (6, 17) 47 (41, 53) 98 (97, 99) – – Nigeria 2018 DHS 24(23, 25) 27 (25, 29) 1 (1, 1) 38 (36, 40) 5 (4, 6) 4 (3, 5) 26 (25, 28) 70 (68, 72) 35 (32, 38) 9 (4, 18) 8 (6, 9) 11 (7, 16) 3 (1, 5) 18 (17, 20) 64 (61, 66) 57 (39, 73) 51 (48, 53) 37 (32, 43) 23 (17, 31) 19 (17, 21) 55 (53, 56) 54 (50, 57) 50 (46, 53) 35 (22, 50) Rwanda 2019 DHS 19(18, 20) 44 (41, 46) 11 (9, 13) 5 (3, 6) 5 (4, 6) 1 (1, 2) 37 (34, 40) 62 (59, 65) 64 (59, 68) 68 (61, 75) 67 (54, 78) 31 (21, 42) – 62 (58, 66) 9 (6, 12) 40 (32, 49) 10 (5, 20) 7 (3, 16) – 1 (0, 2) 13 (11, 17) 92 (84, 97) – – Senegal 2019 DHS 16(14, 17) 42 (37, 47) 0 (0, 1) 3 (2, 4) 4 (2, 6) 2 (1, 4) 50 (44, 55) 48 (43, 53) 32 (26, 38) – 13 (4, 37) 10 (2, 37) 0 (0, 0) 29 (24, 35) 3 (2, 6) – 0 (0, 0) 2 (0, 14) – 0 (0, 0) 3 (2, 5) – – – Sierra Leone 2019 DHS 17(16, 18) 66 (62, 69) 1 (1, 3) 2 (1, 3) 6 (5, 8) 1 (1, 2) 25 (22, 27) 74 (71, 77) 78 (74, 82) 31 (13, 58) 78 (50, 93) 23 (13, 36) 18 (6, 44) 73 (69, 76) 73 (68, 77) 71 (39, 91) 81 (59, 92) 57 (44, 69) 46 (17, 77) 23 (18, 30) 72 (67, 76) 31 (27, 36) 33 (23, 45) – Togo 2017 MIS 24(22, 27) 26 (22, 31) 5 (4, 8) 7 (5, 9) 3 (2, 5) 16 (12, 21) 43 (37, 49) 42 (37, 47) 78 (71, 84) 76 (60, 87) 45 (31, 60) 5 (1, 25) 4 (2, 11) 66 (60, 72) 70 (60, 79) 83 (69, 91) 54 (37, 70) 32 (14, 57) 10 (5, 17) 7 (4, 10) 66 (59, 73) 82 (74, 88) 56 (38, 73) – Uganda 2018 MIS 27(24, 30) 33 (29, 37) 7 (5, 9) 38 (34, 41) 12 (10, 15) 1 (1, 1) 13 (11, 15) 86 (84, 88) 84 (79, 88) 77 (68, 83) 48 (43, 53) 20 (15, 28) 34 (15, 60) 58 (54, 62) 72 (66, 76) 90 (84, 93) 72 (67, 77) 54 (42, 66) – 30 (23, 37) 70 (66, 74) 89 (84, 93) 87 (82, 91) – United Republic of Tanzania 2017 MIS 21(19, 22) 46 (43, 50) 0 (0, 1) 13 (11, 16) 17 (15, 20) 1 (1, 2) 25 (22, 28) 75 (71, 78) 66 (60, 71) – 76 (68, 83) 13 (8, 21) – 55 (51, 60) 34 (28, 40) – 49 (41, 57) 57 (48, 66) – 24 (19, 30) 42 (36, 47) 96 (92, 98) 83 (73, 90) – Zambia 2018 DHS 16(15, 17) 69 (66, 72) 3 (2, 5) 4 (3, 6) 0 (0, 1) 1 (0, 2) 23 (20, 26) 76 (73, 79) 78 (73, 82) 83 (64, 93) 79 (65, 89) – – 78 (73, 82) 42 (37, 47) 86 (72, 93) 54 (41, 67) – – 10 (7, 13) 44 (40, 49) 97 (95, 98) 94 (76, 99) – Notes: The analysis is presented as: point estimate (95% confidence interval). Figures with fewer than 30 children in the denominator were removed. “–” refers to not applicable or data not available. Data as of 24 October 2022 ACT: artemisinin-based combination therapy; DHS: demographic and health survey; MIS: malaria indicator survey; WHO: World Health Organization. Sources: Nationally representative household survey data from DHS and MIS, compiled through WHO calculations. 198 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 - Eb. hOUSEhOLD SURVEY RESULTS, 2017–2021, COMPILED ThROUGh WhO CALCULATIONS WHO region Country Survey Fever prevalence Health sector where treatment was sought Diagnostic testing coverage in each health sector Diagnostic testing coverage in each health sector Antimalarial treatment coverage in each health sector ACT use among antimalarial treatment in each health sector O ve ra ll Pu bl ic ex clu di ng co m m un ity h ea lth w or ke rs Co m m un ity h ea lth w or ke rs Fo rm al m ed ica l pr iva te e xc lu di ng ph ar m ac ie s Ph ar m ac ie s o r ac cr ed ite d dr ug st or es In fo rm al p riv at e No tr ea tm en t se ek in g Tr ai ne d pr ov id er Pu bl ic ex clu di ng co m m un ity h ea lth w or ke rs Co m m un ity h ea lth w or ke rs Fo rm al m ed ica l pr iva te e xc lu di ng ph ar m ac ie s Ph ar m ac ie s or a cc re di te d dr ug st or es In fo rm al p riv at e Tr ai ne d pr ov id er Pu bl ic ex clu di ng co m m un ity h ea lth w or ke rs Co m m un ity h ea lth w or ke rs Fo rm al m ed ica l pr iva te e xc lu di ng ph ar m ac ie s Ph ar m ac ie s o r ac cr ed ite d dr ug st or es Se lf- tre at m en t No tr ea tm en t se ek in g Tr ai ne d pr ov id er Pu bl ic Pr iva te In fo rm al p riv at e AFRICAN Benin 2017 DHS 20(18, 21) 22 (20, 24) 0 (0, 0) 9 (8, 11) 9 (8, 11) 14 (12, 16) 46 (43, 49) 40 (37, 43) 52 (47, 57) – 30 (23, 38) 9 (6, 14) 8 (5, 12) 37 (33, 40) 38 (34, 44) – 34 (27, 41) 23 (17, 30) 12 (9, 17) 7 (5, 9) 34 (30, 37) 44 (36, 52) 31 (24, 39) 40 (26, 55) Burkina Faso 2017 MIS 20(19, 22) 71 (67, 75) 1 (0, 1) 1 (1, 4) 0 (0, 1) 2 (1, 3) 26 (22, 30) 73 (69, 76) 66 (61, 70) – – – 25 (13, 42) 66 (61, 70) 69 (64, 73) – – – 62 (37, 82) 10 (7, 14) 68 (64, 72) 80 (76, 83) 82 (54, 95) – Cameroon 2018 DHS 16(14, 17) 20 (17, 23) 1 (0, 1) 12 (9, 15) 12 (9, 14) 21 (18, 24) 37 (33, 41) 43 (39, 47) 52 (44, 61) – 54 (43, 65) 11 (7, 19) 8 (5, 15) 42 (36, 48) 58 (49, 66) – 48 (38, 58) 33 (24, 43) 46 (38, 54) 12 (9, 16) 48 (43, 54) 25 (17, 35) 21 (15, 27) 15 (8, 27) Gambia 2019 DHS 15(14, 17) 45 (41, 49) 0 (0, 1) 7 (5, 10) 13 (10, 16) 1 (0, 2) 35 (31, 39) 64 (60, 68) 42 (37, 48) – 54 (33, 73) 27 (19, 36) – 40 (35, 45) 5 (3, 8) – 5 (1, 18) 6 (3, 14) – 1 (0, 4) 5 (3, 7) 71 (51, 85) – – Ghana 2019 MIS 30(27, 33) 34 (30, 38) 0 (0, 1) 20 (17, 24) 14 (10, 18) 3 (1, 5) 30 (26, 35) 67 (63, 71) 78 (72, 83) – 30 (22, 39) 8 (4, 16) 3 (0, 28) 50 (45, 55) 63 (56, 70) – 55 (46, 63) 57 (44, 69) 50 (24, 76) 18 (14, 24) 59 (54, 65) 88 (80, 93) 86 (77, 92) – Guinea 2021 MIS 23(21, 25) 43 (37, 48) 4 (3, 6) 6 (4, 10) 5 (3, 7) 7 (5, 10) 38 (33, 43) 56 (51, 61) 54 (48, 60) 42 (26, 61) 35 (21, 52) 7 (2, 19) 7 (3, 18) 47 (42, 53) 55 (48, 61) 70 (49, 85) 26 (15, 42) 23 (12, 40) 24 (14, 38) 9 (6, 13) 50 (44, 56) 53 (46, 60) 53 (36, 70) – Kenya 2020 MIS 17(16, 19) 43 (36, 51) 0 (0, 1) 9 (6, 14) 11 (9, 15) 1 (0, 3) 36 (30, 42) 63 (57, 69) 60 (49, 70) – 54 (33, 74) 5 (2, 13) – 49 (41, 57) 4 (2, 9) – 8 (3, 19) 4 (1, 15) – 1 (0, 2) 5 (3, 8) – – – Liberia 2019 DHS 26(23, 28) 39 (35, 44) 0 (0, 0) 14 (11, 17) 26 (22, 31) 7 (5, 9) 19 (16, 22) 76 (72, 79) 82 (77, 87) – 66 (54, 77) 25 (19, 32) 38 (25, 53) 60 (55, 65) 72 (67, 76) – 62 (51, 72) 42 (31, 53) 76 (67, 83) 22 (17, 29) 59 (54, 64) 40 (33, 46) 47 (38, 56) 34 (19, 52) Madagascar 2021 DHS 12(11, 13) 35 (32, 38) 0 (0, 0) 8 (6, 10) 1 (0, 1) 2 (2, 3) 55 (51, 58) 43 (40, 47) 46 (41, 51) – 26 (17, 37) – 4 (1, 11) 42 (38, 46) 32 (27, 37) – 13 (7, 22) – 10 (7, 14) 6 (4, 9) 28 (24, 32) 55 (46, 63) – – Malawi 2017 MIS 40(38, 43) 38 (34, 43) 3 (2, 5) 6 (4, 8) 2 (1, 4) 7 (5, 10) 46 (41, 51) 48 (43, 52) 76 (70, 82) – 76 (61, 86) 10 (2, 38) 4 (1, 14) 73 (67, 78) 55 (48, 62) – 55 (39, 69) 22 (4, 64) 21 (9, 41) 7 (5, 11) 54 (48, 61) 98 (94, 99) 97 (81, 99) – Mali 2018 DHS 16(15, 17) 24 (21, 27) 3 (2, 5) 2 (1, 3) 7 (5, 9) 23 (19, 27) 42 (38, 46) 36 (33, 39) 46 (39, 53) 37 (22, 56) 16 (5, 38) 8 (3, 17) 5 (3, 9) 36 (30, 42) 61 (54, 68) 56 (38, 72) 29 (11, 57) 17 (9, 30) 5 (2, 10) 4 (3, 6) 50 (44, 57) 35 (27, 43) 20 (9, 38) – Mauritania 2020 DHS 17(16, 18) 25 (22, 28) 0 (0, 1) 2 (1, 3) 4 (3, 5) 2 (1, 2) 68 (65, 71) 31 (28, 34) 11 (8, 15) – 25 (11, 49) 16 (7, 34) 2 (0, 14) 13 (10, 17) 48 (42, 54) – 60 (22, 89) 51 (35, 66) – 2 (2, 4) 49 (43, 55) 21 (15, 29) 15 (6, 35) – Mozambique 2018 MIS 31(28, 35) 64 (57, 70) 4 (2, 7) 0 (0, 1) 0 (0, 1) 1 (1, 3) 31 (26, 37) 68 (62, 73) 72 (67, 76) 41 (13, 76) – – – 70 (65, 74) 47 (40, 53) 57 (44, 70) – – – 10 (6, 17) 47 (41, 53) 98 (97, 99) – – Nigeria 2018 DHS 24(23, 25) 27 (25, 29) 1 (1, 1) 38 (36, 40) 5 (4, 6) 4 (3, 5) 26 (25, 28) 70 (68, 72) 35 (32, 38) 9 (4, 18) 8 (6, 9) 11 (7, 16) 3 (1, 5) 18 (17, 20) 64 (61, 66) 57 (39, 73) 51 (48, 53) 37 (32, 43) 23 (17, 31) 19 (17, 21) 55 (53, 56) 54 (50, 57) 50 (46, 53) 35 (22, 50) Rwanda 2019 DHS 19(18, 20) 44 (41, 46) 11 (9, 13) 5 (3, 6) 5 (4, 6) 1 (1, 2) 37 (34, 40) 62 (59, 65) 64 (59, 68) 68 (61, 75) 67 (54, 78) 31 (21, 42) – 62 (58, 66) 9 (6, 12) 40 (32, 49) 10 (5, 20) 7 (3, 16) – 1 (0, 2) 13 (11, 17) 92 (84, 97) – – Senegal 2019 DHS 16(14, 17) 42 (37, 47) 0 (0, 1) 3 (2, 4) 4 (2, 6) 2 (1, 4) 50 (44, 55) 48 (43, 53) 32 (26, 38) – 13 (4, 37) 10 (2, 37) 0 (0, 0) 29 (24, 35) 3 (2, 6) – 0 (0, 0) 2 (0, 14) – 0 (0, 0) 3 (2, 5) – – – Sierra Leone 2019 DHS 17(16, 18) 66 (62, 69) 1 (1, 3) 2 (1, 3) 6 (5, 8) 1 (1, 2) 25 (22, 27) 74 (71, 77) 78 (74, 82) 31 (13, 58) 78 (50, 93) 23 (13, 36) 18 (6, 44) 73 (69, 76) 73 (68, 77) 71 (39, 91) 81 (59, 92) 57 (44, 69) 46 (17, 77) 23 (18, 30) 72 (67, 76) 31 (27, 36) 33 (23, 45) – Togo 2017 MIS 24(22, 27) 26 (22, 31) 5 (4, 8) 7 (5, 9) 3 (2, 5) 16 (12, 21) 43 (37, 49) 42 (37, 47) 78 (71, 84) 76 (60, 87) 45 (31, 60) 5 (1, 25) 4 (2, 11) 66 (60, 72) 70 (60, 79) 83 (69, 91) 54 (37, 70) 32 (14, 57) 10 (5, 17) 7 (4, 10) 66 (59, 73) 82 (74, 88) 56 (38, 73) – Uganda 2018 MIS 27(24, 30) 33 (29, 37) 7 (5, 9) 38 (34, 41) 12 (10, 15) 1 (1, 1) 13 (11, 15) 86 (84, 88) 84 (79, 88) 77 (68, 83) 48 (43, 53) 20 (15, 28) 34 (15, 60) 58 (54, 62) 72 (66, 76) 90 (84, 93) 72 (67, 77) 54 (42, 66) – 30 (23, 37) 70 (66, 74) 89 (84, 93) 87 (82, 91) – United Republic of Tanzania 2017 MIS 21(19, 22) 46 (43, 50) 0 (0, 1) 13 (11, 16) 17 (15, 20) 1 (1, 2) 25 (22, 28) 75 (71, 78) 66 (60, 71) – 76 (68, 83) 13 (8, 21) – 55 (51, 60) 34 (28, 40) – 49 (41, 57) 57 (48, 66) – 24 (19, 30) 42 (36, 47) 96 (92, 98) 83 (73, 90) – Zambia 2018 DHS 16(15, 17) 69 (66, 72) 3 (2, 5) 4 (3, 6) 0 (0, 1) 1 (0, 2) 23 (20, 26) 76 (73, 79) 78 (73, 82) 83 (64, 93) 79 (65, 89) – – 78 (73, 82) 42 (37, 47) 86 (72, 93) 54 (41, 67) – – 10 (7, 13) 44 (40, 49) 97 (95, 98) 94 (76, 99) – Notes: The analysis is presented as: point estimate (95% confidence interval). Figures with fewer than 30 children in the denominator were removed. “–” refers to not applicable or data not available. Data as of 24 October 2022 ACT: artemisinin-based combination therapy; DHS: demographic and health survey; MIS: malaria indicator survey; WHO: World Health Organization. Sources: Nationally representative household survey data from DHS and MIS, compiled through WHO calculations. 199 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper AFRICAN Algeria1,2,3 2000 1 807 701 – 34 – – 2 – 2001 1 832 745 – 6 – – 1 – 2002 1 857 634 – 10 – – 0 – 2003 1 882 962 – 5 – – 0 – 2004 1 909 648 – 2 – – 0 – 2005 1 935 875 – 1 – – 0 – 2006 1 963 976 – 1 – – 0 – 2007 1 996 210 – 26 – – 0 – 2008 2 030 617 – 3 – – 0 – 2009 2 067 415 – 0 – – 0 – 2010 2 106 201 – 1 – – 1 – 2011 2 146 567 – 1 – – 0 – 2012 2 188 685 – 55 – – 0 – 2013 2 232 156 – 8 – – 0 – 2014 2 276 772 – 0 – – 0 – 2015 2 322 764 – 0 – – 0 – 2016 2 369 532 – 0 – – 0 – 2017 2 416 360 – 0 – – 0 – 2018 2 462 792 – 0 – – 0 – 2019 2 508 513 – 0 – – 0 – 2020 2 552 350 – 0 – – 0 – 2021 2 595 013 – 0 – – 0 – Angola 2000 16 394 062 3 236 000 5 339 605 8 305 000 21 100 23 478 26 200 2001 16 941 588 3 373 000 5 533 981 8 504 000 21 300 23 856 26 700 2002 17 516 140 3 325 000 5 414 605 8 296 000 19 600 21 992 24 700 2003 18 124 342 3 647 000 5 686 167 8 424 000 19 700 22 162 25 000 2004 18 771 124 4 083 000 5 889 113 8 234 000 19 500 22 190 25 200 2005 19 450 960 4 635 000 6 146 933 7 996 000 18 800 21 628 24 900 2006 20 162 340 4 693 000 6 076 182 7 752 000 17 400 20 340 23 900 2007 20 909 684 4 435 000 5 736 210 7 278 000 15 200 18 172 21 700 2008 21 691 522 3 863 000 5 063 972 6 515 000 12 600 15 179 18 400 2009 22 507 674 3 343 000 4 429 795 5 763 000 10 700 13 168 16 200 2010 23 364 184 3 010 000 4 035 439 5 309 000 9 870 12 352 15 500 2011 24 259 112 2 845 000 3 812 871 5 003 000 9 210 11 773 15 100 2012 25 188 292 2 902 000 3 884 445 5 100 000 8 800 11 463 15 000 2013 26 147 002 3 119 000 4 201 780 5 552 000 8 650 11 470 15 400 2014 27 128 336 3 416 000 4 524 454 5 923 000 8 610 11 659 16 000 2015 28 127 720 3 800 000 4 969 705 6 372 000 9 100 12 675 18 000 2016 29 154 746 4 280 000 5 523 430 6 989 000 9 510 13 553 19 800 2017 30 208 628 4 902 000 6 312 055 8 010 000 9 740 14 080 21 000 2018 31 273 532 5 140 000 6 700 267 8 519 000 9 520 13 624 20 500 2019 32 353 588 5 514 000 7 352 473 9 561 000 9 630 13 897 21 300 2020 33 428 486 5 591 000 8 410 016 12 200 000 9 990 14 853 23 700 2021 34 503 776 5 818 000 8 794 084 12 740 000 11 400 17 836 29 800 Benin 2000 6 998 023 2 153 000 2 886 309 3 813 000 7 030 7 420 7 840 2001 7 212 041 2 389 000 3 107 278 3 960 000 7 250 7 662 8 100 2002 7 431 783 2 620 000 3 371 811 4 264 000 8 220 8 693 9 200 2003 7 659 208 2 826 000 3 601 326 4 523 000 9 200 9 748 10 300 2004 7 894 554 3 088 000 3 859 662 4 763 000 10 500 11 134 11 800 2005 8 149 419 3 266 000 4 026 128 4 910 000 11 500 12 180 12 900 2006 8 402 631 3 366 000 4 140 781 5 049 000 12 300 13 078 13 900 2007 8 647 761 3 456 000 4 220 168 5 116 000 11 600 12 342 13 100 2008 8 906 469 3 413 000 4 183 971 5 108 000 10 100 10 790 11 500 2009 9 172 514 3 225 000 4 032 019 5 026 000 8 870 9 491 10 100 2010 9 445 710 3 027 000 3 862 955 4 873 000 8 110 8 694 9 320 2011 9 726 380 2 862 000 3 703 932 4 723 000 7 840 8 429 9 060 2012 10 014 078 2 831 000 3 650 774 4 614 000 8 290 8 942 9 640 2013 10 308 730 2 912 000 3 753 591 4 731 000 9 210 9 973 10 800 2014 10 614 844 3 056 000 3 917 119 4 932 000 9 680 10 522 11 400 2015 10 932 783 3 398 000 4 260 585 5 273 000 10 500 11 546 12 600 2016 11 260 085 3 833 000 4 721 694 5 784 000 10 700 11 849 13 000 2017 11 596 779 3 919 000 4 876 484 5 953 000 9 790 10 854 12 000 2018 11 940 683 3 843 000 4 872 597 6 058 000 9 320 10 428 11 700 2019 12 290 444 3 806 000 4 937 253 6 291 000 9 160 10 454 11 900 2020 12 643 123 3 327 000 4 781 051 6 563 000 8 870 10 585 12 700 2021 12 996 895 3 541 000 4 983 668 6 875 000 8 590 11 154 14 400 200 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper AFRICAN Botswana 2000 1 144 956 13 000 19 146 33 000 20 49 100 2001 1 168 124 4 700 7 698 15 000 7 19 44 2002 1 190 135 2 100 3 663 7 100 3 9 21 2003 1 211 173 750 1 862 4 500 1 4 13 2004 1 232 536 250 1 217 3 700 0 3 10 2005 1 254 893 850 1 466 2 800 1 3 8 2006 1 278 692 3 200 4 801 7 800 5 12 24 2007 1 304 066 500 1 292 3 200 0 3 9 2008 1 330 813 1 200 2 457 4 900 2 6 15 2009 1 358 444 1 300 2 719 5 100 2 6 15 2010 1 386 731 1 300 2 229 3 900 2 5 12 2011 1 414 823 520 682 950 0 1 3 2012 1 442 263 230 304 410 0 0 1 2013 1 470 010 570 725 1 000 0 1 3 2014 1 498 584 1 600 2 076 2 800 2 5 9 2015 1 528 282 350 455 620 0 1 2 2016 1 559 604 760 985 1 300 1 2 4 2017 1 592 371 2 300 2 933 4 000 3 7 13 2018 1 625 235 620 808 1 100 0 2 3 2019 1 657 252 200 259 360 0 0 1 2020 1 688 213 1 200 1 776 2 700 1 4 8 2021 1 716 072 820 1 067 1 500 1 2 4 Burkina Faso 2000 11 882 888 5 672 000 7 121 621 8 835 000 35 600 37 845 40 300 2001 12 249 764 5 865 000 7 346 774 9 048 000 38 200 40 660 43 400 2002 12 632 269 6 023 000 7 513 033 9 196 000 36 500 38 905 41 600 2003 13 030 591 6 110 000 7 615 906 9 373 000 35 200 37 642 40 300 2004 13 445 977 6 050 000 7 546 925 9 280 000 32 900 35 229 37 800 2005 13 876 127 5 819 000 7 363 398 9 204 000 30 300 32 505 34 900 2006 14 316 242 5 772 000 7 347 269 9 212 000 29 300 31 512 33 900 2007 14 757 074 6 064 000 7 693 631 9 614 000 30 100 32 545 35 100 2008 15 197 915 6 759 000 8 386 574 10 290 000 32 800 35 534 38 500 2009 15 650 022 7 147 000 8 932 652 10 950 000 28 800 31 446 34 300 2010 16 116 845 7 399 000 9 194 223 11 260 000 32 800 36 142 39 700 2011 16 602 651 7 455 000 9 242 057 11 290 000 31 400 34 912 38 700 2012 17 113 732 7 380 000 9 159 529 11 230 000 25 200 28 346 31 900 2013 17 636 408 6 941 000 8 620 220 10 590 000 23 800 27 175 31 100 2014 18 169 842 6 535 000 8 132 937 10 000 000 20 200 23 619 27 600 2015 18 718 020 6 210 000 7 848 131 9 775 000 18 400 21 940 26 200 2016 19 275 498 5 351 000 7 215 226 9 555 000 16 900 20 625 25 200 2017 19 835 858 4 906 000 7 093 584 10 000 000 16 200 20 228 25 500 2018 20 392 724 4 892 000 7 280 725 10 380 000 14 500 18 482 24 100 2019 20 951 640 5 064 000 7 472 680 10 750 000 14 700 19 228 25 900 2020 21 522 626 5 375 000 8 122 685 11 720 000 14 300 21 402 33 100 2021 22 100 684 5 527 000 8 326 915 12 050 000 13 400 18 976 28 000 Burundi 2000 6 307 659 2 059 000 2 828 604 3 815 000 10 500 11 340 12 300 2001 6 465 729 2 118 000 2 817 680 3 708 000 9 690 10 469 11 400 2002 6 648 938 2 053 000 2 777 742 3 656 000 8 500 9 186 9 970 2003 6 860 846 1 984 000 2 699 483 3 594 000 7 660 8 268 8 970 2004 7 120 496 1 809 000 2 453 417 3 258 000 6 550 7 064 7 650 2005 7 388 874 1 711 000 2 307 035 3 045 000 5 700 6 117 6 600 2006 7 658 190 1 554 000 2 161 939 2 903 000 5 320 5 699 6 130 2007 7 944 609 1 316 000 1 891 093 2 611 000 5 150 5 512 5 920 2008 8 278 109 1 164 000 1 694 813 2 379 000 5 160 5 529 5 960 2009 8 709 366 1 046 000 1 532 448 2 181 000 5 140 5 530 5 970 2010 9 126 605 1 050 000 1 544 609 2 198 000 5 080 5 470 5 930 2011 9 455 733 1 089 000 1 560 776 2 191 000 4 810 5 187 5 630 2012 9 795 479 1 163 000 1 628 919 2 219 000 4 660 5 048 5 510 2013 10 149 577 1 275 000 1 778 478 2 420 000 4 500 4 899 5 390 2014 10 494 913 1 368 000 1 916 653 2 607 000 4 530 4 990 5 560 2015 10 727 148 1 543 000 2 144 520 2 908 000 4 650 5 198 5 910 2016 10 903 327 1 856 000 2 526 223 3 358 000 5 090 5 851 6 890 2017 11 155 593 2 050 000 2 794 830 3 720 000 5 230 6 125 7 390 2018 11 493 472 2 477 000 3 315 862 4 379 000 4 600 5 299 6 320 2019 11 874 838 2 531 000 3 549 058 4 887 000 4 600 5 315 6 420 2020 12 220 227 2 352 000 3 708 079 5 573 000 4 640 5 792 7 810 2021 12 551 213 2 280 000 3 657 985 5 557 000 4 700 5 957 8 240 201 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper AFRICAN Cabo Verde1,2 2000 119 145 – 144 – – 0 – 2001 121 149 – 107 – – 0 – 2002 123 040 – 76 – – 2 – 2003 124 823 – 68 – – 4 – 2004 126 511 – 45 – – 4 – 2005 128 135 – 68 – – 2 – 2006 129 709 – 80 – – 8 – 2007 131 230 – 18 – – 2 – 2008 132 687 – 35 – – 2 – 2009 134 065 – 65 – – 2 – 2010 135 515 – 47 – – 1 – 2011 137 155 – 7 – – 1 – 2012 138 804 – 1 – – 0 – 2013 140 384 – 22 – – 0 – 2014 141 979 – 26 – – 1 – 2015 143 563 – 7 – – 0 – 2016 145 182 – 49 – – 1 – 2017 146 888 – 423 – – 2 – 2018 148 512 – 2 – – 0 – 2019 150 027 – 0 – – 0 – 2020 151 486 – 0 – – 0 – 2021 152 860 – 0 – – 0 – Cameroon 2000 15 091 594 4 858 000 6 133 781 7 672 000 15 100 16 029 17 000 2001 15 493 253 5 102 000 6 421 751 7 948 000 15 000 15 962 17 000 2002 15 914 033 5 212 000 6 530 590 8 124 000 16 200 17 289 18 400 2003 16 354 326 5 515 000 6 910 604 8 574 000 16 800 17 958 19 200 2004 16 809 408 5 789 000 7 231 963 8 913 000 17 800 19 026 20 400 2005 17 275 172 5 792 000 7 297 108 9 106 000 18 400 19 704 21 100 2006 17 751 332 5 557 000 7 222 479 9 224 000 18 200 19 474 20 900 2007 18 251 866 5 298 000 7 031 151 9 201 000 16 600 17 798 19 100 2008 18 777 080 4 832 000 6 574 374 8 732 000 14 600 15 635 16 700 2009 19 319 274 4 370 000 5 936 562 7 877 000 13 300 14 198 15 200 2010 19 878 036 4 249 000 5 688 134 7 415 000 12 200 13 110 14 000 2011 20 448 872 4 124 000 5 451 139 7 021 000 11 300 12 048 12 900 2012 21 032 684 4 070 000 5 418 988 7 102 000 11 200 12 056 12 900 2013 21 632 850 4 161 000 5 667 877 7 535 000 11 400 12 197 13 100 2014 22 299 584 4 234 000 5 825 479 7 800 000 11 600 12 438 13 400 2015 23 012 646 4 386 000 5 996 035 8 053 000 12 000 12 932 14 000 2016 23 711 630 4 604 000 6 126 789 8 036 000 12 600 13 698 15 000 2017 24 393 180 4 720 000 6 151 314 7 931 000 12 400 13 736 15 200 2018 25 076 748 4 711 000 6 218 960 8 007 000 11 500 12 898 14 500 2019 25 782 340 4 526 000 6 246 049 8 397 000 11 300 12 792 14 600 2020 26 491 088 4 187 000 6 601 170 9 884 000 11 900 14 505 17 900 2021 27 198 628 4 301 000 6 665 957 10 060 000 11 200 13 839 17 300 Central African Republic 2000 3 759 170 1 089 000 1 661 418 2 411 000 6 780 7 525 8 340 2001 3 844 773 1 104 000 1 668 386 2 458 000 6 920 7 709 8 580 2002 3 930 648 1 142 000 1 707 571 2 459 000 7 040 7 862 8 820 2003 4 026 841 1 216 000 1 767 275 2 491 000 7 470 8 391 9 480 2004 4 115 138 1 289 000 1 837 290 2 530 000 7 750 8 777 10 000 2005 4 208 834 1 297 000 1 872 113 2 610 000 8 170 9 367 10 800 2006 4 294 352 1 294 000 1 889 964 2 649 000 8 630 10 017 11 800 2007 4 375 569 1 270 000 1 891 580 2 726 000 8 410 9 890 11 800 2008 4 467 233 1 203 000 1 888 773 2 848 000 7 710 9 181 11 200 2009 4 564 540 1 116 000 1 869 529 2 926 000 7 220 8 749 10 900 2010 4 660 067 1 034 000 1 843 530 3 071 000 6 540 8 125 10 300 2011 4 732 022 987 000 1 825 670 3 070 000 5 710 7 279 9 470 2012 4 773 306 987 000 1 802 996 3 000 000 5 590 7 343 9 840 2013 4 802 428 995 000 1 782 614 2 992 000 5 050 6 813 9 450 2014 4 798 734 971 000 1 740 283 2 919 000 4 800 6 684 9 590 2015 4 819 333 901 000 1 669 623 2 837 000 4 320 6 174 9 160 2016 4 904 177 869 000 1 638 585 2 790 000 3 940 5 763 8 860 2017 4 996 741 868 000 1 642 425 2 845 000 3 680 5 533 8 740 2018 5 094 780 868 000 1 654 108 2 868 000 3 520 5 397 8 770 2019 5 209 324 862 000 1 681 948 2 945 000 3 260 5 096 8 570 2020 5 343 020 926 000 1 795 225 3 142 000 3 180 5 130 8 970 2021 5 457 154 920 000 1 826 551 3 217 000 3 120 5 151 9 270 202 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper AFRICAN Chad 2000 8 168 699 1 321 000 2 205 617 3 486 000 7 950 8 426 8 970 2001 8 445 304 1 337 000 2 291 665 3 704 000 8 550 9 069 9 670 2002 8 741 588 1 131 000 2 066 768 3 529 000 8 120 8 615 9 180 2003 9 095 665 1 125 000 2 100 279 3 539 000 8 350 8 876 9 460 2004 9 508 235 1 169 000 2 133 329 3 524 000 8 420 8 964 9 570 2005 9 895 457 1 225 000 2 232 423 3 745 000 8 840 9 428 10 100 2006 10 252 110 1 297 000 2 342 105 3 881 000 9 360 9 994 10 700 2007 10 605 317 1 449 000 2 424 988 3 836 000 9 750 10 446 11 200 2008 10 977 133 1 729 000 2 521 828 3 547 000 10 100 10 874 11 700 2009 11 370 245 2 064 000 2 687 611 3 451 000 10 600 11 421 12 400 2010 11 764 479 2 160 000 2 777 637 3 520 000 10 900 11 796 12 800 2011 12 182 850 2 022 000 2 680 861 3 489 000 10 600 11 519 12 600 2012 12 615 239 1 721 000 2 581 469 3 722 000 10 300 11 291 12 400 2013 13 072 042 1 454 000 2 516 380 4 078 000 10 100 11 132 12 300 2014 13 547 142 1 402 000 2 584 781 4 354 000 9 930 11 106 12 400 2015 13 985 437 1 495 000 2 681 805 4 506 000 9 870 11 141 12 600 2016 14 432 796 1 618 000 2 812 456 4 572 000 9 960 11 414 13 100 2017 14 920 693 1 702 000 2 961 677 4 822 000 10 000 11 657 13 700 2018 15 433 343 1 851 000 3 216 108 5 303 000 9 750 11 502 13 800 2019 15 950 276 1 854 000 3 277 966 5 393 000 9 670 11 621 14 300 2020 16 462 441 1 904 000 3 433 441 5 752 000 9 580 12 190 16 000 2021 16 991 621 1 938 000 3 506 428 5 833 000 9 320 11 744 15 400 Comoros1 2000 536 758 24 000 35 604 48 000 31 89 180 2001 547 741 24 000 35 623 48 000 30 89 180 2002 559 047 24 000 35 657 48 000 31 89 180 2003 570 130 24 000 35 658 48 000 31 89 180 2004 581 154 24 000 35 631 48 000 31 89 180 2005 592 683 24 000 35 616 48 000 31 89 180 2006 604 658 24 000 35 607 48 000 31 89 180 2007 616 899 24 000 35 591 48 000 31 89 180 2008 629 470 24 000 35 567 48 000 31 89 180 2009 642 493 24 000 35 544 48 000 30 89 180 2010 656 024 – 36 538 – 42 92 140 2011 670 071 – 24 856 – 28 62 96 2012 684 553 – 49 840 – 58 125 190 2013 699 393 – 53 156 – 63 135 210 2014 714 612 – 2 203 – 2 5 8 2015 730 216 – 1 300 – 1 3 5 2016 746 232 – 1 143 – 1 2 4 2017 761 664 – 3 230 – 3 8 12 2018 776 313 – 15 186 – 17 38 59 2019 790 986 – 17 599 – 21 45 69 2020 806 166 – 4 546 – 5 11 17 2021 821 625 – 10 537 – 12 26 41 Congo 2000 3 134 030 794 000 1 107 615 1 502 000 2 690 2 879 3 100 2001 3 254 101 784 000 1 141 982 1 604 000 2 610 2 790 3 000 2002 3 331 158 699 000 1 071 547 1 599 000 2 380 2 526 2 700 2003 3 424 653 697 000 1 093 188 1 632 000 2 370 2 513 2 680 2004 3 543 012 727 000 1 126 012 1 671 000 2 280 2 416 2 580 2005 3 672 839 707 000 1 100 055 1 619 000 2 250 2 386 2 540 2006 3 813 323 699 000 1 063 802 1 559 000 2 100 2 225 2 360 2007 3 956 329 678 000 1 015 538 1 448 000 1 960 2 065 2 180 2008 4 089 602 653 000 946 201 1 321 000 1 840 1 928 2 040 2009 4 257 230 638 000 923 124 1 295 000 1 810 1 898 2 000 2010 4 437 884 656 000 951 092 1 332 000 1 830 1 931 2 040 2011 4 584 216 688 000 987 985 1 386 000 1 850 1 970 2 100 2012 4 713 257 713 000 1 020 805 1 409 000 1 850 2 001 2 160 2013 4 828 066 733 000 1 058 288 1 473 000 1 920 2 119 2 340 2014 4 944 861 719 000 1 053 862 1 492 000 1 920 2 147 2 430 2015 5 064 386 716 000 1 068 094 1 538 000 1 820 2 024 2 300 2016 5 186 824 707 000 1 126 282 1 722 000 1 910 2 158 2 530 2017 5 312 340 739 000 1 238 519 1 975 000 1 940 2 215 2 640 2018 5 441 062 801 000 1 321 959 2 068 000 1 940 2 227 2 680 2019 5 570 733 813 000 1 324 386 2 026 000 2 020 2 360 2 930 2020 5 702 174 820 000 1 304 055 1 963 000 1 970 2 279 2 850 2021 5 835 806 800 000 1 280 047 1 949 000 1 980 2 293 2 900 203 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper AFRICAN Côte d’Ivoire 2000 16 799 670 6 671 000 8 349 017 10 370 000 30 000 32 078 34 400 2001 17 245 468 7 113 000 8 818 723 10 800 000 31 600 33 888 36 300 2002 17 683 896 7 350 000 9 068 162 11 040 000 32 100 34 372 36 900 2003 18 116 452 7 470 000 9 270 252 11 310 000 32 000 34 284 36 900 2004 18 544 904 7 509 000 9 314 093 11 450 000 30 400 32 711 35 200 2005 18 970 216 7 109 000 8 692 864 10 560 000 29 400 31 615 34 100 2006 19 394 056 6 911 000 8 496 241 10 310 000 28 900 31 237 33 700 2007 19 817 700 7 112 000 8 685 416 10 500 000 29 400 31 806 34 500 2008 20 244 448 7 515 000 9 176 757 11 090 000 28 400 30 883 33 500 2009 20 677 762 7 883 000 9 645 843 11 690 000 28 000 30 583 33 300 2010 21 120 042 8 136 000 9 969 293 12 070 000 24 900 27 301 29 900 2011 21 562 914 8 165 000 9 963 140 11 970 000 20 700 22 772 25 100 2012 22 010 712 6 556 000 8 330 987 10 440 000 15 000 16 621 18 400 2013 22 469 268 4 912 000 6 533 301 8 599 000 12 000 13 315 14 800 2014 22 995 556 4 369 000 5 955 704 7 945 000 10 900 12 233 13 700 2015 23 596 740 3 962 000 5 658 630 7 916 000 12 300 14 009 15 900 2016 24 213 622 3 722 000 5 867 998 8 772 000 14 600 16 874 19 400 2017 24 848 016 3 611 000 6 229 084 9 899 000 15 400 18 079 21 100 2018 25 493 988 3 853 000 6 730 934 10 910 000 12 600 15 035 17 800 2019 26 147 552 4 117 000 7 184 054 11 810 000 12 300 14 918 18 100 2020 26 811 790 4 158 000 7 398 482 12 240 000 11 800 15 110 19 300 2021 27 478 248 4 149 000 7 443 146 12 400 000 11 400 14 906 19 500 Democratic Republic of the Congo 2000 48 616 316 18 280 000 22 826 514 28 320 000 92 100 100 495 110 000 2001 50 106 656 18 690 000 23 541 817 29 230 000 91 700 100 200 110 000 2002 51 662 072 19 130 000 24 298 635 30 430 000 92 900 101 615 112 000 2003 53 205 640 19 960 000 25 246 571 31 260 000 96 100 105 477 116 000 2004 54 815 608 21 210 000 26 379 550 32 420 000 102 000 112 038 124 000 2005 56 550 248 22 130 000 27 239 636 33 190 000 102 000 112 726 125 000 2006 58 381 632 22 480 000 27 667 876 33 730 000 104 000 115 483 129 000 2007 60 289 424 22 940 000 27 977 616 33 950 000 99 100 111 540 125 000 2008 62 249 724 22 730 000 27 698 851 33 540 000 93 400 106 675 121 000 2009 64 270 232 21 920 000 26 605 770 32 130 000 87 500 101 411 117 000 2010 66 391 256 21 120 000 25 886 795 31 370 000 77 700 91 816 107 000 2011 68 654 272 20 460 000 25 108 174 30 590 000 66 400 79 872 94 300 2012 70 997 872 19 620 000 24 531 464 30 210 000 55 200 67 514 81 000 2013 73 460 024 19 040 000 23 984 116 29 820 000 47 600 59 282 72 500 2014 76 035 584 18 390 000 23 381 296 29 330 000 44 600 56 824 70 900 2015 78 656 904 18 210 000 23 263 844 29 360 000 44 700 58 166 74 400 2016 81 430 976 18 790 000 24 224 130 30 810 000 47 900 63 972 84 500 2017 84 283 272 20 050 000 25 976 981 33 100 000 49 500 67 810 92 300 2018 87 087 352 21 480 000 27 944 491 35 280 000 46 800 65 576 92 200 2019 89 906 888 22 130 000 28 830 863 36 930 000 45 400 65 353 94 500 2020 92 853 168 23 120 000 30 103 686 38 300 000 52 200 78 709 120 000 2021 95 894 120 23 460 000 30 518 545 38 840 000 50 600 78 847 126 000 Equatorial Guinea 2000 684 977 136 000 213 141 320 000 660 715 780 2001 719 270 151 000 219 511 310 000 660 712 780 2002 754 115 164 000 227 948 306 000 660 716 790 2003 789 681 181 000 242 764 319 000 680 739 820 2004 826 355 202 000 264 018 339 000 700 766 850 2005 864 726 222 000 287 656 368 000 700 772 860 2006 905 418 230 000 302 024 388 000 680 752 840 2007 948 814 238 000 314 016 405 000 620 698 780 2008 994 971 229 000 307 734 406 000 580 648 730 2009 1 043 686 213 000 293 747 394 000 540 610 690 2010 1 094 524 213 000 293 402 395 000 620 712 820 2011 1 144 588 219 000 306 229 419 000 630 731 850 2012 1 193 636 250 000 343 007 461 000 660 786 940 2013 1 243 941 274 000 382 479 523 000 720 873 1 060 2014 1 295 183 277 000 397 146 552 000 730 904 1 130 2015 1 346 973 271 000 406 082 584 000 650 803 1 010 2016 1 398 927 249 000 397 276 604 000 600 740 930 2017 1 450 694 223 000 380 328 608 000 610 756 980 2018 1 502 091 200 000 360 556 600 000 590 724 940 2019 1 553 031 192 000 347 501 584 000 590 726 960 2020 1 596 049 188 000 352 514 617 000 600 766 1 060 2021 1 634 466 183 000 348 428 603 000 610 785 1 120 204 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper AFRICAN Eritrea 2000 2 392 880 14 000 42 711 88 000 24 98 250 2001 2 461 927 19 000 54 461 107 000 35 130 310 2002 2 547 424 12 000 32 823 64 000 20 74 170 2003 2 653 390 23 000 49 490 87 000 35 112 240 2004 2 763 140 7 500 15 093 26 000 11 32 70 2005 2 831 732 16 000 27 680 42 000 22 59 120 2006 2 880 093 10 000 16 438 23 000 14 36 69 2007 2 926 168 24 000 37 568 53 000 25 60 110 2008 3 005 779 13 000 20 767 29 000 15 37 70 2009 3 083 888 18 000 27 656 39 000 17 41 73 2010 3 147 727 53 000 83 471 117 000 66 161 290 2011 3 207 570 49 000 76 678 107 000 57 141 260 2012 3 252 596 33 000 52 483 76 000 35 85 160 2013 3 296 367 31 000 49 309 70 000 36 88 160 2014 3 323 425 71 000 109 689 153 000 91 227 420 2015 3 340 006 41 000 64 020 89 000 51 128 240 2016 3 365 287 47 000 86 561 139 000 71 198 400 2017 3 396 933 74 000 115 928 161 000 90 221 400 2018 3 445 374 64 000 99 716 139 000 79 196 360 2019 3 498 818 129 000 200 382 279 000 170 437 820 2020 3 555 868 102 000 158 684 220 000 150 372 690 2021 3 620 312 60 000 92 898 129 000 83 211 390 Eswatini1 2000 288 538 340 787 1 400 0 2 5 2001 291 590 – 1 395 – 1 3 5 2002 294 226 – 670 – 0 1 2 2003 296 463 – 342 – 0 0 1 2004 298 413 – 574 – 0 1 2 2005 300 128 – 279 – 0 0 1 2006 301 765 – 155 – – 0 – 2007 303 522 – 84 – – 0 – 2008 305 163 – 58 – – 0 – 2009 306 568 – 106 – – 0 – 2010 307 977 – 268 – 0 0 1 2011 309 503 – 379 – 0 0 1 2012 311 204 – 409 – 0 1 1 2013 313 129 – 728 – 0 1 2 2014 315 242 – 389 – 0 0 1 2015 317 502 – 318 – 0 0 1 2016 319 906 – 250 – – 0 – 2017 322 389 – 440 – 0 1 1 2018 324 919 – 686 – 0 1 2 2019 327 491 – 235 – – 0 – 2020 330 583 – 233 – – 0 – 2021 333 835 – 306 – 0 0 1 Ethiopia 2000 45 581 670 9 009 000 15 016 444 23 740 000 10 300 25 208 47 700 2001 46 932 871 3 555 000 13 910 882 26 450 000 4 980 23 352 52 600 2002 48 329 786 3 541 000 12 570 226 23 260 000 4 730 21 590 47 000 2003 49 754 811 4 418 000 13 022 221 24 510 000 6 520 22 799 50 200 2004 51 204 696 5 365 000 12 673 710 33 330 000 6 820 23 844 72 600 2005 52 679 556 3 827 000 8 879 900 23 720 000 4 990 16 956 51 500 2006 54 189 912 3 464 000 8 345 640 23 760 000 4 200 15 221 47 500 2007 55 757 405 3 020 000 7 346 228 19 960 000 3 410 12 705 41 200 2008 57 362 830 3 037 000 6 406 358 16 430 000 3 430 11 109 32 000 2009 58 993 797 3 737 000 8 773 515 23 360 000 4 440 16 218 49 500 2010 60 681 698 4 405 000 10 362 488 27 230 000 5 200 19 144 57 400 2011 62 436 191 5 956 000 10 953 634 23 750 000 6 380 17 276 43 700 2012 64 226 870 6 611 000 13 064 346 24 140 000 8 010 20 863 45 200 2013 66 017 370 6 857 000 15 281 540 23 400 000 9 410 27 024 51 000 2014 67 827 802 3 726 000 14 339 644 28 070 000 4 680 23 862 58 100 2015 69 680 889 3 473 000 12 528 267 25 030 000 4 560 22 125 54 000 2016 71 599 397 2 833 000 8 922 403 18 580 000 3 750 16 302 40 600 2017 73 574 607 2 511 000 6 600 689 13 700 000 3 420 12 460 30 400 2018 75 568 019 1 541 000 2 793 314 4 176 000 2 330 6 500 12 800 2019 77 602 002 1 472 000 2 645 193 3 979 000 2 060 5 708 11 100 2020 79 689 820 2 351 000 4 227 742 6 366 000 3 410 9 424 18 500 2021 81 792 456 2 094 000 3 783 896 5 673 000 2 920 8 041 15 800 205 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper AFRICAN Gabon 2000 1 272 935 279 000 420 619 613 000 340 365 400 2001 1 306 590 281 000 421 598 607 000 270 292 320 2002 1 341 696 255 000 393 460 575 000 260 276 300 2003 1 378 398 218 000 342 808 505 000 230 243 260 2004 1 417 110 173 000 266 172 388 000 210 218 230 2005 1 458 353 141 000 214 915 315 000 210 218 230 2006 1 502 534 130 000 202 209 302 000 200 214 220 2007 1 549 774 123 000 200 278 310 000 200 213 220 2008 1 599 978 143 000 245 099 392 000 220 226 240 2009 1 653 542 182 000 325 788 544 000 230 243 260 2010 1 711 105 228 000 400 246 663 000 250 269 290 2011 1 772 500 259 000 452 439 738 000 280 300 330 2012 1 836 705 265 000 473 866 774 000 300 332 370 2013 1 902 226 251 000 475 337 824 000 340 378 430 2014 1 966 855 242 000 473 210 837 000 350 403 470 2015 2 028 517 243 000 473 905 838 000 350 407 480 2016 2 086 206 250 000 476 346 825 000 340 387 460 2017 2 140 215 245 000 488 279 879 000 330 383 460 2018 2 192 012 248 000 499 577 913 000 330 387 470 2019 2 242 785 269 000 512 184 897 000 330 384 470 2020 2 292 573 283 000 527 769 907 000 320 385 490 2021 2 341 179 287 000 535 939 916 000 320 384 500 Gambia 2000 1 437 539 334 000 449 173 582 000 600 632 660 2001 1 479 449 331 000 449 211 582 000 560 584 610 2002 1 522 223 333 000 449 003 581 000 520 542 570 2003 1 566 257 331 000 448 547 582 000 500 517 540 2004 1 612 225 332 000 447 989 581 000 480 498 520 2005 1 660 368 333 000 447 415 581 000 470 488 510 2006 1 711 294 331 000 447 057 580 000 470 487 510 2007 1 764 883 331 000 446 954 576 000 470 488 510 2008 1 820 542 331 000 446 959 579 000 480 492 510 2009 1 878 119 331 000 447 032 576 000 480 496 510 2010 1 937 275 332 000 447 124 577 000 500 518 540 2011 1 998 212 384 000 473 874 574 000 510 528 550 2012 2 061 014 420 000 523 991 638 000 520 536 560 2013 2 124 869 338 000 429 429 529 000 520 541 560 2014 2 189 019 237 000 303 721 376 000 530 551 570 2015 2 253 133 346 000 450 479 564 000 540 557 580 2016 2 317 206 225 000 297 058 375 000 540 562 590 2017 2 381 182 110 000 148 057 189 000 550 577 600 2018 2 444 916 143 000 196 160 253 000 570 596 630 2019 2 508 883 85 000 109 975 137 000 570 601 640 2020 2 573 995 148 000 210 897 304 000 580 608 650 2021 2 639 916 150 000 213 311 307 000 580 615 660 Ghana 2000 19 665 502 6 322 000 8 477 878 11 070 000 17 900 18 647 19 400 2001 20 195 576 6 553 000 8 428 599 10 670 000 17 600 18 294 19 000 2002 20 758 326 6 429 000 8 148 113 10 170 000 16 400 16 996 17 700 2003 21 329 514 6 182 000 7 895 135 9 933 000 16 100 16 714 17 400 2004 21 906 444 5 878 000 7 590 600 9 596 000 15 300 15 838 16 400 2005 22 496 952 5 673 000 7 350 612 9 341 000 14 900 15 430 16 000 2006 23 098 586 5 690 000 7 330 580 9 305 000 15 100 15 682 16 300 2007 23 708 320 5 889 000 7 556 676 9 540 000 14 600 15 139 15 700 2008 24 326 088 6 533 000 8 170 400 10 210 000 14 900 15 430 16 000 2009 24 950 762 7 235 000 8 947 844 10 960 000 15 800 16 413 17 100 2010 25 574 720 7 798 000 9 571 242 11 620 000 15 800 16 496 17 200 2011 26 205 940 8 137 000 9 973 490 11 990 000 16 900 17 663 18 500 2012 26 858 762 8 187 000 9 977 648 12 050 000 16 300 17 076 17 900 2013 27 525 596 7 783 000 9 540 098 11 660 000 15 600 16 352 17 100 2014 28 196 358 7 104 000 8 867 522 10 960 000 14 200 14 869 15 600 2015 28 870 940 6 263 000 7 999 986 10 090 000 12 900 13 459 14 100 2016 29 554 304 5 286 000 6 953 304 8 934 000 11 700 12 174 12 800 2017 30 222 262 4 475 000 5 991 625 7 808 000 11 300 11 839 12 500 2018 30 870 640 3 976 000 5 314 525 7 054 000 11 600 12 227 13 000 2019 31 522 290 3 917 000 5 242 734 6 942 000 11 600 12 324 13 200 2020 32 180 400 3 799 000 5 313 608 7 289 000 11 600 12 516 13 800 2021 32 833 032 3 833 000 5 396 652 7 371 000 11 600 12 557 14 000 206 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper AFRICAN Guinea 2000 8 336 967 2 705 000 3 616 058 4 733 000 15 500 16 416 17 400 2001 8 445 717 2 728 000 3 641 386 4 852 000 15 000 15 929 16 900 2002 8 577 790 2 536 000 3 556 986 4 850 000 13 600 14 464 15 400 2003 8 772 254 2 406 000 3 457 916 4 817 000 12 100 12 852 13 700 2004 8 961 039 2 228 000 3 277 416 4 643 000 10 300 10 996 11 700 2005 9 140 114 2 101 000 3 143 778 4 511 000 9 290 9 906 10 600 2006 9 330 625 2 129 000 3 153 121 4 483 000 9 070 9 681 10 300 2007 9 547 082 2 302 000 3 297 950 4 599 000 9 300 9 941 10 600 2008 9 779 785 2 692 000 3 612 353 4 728 000 10 100 10 773 11 500 2009 10 021 323 3 144 000 4 003 420 5 045 000 11 500 12 366 13 300 2010 10 270 728 3 468 000 4 320 306 5 328 000 12 400 13 305 14 400 2011 10 527 712 3 715 000 4 565 610 5 530 000 12 900 13 865 15 000 2012 10 788 692 3 884 000 4 696 591 5 631 000 12 700 13 731 14 900 2013 11 055 430 3 638 000 4 568 741 5 658 000 11 500 12 537 13 700 2014 11 333 365 3 402 000 4 460 552 5 743 000 10 600 11 602 12 800 2015 11 625 998 3 251 000 4 402 690 5 878 000 9 580 10 530 11 700 2016 11 930 985 3 073 000 4 297 908 5 822 000 8 810 9 742 10 800 2017 12 240 789 3 058 000 4 377 224 6 041 000 8 330 9 277 10 400 2018 12 554 864 3 126 000 4 508 348 6 301 000 7 590 8 500 9 630 2019 12 877 539 2 977 000 4 425 904 6 386 000 7 610 8 611 9 870 2020 13 205 153 2 698 000 4 416 575 6 876 000 8 210 9 954 12 300 2021 13 531 906 2 740 000 4 477 687 6 958 000 8 010 9 439 11 300 Guinea-Bissau 2000 1 230 849 294 000 496 631 781 000 2 120 2 296 2 510 2001 1 257 380 228 000 410 118 673 000 1 680 1 814 1 980 2002 1 285 678 168 000 323 277 570 000 1 230 1 324 1 440 2003 1 315 653 122 000 240 938 430 000 800 855 910 2004 1 347 009 88 000 166 563 286 000 720 766 820 2005 1 379 713 72 000 131 429 221 000 730 775 830 2006 1 414 091 63 000 111 165 181 000 710 756 810 2007 1 450 572 73 000 112 956 169 000 710 756 810 2008 1 488 431 99 000 134 274 178 000 720 774 840 2009 1 527 196 115 000 166 267 234 000 750 811 880 2010 1 567 220 122 000 198 836 304 000 770 839 920 2011 1 609 017 120 000 212 103 348 000 760 844 940 2012 1 652 717 108 000 213 019 379 000 740 827 930 2013 1 697 753 89 000 208 894 418 000 730 815 920 2014 1 743 309 70 000 186 564 413 000 710 800 910 2015 1 788 919 58 000 161 401 379 000 690 772 880 2016 1 834 552 47 000 139 154 326 000 720 817 950 2017 1 879 826 41 000 122 291 294 000 720 829 980 2018 1 924 955 42 000 125 653 301 000 760 880 1 060 2019 1 970 457 49 000 146 986 342 000 750 875 1 060 2020 2 015 828 61 000 189 707 453 000 770 969 1 310 2021 2 060 721 72 000 216 542 499 000 820 1 029 1 400 Kenya 2000 30 851 606 5 328 000 6 837 558 8 588 000 11 000 11 376 11 900 2001 31 800 344 6 270 000 7 808 825 9 619 000 12 600 13 118 13 700 2002 32 779 824 5 883 000 7 444 032 9 248 000 12 500 12 989 13 600 2003 33 767 120 5 627 000 7 177 403 8 995 000 12 100 12 595 13 200 2004 34 791 836 4 830 000 6 304 649 8 064 000 10 900 11 285 11 700 2005 35 843 008 3 700 000 5 018 999 6 681 000 9 750 10 098 10 500 2006 36 925 252 2 817 000 3 900 370 5 279 000 9 250 9 562 9 880 2007 38 036 792 2 216 000 3 076 957 4 175 000 9 240 9 541 9 850 2008 39 186 896 1 918 000 2 652 387 3 558 000 9 200 9 479 9 770 2009 40 364 444 1 910 000 2 593 628 3 452 000 9 340 9 618 9 920 2010 41 517 896 1 993 000 2 696 154 3 583 000 9 500 9 788 10 100 2011 42 635 144 2 105 000 2 844 501 3 761 000 9 760 10 074 10 400 2012 43 725 808 2 252 000 3 052 340 4 052 000 9 720 10 037 10 400 2013 44 792 368 2 372 000 3 232 499 4 327 000 9 720 10 058 10 400 2014 45 831 864 2 428 000 3 312 249 4 390 000 9 810 10 182 10 600 2015 46 851 488 2 421 000 3 319 230 4 397 000 9 930 10 351 10 900 2016 47 894 672 2 301 000 3 191 546 4 302 000 10 200 10 733 11 400 2017 48 948 136 2 217 000 3 155 636 4 375 000 10 600 11 223 12 100 2018 49 953 304 2 148 000 3 068 062 4 260 000 10 500 11 218 12 200 2019 50 951 448 2 168 000 3 037 541 4 130 000 10 600 11 298 12 400 2020 51 985 780 2 385 000 3 302 189 4 466 000 10 600 11 768 13 600 2021 53 005 616 2 478 000 3 419 698 4 641 000 10 800 12 011 14 000 207 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper AFRICAN Liberia 2000 2 895 224 784 000 1 248 403 1 870 000 6 020 6 426 6 860 2001 2 981 648 830 000 1 311 933 1 965 000 5 970 6 369 6 800 2002 3 060 599 838 000 1 343 961 2 049 000 5 950 6 352 6 780 2003 3 085 173 843 000 1 339 076 2 021 000 5 240 5 596 5 970 2004 3 122 447 831 000 1 270 287 1 886 000 4 400 4 688 5 000 2005 3 266 318 886 000 1 269 211 1 765 000 3 810 4 062 4 330 2006 3 455 397 956 000 1 300 190 1 722 000 3 440 3 675 3 920 2007 3 632 740 1 020 000 1 347 464 1 742 000 3 300 3 522 3 760 2008 3 783 887 1 090 000 1 403 261 1 770 000 3 300 3 525 3 770 2009 3 905 066 1 073 000 1 384 603 1 769 000 3 090 3 305 3 540 2010 4 019 956 1 037 000 1 354 819 1 748 000 2 680 2 871 3 080 2011 4 181 150 1 031 000 1 354 061 1 745 000 2 630 2 813 3 020 2012 4 331 740 981 000 1 328 628 1 758 000 2 570 2 760 2 980 2013 4 427 313 995 000 1 381 944 1 876 000 2 700 2 916 3 170 2014 4 519 398 1 106 000 1 506 350 2 024 000 3 040 3 315 3 640 2015 4 612 329 1 223 000 1 591 767 2 054 000 2 790 3 070 3 400 2016 4 706 097 1 436 000 1 773 971 2 171 000 3 380 3 789 4 300 2017 4 796 631 1 555 000 1 939 400 2 403 000 3 540 4 046 4 690 2018 4 889 391 1 496 000 1 966 210 2 550 000 2 960 3 419 4 030 2019 4 985 289 1 383 000 1 954 336 2 690 000 2 880 3 390 4 100 2020 5 087 584 1 172 000 1 914 081 2 944 000 2 820 3 931 5 620 2021 5 193 416 1 124 000 1 852 429 2 958 000 2 690 3 578 4 960 Madagascar 2000 16 216 431 84 000 905 987 1 995 000 190 2 239 5 830 2001 16 709 665 173 000 1 111 496 2 374 000 290 2 747 6 900 2002 17 211 934 24 000 898 515 2 020 000 69 2 221 5 890 2003 17 724 310 26 000 1 180 072 2 700 000 76 2 917 7 880 2004 18 250 774 30 000 834 134 1 860 000 93 2 062 5 350 2005 18 792 172 24 000 662 852 1 477 000 64 1 638 4 310 2006 19 350 300 24 000 666 523 1 455 000 71 1 647 4 300 2007 19 924 958 135 000 442 208 875 000 260 1 092 2 560 2008 20 513 600 260 000 469 371 798 000 430 1 160 2 410 2009 21 117 092 529 000 882 824 1 397 000 840 2 182 4 350 2010 21 731 052 524 000 893 540 1 440 000 820 2 208 4 460 2011 22 348 158 500 000 823 538 1 197 000 770 2 035 3 850 2012 22 966 240 972 000 1 594 592 2 509 000 1 520 3 941 7 730 2013 23 588 072 961 000 1 497 292 2 300 000 1 470 3 701 7 170 2014 24 215 976 767 000 1 078 280 1 441 000 1 130 2 665 4 720 2015 24 850 912 1 696 000 2 345 948 3 074 000 2 480 5 800 10 200 2016 25 501 940 1 034 000 1 408 501 1 840 000 1 480 3 482 6 050 2017 26 169 542 1 431 000 1 971 342 2 576 000 2 060 4 873 8 610 2018 26 846 540 1 356 000 1 921 403 2 541 000 1 960 4 750 8 550 2019 27 533 134 1 390 000 2 032 961 2 723 000 2 050 5 026 9 120 2020 28 225 176 2 660 000 4 010 537 5 479 000 4 050 10 265 18 800 2021 28 915 652 3 126 000 4 911 293 6 833 000 4 890 12 571 23 400 Malawi 2000 11 229 387 3 985 000 5 068 222 6 336 000 17 300 18 170 19 100 2001 11 498 818 4 274 000 5 289 453 6 504 000 16 100 16 919 17 800 2002 11 784 498 4 086 000 5 157 186 6 371 000 13 700 14 390 15 100 2003 12 087 965 3 777 000 4 851 733 6 108 000 11 500 12 078 12 600 2004 12 411 342 3 631 000 4 633 730 5 816 000 10 100 10 600 11 100 2005 12 755 648 3 728 000 4 716 725 5 875 000 9 620 10 078 10 600 2006 13 118 307 3 709 000 4 638 347 5 718 000 9 970 10 424 10 900 2007 13 495 463 3 765 000 4 731 580 5 843 000 10 700 11 175 11 700 2008 13 889 423 4 113 000 5 115 871 6 313 000 11 000 11 588 12 200 2009 14 298 932 4 398 000 5 457 399 6 648 000 10 900 11 492 12 200 2010 14 718 422 4 539 000 5 615 374 6 873 000 10 200 10 817 11 500 2011 15 146 094 4 460 000 5 570 026 6 906 000 9 400 9 906 10 500 2012 15 581 251 3 896 000 5 053 353 6 421 000 8 580 9 015 9 520 2013 16 024 775 3 566 000 4 685 666 6 075 000 7 530 7 925 8 390 2014 16 477 966 3 306 000 4 353 265 5 658 000 6 930 7 365 7 870 2015 16 938 942 3 115 000 4 106 623 5 354 000 6 520 7 023 7 600 2016 17 405 624 2 975 000 3 949 459 5 105 000 6 370 6 954 7 630 2017 17 881 168 2 961 000 3 911 675 5 053 000 6 350 7 024 7 850 2018 18 367 884 2 893 000 3 859 499 5 054 000 6 280 7 021 7 990 2019 18 867 336 2 661 000 3 867 128 5 358 000 6 260 7 061 8 160 2020 19 377 060 2 503 000 4 252 631 6 787 000 6 300 7 400 9 200 2021 19 889 742 2 557 000 4 359 158 6 888 000 6 310 7 392 9 230 208 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper AFRICAN Mali 2000 11 239 101 3 100 000 4 565 653 6 579 000 23 400 24 739 26 200 2001 11 583 824 3 192 000 4 719 788 6 804 000 21 000 22 250 23 600 2002 11 952 660 3 328 000 4 977 784 7 234 000 21 100 22 339 23 700 2003 12 342 165 3 492 000 5 230 718 7 473 000 20 700 21 913 23 200 2004 12 751 995 3 741 000 5 475 357 7 761 000 21 300 22 551 23 900 2005 13 180 551 3 946 000 5 683 984 8 192 000 22 400 23 776 25 300 2006 13 623 541 3 844 000 5 527 211 7 850 000 22 000 23 397 24 900 2007 14 080 912 3 825 000 5 474 736 7 702 000 20 400 21 774 23 200 2008 14 551 117 3 813 000 5 515 727 7 710 000 18 000 19 237 20 600 2009 15 032 635 3 905 000 5 630 658 7 774 000 16 000 17 172 18 400 2010 15 529 181 4 268 000 5 957 047 8 204 000 16 100 17 356 18 700 2011 16 039 734 4 634 000 6 491 808 8 858 000 18 700 20 247 22 000 2012 16 514 687 5 122 000 7 194 633 9 768 000 21 900 23 944 26 200 2013 17 004 032 5 513 000 7 699 696 10 580 000 23 200 25 545 28 100 2014 17 551 814 5 562 000 7 740 479 10 730 000 20 500 22 863 25 400 2015 18 112 908 5 034 000 7 097 168 10 060 000 15 800 17 783 20 000 2016 18 700 106 5 063 000 7 184 988 10 200 000 12 900 14 736 16 800 2017 19 311 356 5 279 000 7 469 199 10 620 000 13 300 15 442 17 900 2018 19 934 298 5 441 000 7 710 138 10 950 000 15 500 18 423 21 800 2019 20 567 424 4 844 000 6 863 255 9 742 000 15 300 18 630 22 600 2020 21 224 040 5 354 000 7 586 538 10 780 000 15 100 20 662 28 000 2021 21 904 984 5 463 000 7 744 735 11 010 000 14 600 19 933 27 400 Mauritania 2000 2 695 003 147 000 304 757 559 000 890 937 990 2001 2 761 823 153 000 307 700 552 000 850 900 950 2002 2 821 703 130 000 308 961 579 000 830 880 940 2003 2 883 326 136 000 319 661 628 000 820 877 940 2004 2 946 575 90 000 286 610 557 000 790 845 910 2005 3 012 360 77 000 281 841 563 000 820 880 960 2006 3 081 229 61 000 258 559 552 000 800 874 960 2007 3 153 508 64 000 246 899 519 000 790 863 960 2008 3 233 336 50 000 209 089 423 000 790 867 970 2009 3 322 616 3 600 108 111 256 000 790 874 990 2010 3 419 461 6 900 123 348 275 000 790 869 990 2011 3 524 249 35 000 162 490 343 000 810 902 1 040 2012 3 636 113 5 600 88 074 212 000 840 939 1 100 2013 3 742 959 5 800 101 247 237 000 840 953 1 130 2014 3 843 174 75 000 216 687 431 000 870 996 1 210 2015 3 946 220 107 000 291 679 578 000 890 1 032 1 280 2016 4 051 890 162 000 386 314 723 000 910 1 049 1 310 2017 4 160 015 112 000 306 475 614 000 920 1 069 1 340 2018 4 270 712 98 000 236 485 436 000 930 1 085 1 380 2019 4 383 849 65 000 184 057 356 000 950 1 114 1 440 2020 4 498 604 69 000 201 771 395 000 970 1 129 1 460 2021 4 614 974 40 000 85 030 145 000 980 1 144 1 500 Mozambique 2000 17 768 504 6 834 000 8 416 877 10 240 000 41 500 44 699 48 100 2001 18 220 716 7 454 000 8 997 785 10 840 000 41 000 44 168 47 600 2002 18 694 946 7 613 000 9 283 388 11 120 000 38 000 40 972 44 200 2003 19 186 754 7 634 000 9 278 676 11 180 000 34 200 36 977 39 900 2004 19 694 412 7 385 000 8 967 650 10 870 000 29 500 31 912 34 500 2005 20 211 114 6 982 000 8 517 561 10 330 000 25 100 27 220 29 400 2006 20 735 982 6 796 000 8 325 146 10 090 000 25 200 27 304 29 500 2007 21 280 512 6 739 000 8 256 754 10 030 000 24 000 26 006 28 100 2008 21 845 572 6 821 000 8 345 403 10 100 000 23 600 25 587 27 700 2009 22 436 660 6 916 000 8 469 429 10 330 000 24 000 25 990 28 200 2010 23 073 724 7 027 000 8 660 053 10 530 000 24 000 26 140 28 600 2011 23 760 420 7 196 000 8 783 904 10 700 000 23 300 25 686 28 500 2012 24 487 612 7 374 000 9 032 885 11 010 000 22 400 25 176 28 500 2013 25 251 732 7 595 000 9 291 989 11 360 000 22 100 25 425 29 500 2014 26 038 704 7 549 000 9 315 947 11 400 000 21 000 24 756 29 700 2015 26 843 246 7 652 000 9 403 454 11 440 000 19 600 23 519 29 000 2016 27 696 492 7 668 000 9 501 504 11 610 000 18 600 22 857 29 200 2017 28 569 440 7 590 000 9 422 965 11 580 000 17 000 21 403 28 100 2018 29 423 878 7 453 000 9 406 462 11 690 000 14 900 19 200 25 900 2019 30 285 596 7 228 000 9 421 431 12 000 000 14 500 19 156 26 700 2020 31 178 240 7 028 000 9 990 474 13 770 000 14 700 23 693 40 300 2021 32 077 072 7 172 000 10 283 922 14 230 000 13 900 22 291 37 800 209 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper AFRICAN Namibia 2000 1 443 943 31 000 77 408 155 000 56 198 470 2001 1 473 519 56 000 102 902 185 000 93 263 580 2002 1 499 016 33 000 70 395 139 000 56 180 420 2003 1 520 368 28 000 67 564 140 000 48 172 430 2004 1 539 403 51 000 105 493 202 000 89 270 630 2005 1 558 024 34 000 65 303 118 000 57 167 370 2006 1 576 830 41 000 68 176 113 000 66 174 350 2007 1 596 621 6 300 20 663 47 000 11 52 150 2008 1 618 100 1 600 12 094 33 000 2 30 97 2009 1 641 410 790 6 068 18 000 1 15 53 2010 1 666 296 800 2 590 6 200 1 6 19 2011 1 692 544 2 100 3 092 4 800 3 7 15 2012 1 720 429 2 200 5 465 9 400 3 13 32 2013 1 749 829 6 200 7 844 9 600 8 20 33 2014 1 780 382 20 000 25 780 32 000 28 65 110 2015 1 811 896 12 000 15 052 18 000 17 38 65 2016 1 844 160 25 000 32 052 39 000 36 82 140 2017 1 876 848 71 000 89 155 109 000 100 228 380 2018 1 909 508 40 000 50 217 61 000 56 128 220 2019 1 942 023 4 100 5 705 8 100 6 14 26 2020 1 975 721 16 000 20 258 25 000 22 51 87 2021 2 008 307 17 000 21 322 26 000 23 54 92 Niger 2000 11 622 665 2 317 000 4 134 706 6 854 000 24 400 26 182 28 100 2001 12 031 430 2 346 000 4 180 737 6 946 000 21 600 23 238 25 000 2002 12 456 517 2 278 000 4 048 859 6 616 000 19 900 21 344 23 000 2003 12 900 790 2 266 000 3 825 282 6 086 000 17 800 19 133 20 600 2004 13 366 885 2 525 000 4 016 648 6 010 000 16 600 17 876 19 300 2005 13 855 221 2 688 000 4 271 200 6 443 000 18 500 20 011 21 600 2006 14 365 168 2 712 000 4 386 050 6 753 000 17 800 19 349 21 000 2007 14 897 873 2 984 000 4 668 666 6 934 000 18 400 20 076 21 900 2008 15 455 175 3 756 000 5 403 729 7 532 000 19 200 21 260 23 500 2009 16 037 915 4 323 000 6 017 879 8 204 000 21 300 23 951 26 900 2010 16 647 543 4 821 000 6 785 885 9 379 000 23 100 26 471 30 300 2011 17 283 112 5 094 000 7 283 090 10 040 000 24 000 28 225 33 000 2012 17 954 408 5 207 000 7 633 594 10 810 000 24 000 28 977 34 700 2013 18 653 200 5 056 000 7 717 689 11 360 000 23 200 28 864 35 800 2014 19 372 014 5 052 000 7 880 779 11 660 000 21 900 27 969 35 700 2015 20 128 124 5 143 000 8 073 585 12 100 000 19 900 26 184 34 500 2016 20 921 744 5 142 000 8 053 568 12 120 000 19 300 26 110 35 500 2017 21 737 922 5 016 000 7 883 346 11 850 000 17 100 23 815 33 400 2018 22 577 058 4 896 000 7 760 340 11 740 000 15 400 22 047 32 200 2019 23 443 392 4 731 000 7 631 511 11 760 000 15 300 22 568 34 200 2020 24 333 640 4 583 000 7 845 383 12 750 000 15 400 24 068 39 200 2021 25 252 722 4 723 000 8 166 369 13 300 000 15 300 24 997 42 700 Nigeria 2000 122 851 984 39 480 000 50 779 020 64 300 000 236 000 249 259 263 000 2001 126 152 680 39 830 000 51 229 713 65 120 000 229 000 242 479 256 000 2002 129 583 024 39 530 000 50 952 482 64 780 000 218 000 230 464 244 000 2003 133 119 800 40 940 000 52 423 482 66 420 000 213 000 226 442 240 000 2004 136 756 848 43 690 000 55 028 747 68 160 000 213 000 226 548 240 000 2005 140 490 720 46 230 000 57 411 916 70 620 000 217 000 230 350 244 000 2006 144 329 760 47 900 000 59 418 382 72 790 000 218 000 232 391 247 000 2007 148 294 032 49 660 000 61 400 329 75 070 000 215 000 229 178 244 000 2008 152 382 512 51 780 000 63 355 538 76 750 000 207 000 221 435 237 000 2009 156 595 760 51 800 000 63 192 869 76 430 000 196 000 210 275 226 000 2010 160 952 848 49 030 000 60 551 056 73 900 000 183 000 197 612 214 000 2011 165 463 744 46 920 000 58 184 509 71 320 000 169 000 184 121 200 000 2012 170 075 936 44 970 000 55 718 235 68 540 000 158 000 173 344 191 000 2013 174 726 128 43 380 000 53 921 321 66 320 000 152 000 168 943 188 000 2014 179 379 008 42 810 000 53 483 458 65 930 000 147 000 165 188 186 000 2015 183 995 792 43 300 000 54 115 123 67 110 000 143 000 163 088 188 000 2016 188 666 928 44 060 000 55 667 334 69 130 000 143 000 165 672 195 000 2017 193 495 904 45 870 000 57 869 533 72 050 000 150 000 176 539 212 000 2018 198 387 616 46 930 000 59 652 248 75 230 000 156 000 186 940 231 000 2019 203 304 496 47 440 000 61 379 283 78 810 000 152 000 187 428 239 000 2020 208 327 408 46 800 000 65 133 759 88 650 000 151 000 195 720 263 000 2021 213 401 328 47 520 000 65 399 501 89 000 000 146 000 193 512 268 000 210 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper AFRICAN Rwanda 2000 8 109 989 745 000 1 536 115 2 793 000 6 480 6 769 7 080 2001 8 223 941 756 000 1 478 868 2 630 000 6 180 6 459 6 760 2002 8 372 306 708 000 1 375 805 2 495 000 5 100 5 320 5 560 2003 8 567 992 610 000 1 231 753 1 987 000 4 230 4 393 4 570 2004 8 791 853 455 000 1 101 660 1 815 000 3 600 3 718 3 850 2005 9 026 299 385 000 1 385 594 2 311 000 3 230 3 334 3 450 2006 9 270 066 360 000 1 396 437 2 213 000 3 040 3 129 3 230 2007 9 523 168 501 000 840 559 1 264 000 2 950 3 031 3 120 2008 9 781 996 425 000 683 661 1 003 000 2 900 2 984 3 070 2009 10 043 737 1 007 000 1 546 660 2 169 000 2 880 2 956 3 040 2010 10 309 031 747 000 1 079 765 1 422 000 2 900 2 982 3 080 2011 10 576 932 291 000 390 611 494 000 2 850 2 936 3 030 2012 10 840 334 511 000 646 386 788 000 2 820 2 903 3 000 2013 11 101 350 1 014 000 1 214 623 1 429 000 2 810 2 894 3 000 2014 11 368 451 1 727 000 2 299 121 2 895 000 2 820 2 904 3 010 2015 11 642 959 2 667 000 3 585 563 4 538 000 2 850 2 948 3 070 2016 11 930 899 3 602 000 4 887 836 6 231 000 2 910 3 027 3 180 2017 12 230 339 6 334 000 8 681 013 11 160 000 2 950 3 086 3 270 2018 12 531 808 4 523 000 6 184 131 7 923 000 2 940 3 093 3 310 2019 12 835 028 3 862 000 5 279 485 6 772 000 2 970 3 143 3 400 2020 13 146 362 2 177 000 2 986 047 3 831 000 3 010 3 205 3 530 2021 13 461 888 1 242 000 1 700 493 2 184 000 3 050 3 258 3 630 Sao Tome and Principe1,2 2000 143 714 – 31 975 – – 254 – 2001 146 258 – 42 086 – – 248 – 2002 149 841 – 50 586 – – 321 – 2003 153 762 – 42 656 – – 193 – 2004 157 697 – 46 486 – – 169 – 2005 161 680 – 18 139 – – 85 – 2006 165 725 – 5 146 – – 26 – 2007 169 845 – 2 421 – – 3 – 2008 174 004 – 6 258 – – 16 – 2009 178 128 – 6 182 – – 23 – 2010 182 138 – 3 146 – – 14 – 2011 186 044 – 8 442 – – 19 – 2012 189 924 – 12 550 – – 7 – 2013 193 757 – 9 243 – – 11 – 2014 197 497 – 1 754 – – 0 – 2015 201 124 – 2 056 – – 0 – 2016 204 632 – 2 238 – – 1 – 2017 208 036 – 2 239 – – 1 – 2018 211 344 – 2 937 – – 0 – 2019 214 599 – 2 732 – – 0 – 2020 218 641 – 1 933 – – 0 – 2021 223 107 – 2 719 – – 1 – Senegal 2000 9 704 287 1 762 000 2 676 527 3 648 000 5 890 6 114 6 350 2001 9 938 027 669 000 2 470 115 3 713 000 5 590 5 796 6 010 2002 10 180 950 505 000 2 248 602 3 810 000 4 610 4 753 4 900 2003 10 434 504 726 000 2 063 074 3 342 000 4 720 4 867 5 020 2004 10 698 691 308 000 1 393 434 2 581 000 4 310 4 435 4 560 2005 10 974 057 441 000 1 362 379 2 275 000 3 950 4 056 4 160 2006 11 263 387 442 000 1 376 263 2 459 000 3 830 3 926 4 020 2007 11 563 869 361 000 1 160 145 2 134 000 3 760 3 850 3 940 2008 11 872 929 396 000 628 766 914 000 3 770 3 862 3 950 2009 12 195 029 276 000 405 713 550 000 3 760 3 847 3 940 2010 12 530 121 536 000 763 134 1 012 000 3 750 3 833 3 920 2011 12 875 880 437 000 626 038 831 000 3 720 3 799 3 880 2012 13 231 833 489 000 706 020 954 000 3 640 3 712 3 790 2013 13 595 566 567 000 793 415 1 058 000 3 670 3 741 3 820 2014 13 970 308 390 000 532 782 694 000 3 780 3 857 3 940 2015 14 356 181 684 000 1 010 929 1 370 000 3 820 3 910 4 010 2016 14 751 356 467 000 684 129 924 000 3 810 3 904 4 020 2017 15 157 793 559 000 805 691 1 073 000 3 870 3 987 4 130 2018 15 574 909 731 000 1 047 659 1 405 000 4 040 4 192 4 390 2019 16 000 781 455 000 672 221 913 000 4 070 4 251 4 490 2020 16 436 120 565 000 835 455 1 131 000 4 110 4 311 4 580 2021 16 876 720 674 000 995 763 1 347 000 4 160 4 375 4 680 211 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper AFRICAN Sierra Leone 2000 4 584 067 1 336 000 2 081 568 3 098 000 11 900 12 666 13 500 2001 4 857 096 1 414 000 2 201 474 3 271 000 13 000 13 847 14 700 2002 5 140 113 1 427 000 2 269 445 3 436 000 13 000 13 834 14 700 2003 5 350 907 1 406 000 2 246 838 3 467 000 12 300 13 061 13 900 2004 5 533 329 1 411 000 2 267 452 3 451 000 11 800 12 513 13 300 2005 5 683 334 1 388 000 2 270 241 3 575 000 10 700 11 356 12 100 2006 5 809 774 1 356 000 2 277 054 3 631 000 10 200 10 844 11 600 2007 5 939 163 1 490 000 2 396 659 3 650 000 10 500 11 216 12 000 2008 6 090 860 1 876 000 2 654 929 3 670 000 11 900 12 713 13 600 2009 6 259 842 2 161 000 2 881 379 3 778 000 13 300 14 159 15 100 2010 6 436 698 2 222 000 2 977 797 3 884 000 14 400 15 377 16 500 2011 6 612 385 2 266 000 3 009 591 3 916 000 14 000 15 008 16 100 2012 6 788 587 2 324 000 2 986 182 3 751 000 13 000 14 021 15 100 2013 6 964 859 2 344 000 2 921 184 3 620 000 12 000 13 009 14 100 2014 7 140 688 2 265 000 2 868 023 3 563 000 10 400 11 317 12 400 2015 7 314 773 2 252 000 2 857 523 3 601 000 9 940 10 884 12 000 2016 7 493 913 2 304 000 2 914 796 3 616 000 8 720 9 659 10 700 2017 7 677 565 2 239 000 2 883 985 3 668 000 7 830 8 760 9 790 2018 7 861 281 2 165 000 2 901 372 3 819 000 6 550 7 408 8 370 2019 8 046 828 1 953 000 2 857 585 4 016 000 6 330 7 236 8 300 2020 8 233 970 1 646 000 2 795 827 4 477 000 6 260 9 020 12 600 2021 8 420 641 1 596 000 2 777 394 4 538 000 5 940 8 314 11 400 South Africa1,2 2000 4 681 326 13 000 18 206 26 000 – 424 – 2001 4 722 971 – 26 506 – – 81 – 2002 4 766 151 – 15 649 – – 96 – 2003 4 810 404 – 13 459 – – 142 – 2004 4 855 607 – 13 399 – – 88 – 2005 4 901 714 – 7 755 – – 63 – 2006 4 949 175 – 12 098 – – 87 – 2007 4 999 609 – 6 327 – – 37 – 2008 5 056 581 – 7 796 – – 43 – 2009 5 117 078 – 6 072 – – 45 – 2010 5 178 492 – 8 060 – – 83 – 2011 5 244 332 – 9 866 – – 54 – 2012 5 314 503 – 6 621 – – 72 – 2013 5 387 361 – 8 645 – – 105 – 2014 5 472 955 – 11 705 – – 174 – 2015 5 587 650 – 4 959 – – 110 – 2016 5 642 227 – 4 323 – – 34 – 2017 5 664 120 – 23 381 – – 301 – 2018 5 733 963 – 9 562 – – 69 – 2019 5 808 705 – 4 821 – – 79 – 2020 5 880 192 – 4 463 – – 38 – 2021 5 939 225 – 2 958 – – 56 – South Sudan4 2000 6 114 440 1 533 000 2 245 004 3 162 000 9 630 10 974 12 500 2001 6 394 431 1 488 000 2 283 971 3 398 000 9 240 10 504 11 900 2002 6 686 100 1 410 000 2 250 591 3 418 000 8 320 9 420 10 700 2003 6 992 367 1 421 000 2 229 084 3 353 000 7 760 8 771 9 920 2004 7 317 118 1 514 000 2 243 889 3 253 000 7 150 8 090 9 180 2005 7 662 654 1 618 000 2 342 844 3 292 000 6 640 7 547 8 600 2006 8 029 517 1 710 000 2 450 862 3 393 000 6 290 7 226 8 290 2007 8 417 823 1 819 000 2 520 885 3 395 000 5 920 6 861 7 950 2008 8 823 888 2 000 000 2 586 745 3 267 000 5 670 6 687 7 860 2009 9 229 227 2 099 000 2 640 300 3 268 000 5 320 6 387 7 630 2010 9 714 419 2 183 000 2 775 374 3 450 000 5 010 6 108 7 390 2011 10 243 050 2 308 000 2 979 272 3 782 000 4 760 5 900 7 270 2012 10 701 604 2 358 000 3 079 924 3 971 000 4 640 5 840 7 330 2013 11 106 031 2 399 000 3 173 418 4 121 000 4 590 5 925 7 640 2014 11 213 284 2 282 000 3 098 291 4 115 000 4 660 6 232 8 330 2015 11 194 299 2 108 000 3 033 662 4 190 000 4 720 6 542 9 090 2016 11 066 105 1 901 000 2 949 646 4 401 000 4 760 6 890 10 000 2017 10 658 226 1 791 000 2 960 681 4 578 000 4 690 7 134 10 900 2018 10 395 329 1 695 000 2 898 120 4 628 000 4 400 6 869 11 000 2019 10 447 666 1 652 000 2 938 499 4 811 000 4 380 7 095 11 900 2020 10 606 227 1 664 000 3 042 784 5 116 000 4 340 7 290 12 800 2021 10 748 272 1 643 000 2 953 582 4 972 000 4 200 7 344 13 600 212 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper AFRICAN Togo 2000 5 008 035 1 614 000 2 221 914 2 995 000 4 900 5 177 5 490 2001 5 145 426 1 700 000 2 289 405 3 019 000 4 980 5 270 5 590 2002 5 281 538 1 806 000 2 382 832 3 104 000 5 400 5 723 6 080 2003 5 421 001 1 957 000 2 518 511 3 171 000 5 630 5 978 6 360 2004 5 565 218 2 123 000 2 657 728 3 295 000 6 100 6 497 6 920 2005 5 711 597 2 152 000 2 693 615 3 327 000 6 420 6 843 7 300 2006 5 874 240 2 115 000 2 660 724 3 317 000 6 210 6 626 7 080 2007 6 047 537 2 020 000 2 566 605 3 203 000 5 660 6 042 6 470 2008 6 222 482 1 780 000 2 307 220 2 934 000 4 670 4 984 5 330 2009 6 398 624 1 588 000 2 050 314 2 609 000 3 990 4 253 4 540 2010 6 571 855 1 551 000 1 984 921 2 513 000 3 780 4 046 4 330 2011 6 748 672 1 581 000 2 034 724 2 574 000 3 800 4 076 4 380 2012 6 926 635 1 801 000 2 295 985 2 867 000 4 190 4 526 4 890 2013 7 106 229 2 106 000 2 609 672 3 177 000 4 720 5 138 5 600 2014 7 288 383 2 179 000 2 688 180 3 267 000 4 870 5 356 5 880 2015 7 473 229 2 163 000 2 669 469 3 274 000 4 670 5 177 5 730 2016 7 661 354 1 986 000 2 488 548 3 091 000 3 960 4 414 4 920 2017 7 852 795 1 692 000 2 179 253 2 774 000 3 390 3 793 4 260 2018 8 046 679 1 497 000 2 014 395 2 660 000 3 240 3 667 4 180 2019 8 243 094 1 351 000 1 966 638 2 775 000 3 220 3 703 4 310 2020 8 442 580 1 236 000 2 049 459 3 196 000 3 160 3 745 4 570 2021 8 644 829 1 246 000 2 053 345 3 216 000 3 090 3 715 4 650 Uganda 2000 24 020 696 9 001 000 11 703 496 14 900 000 42 500 44 688 47 000 2001 24 763 324 9 580 000 12 579 134 16 060 000 48 500 51 069 53 800 2002 25 545 090 9 848 000 12 776 442 16 360 000 46 100 48 487 51 000 2003 26 354 736 9 768 000 12 758 978 16 370 000 41 600 43 827 46 100 2004 27 146 084 9 362 000 12 210 978 15 630 000 35 800 37 670 39 600 2005 27 946 588 9 381 000 12 118 906 15 490 000 27 800 29 135 30 600 2006 28 773 228 9 271 000 12 122 459 15 580 000 24 200 25 409 26 700 2007 29 629 804 9 526 000 12 308 621 15 730 000 23 400 24 542 25 800 2008 30 509 862 10 080 000 12 751 923 15 880 000 25 600 26 929 28 400 2009 31 412 520 10 610 000 13 289 108 16 390 000 27 300 28 832 30 400 2010 32 341 728 10 580 000 13 241 888 16 340 000 25 500 26 913 28 400 2011 33 295 738 10 370 000 13 141 152 16 350 000 22 400 23 606 24 900 2012 34 273 296 10 170 000 12 905 814 16 150 000 18 600 19 568 20 700 2013 35 273 568 8 926 000 11 722 973 15 070 000 15 200 15 938 16 800 2014 36 336 540 7 964 000 10 693 203 14 110 000 14 400 15 148 16 000 2015 37 477 356 6 650 000 9 501 063 12 970 000 14 800 15 704 16 800 2016 38 748 300 7 796 000 10 971 082 14 470 000 17 000 18 494 20 300 2017 40 127 084 7 821 000 11 833 607 16 340 000 18 500 20 560 23 200 2018 41 515 396 7 261 000 10 905 744 17 280 000 15 500 17 114 19 300 2019 42 949 080 7 420 000 11 282 360 17 810 000 15 500 17 282 19 800 2020 44 404 612 8 333 000 12 602 807 19 840 000 15 700 20 279 27 800 2021 45 853 776 8 637 000 13 023 397 20 610 000 15 700 19 663 26 400 United Republic of Tanzania 2000 34 463 704 9 392 000 12 072 965 15 090 000 40 700 42 579 44 700 2001 35 414 468 9 448 000 12 064 438 15 210 000 38 200 40 009 41 900 2002 36 353 532 8 826 000 11 478 532 14 640 000 33 400 34 901 36 500 2003 37 333 920 8 430 000 11 088 120 14 240 000 30 200 31 635 33 100 2004 38 360 880 8 086 000 10 574 049 13 660 000 27 000 28 232 29 500 2005 39 439 504 7 651 000 9 949 128 12 810 000 24 800 25 833 27 000 2006 40 562 052 6 980 000 9 232 996 11 950 000 23 100 24 055 25 100 2007 41 716 496 6 160 000 8 298 126 10 990 000 21 900 22 751 23 700 2008 42 870 884 5 486 000 7 417 960 9 901 000 21 500 22 293 23 200 2009 43 957 932 4 999 000 6 797 118 9 043 000 21 200 21 981 22 900 2010 45 110 528 4 659 000 6 350 485 8 422 000 20 900 21 764 22 700 2011 46 416 032 4 415 000 5 992 159 8 018 000 20 500 21 356 22 300 2012 47 786 136 4 348 000 5 841 953 7 779 000 20 000 20 879 21 800 2013 49 253 644 4 775 000 6 440 590 8 504 000 20 700 21 744 22 900 2014 50 814 552 5 387 000 7 267 262 9 511 000 21 200 22 378 23 700 2015 52 542 824 5 516 000 7 382 963 9 735 000 21 600 22 917 24 500 2016 54 401 800 5 324 000 7 151 258 9 366 000 22 100 23 689 25 600 2017 56 267 032 5 188 000 7 003 374 9 204 000 22 100 23 852 26 100 2018 58 090 444 5 082 000 6 852 548 9 022 000 22 400 24 278 26 900 2019 59 872 580 5 051 000 7 040 493 9 508 000 22 600 24 699 27 700 2020 61 704 520 4 974 000 7 604 750 10 990 000 22 800 25 464 29 500 2021 63 588 336 5 300 000 7 997 711 11 620 000 23 000 25 787 30 300 213 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper AFRICAN Zambia 2000 9 891 136 2 989 000 3 927 883 5 045 000 11 900 12 485 13 200 2001 10 191 964 3 233 000 4 119 122 5 158 000 11 900 12 569 13 200 2002 10 508 294 3 021 000 3 903 346 4 956 000 10 500 10 981 11 500 2003 10 837 973 2 894 000 3 777 289 4 879 000 9 600 10 055 10 500 2004 11 188 040 2 576 000 3 376 477 4 342 000 8 360 8 759 9 170 2005 11 564 870 2 316 000 2 988 921 3 805 000 7 360 7 702 8 050 2006 11 971 567 2 061 000 2 663 591 3 403 000 6 840 7 118 7 420 2007 12 402 073 1 842 000 2 399 017 3 054 000 6 520 6 767 7 030 2008 12 852 966 1 714 000 2 217 906 2 805 000 6 450 6 688 6 940 2009 13 318 087 1 712 000 2 200 401 2 773 000 6 480 6 720 6 980 2010 13 792 086 1 811 000 2 323 628 2 922 000 6 440 6 675 6 930 2011 14 265 814 2 024 000 2 575 149 3 259 000 6 790 7 047 7 330 2012 14 744 658 2 318 000 2 954 071 3 705 000 7 160 7 458 7 780 2013 15 234 976 2 727 000 3 467 685 4 340 000 7 630 7 974 8 360 2014 15 737 793 2 864 000 3 656 181 4 594 000 7 980 8 385 8 840 2015 16 248 230 2 796 000 3 563 606 4 453 000 7 820 8 231 8 700 2016 16 767 761 2 607 000 3 453 634 4 480 000 7 660 8 104 8 620 2017 17 298 054 2 257 000 3 204 416 4 410 000 7 510 8 011 8 610 2018 17 835 892 2 156 000 3 189 765 4 575 000 7 520 8 107 8 870 2019 18 380 476 2 122 000 3 205 531 4 623 000 7 560 8 244 9 190 2020 18 927 716 2 217 000 3 537 563 5 289 000 7 590 8 846 10 800 2021 19 473 124 2 316 000 3 655 518 5 482 000 7 580 8 806 10 800 Zimbabwe 2000 9 318 943 240 000 987 103 2 453 000 440 2 526 7 240 2001 9 379 025 226 000 993 468 2 460 000 470 2 543 7 270 2002 9 437 032 226 000 999 612 2 445 000 490 2 559 7 160 2003 9 508 833 232 000 1 007 217 2 517 000 480 2 578 7 440 2004 9 575 806 217 000 1 014 311 2 486 000 480 2 596 7 340 2005 9 626 100 228 000 1 019 639 2 515 000 460 2 610 7 480 2006 9 709 360 236 000 1 028 458 2 582 000 490 2 632 7 750 2007 9 803 913 718 000 1 690 025 2 968 000 1 340 4 326 9 310 2008 9 882 482 119 000 688 884 1 367 000 270 1 763 4 240 2009 9 984 424 298 000 763 956 1 343 000 560 1 955 4 240 2010 10 110 382 608 000 1 097 776 1 733 000 1 000 2 810 5 630 2011 10 256 854 472 000 722 030 993 000 730 1 848 3 430 2012 10 445 479 405 000 592 559 794 000 620 1 516 2 760 2013 10 673 905 618 000 863 692 1 126 000 910 2 211 3 960 2014 10 910 394 808 000 1 093 059 1 399 000 1 180 2 798 4 870 2015 11 145 979 726 000 1 067 121 1 450 000 1 090 2 731 4 970 2016 11 380 449 501 000 760 626 1 052 000 780 1 947 3 600 2017 11 615 415 833 000 1 337 886 1 931 000 1 300 3 424 6 530 2018 11 852 496 392 000 634 718 896 000 630 1 624 3 100 2019 12 090 632 471 000 782 740 1 125 000 750 2 003 3 860 2020 12 338 718 693 000 1 152 901 1 662 000 1 080 2 951 5 680 2021 12 593 732 205 000 342 543 492 000 320 876 1 680 AMERICAS Argentina1,2,3 2000 185 353 – 440 – – 0 – 2001 187 402 – 215 – – 0 – 2002 189 425 – 125 – – 0 – 2003 191 390 – 124 – – 1 – 2004 193 343 – 116 – – 0 – 2005 195 352 – 231 – – 0 – 2006 197 384 – 172 – – 0 – 2007 199 380 – 328 – – 0 – 2008 201 368 – 105 – – 0 – 2009 203 421 – 92 – – 0 – 2010 205 500 – 54 – – 0 – 2011 207 603 – 0 – – 0 – 2012 209 761 – 0 – – 0 – 2013 211 941 – 0 – – 0 – 2014 214 120 – 0 – – 0 – 2015 216 285 – 0 – – 0 – 2016 218 341 – 0 – – 0 – 2017 220 273 – 0 – – 0 – 2018 222 067 – 0 – – 0 – 2019 223 727 – 0 – – 0 – 2020 225 180 – 0 – – 0 – 2021 226 383 – 0 – – 0 – 214 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper AMERICAS Belize1,2 2000 165 880 – 1 486 – – 0 – 2001 171 189 – 1 162 – – 0 – 2002 176 631 – 1 134 – – 0 – 2003 182 158 – 1 324 – – 0 – 2004 187 768 – 1 066 – – 1 – 2005 193 458 – 1 549 – – 0 – 2006 199 223 – 844 – – 1 – 2007 205 049 – 845 – – 0 – 2008 210 912 – 540 – – 0 – 2009 216 777 – 256 – – 0 – 2010 222 253 – 150 – – 0 – 2011 227 381 – 72 – – 0 – 2012 232 570 – 33 – – 0 – 2013 237 834 – 20 – – 0 – 2014 243 111 – 19 – – 0 – 2015 248 310 – 9 – – 0 – 2016 253 445 – 4 – – 0 – 2017 258 538 – 7 – – 0 – 2018 263 625 – 3 – – 0 – 2019 268 475 – 0 – – 0 – 2020 272 495 – 0 – – 0 – 2021 276 021 – 0 – – 0 – Bolivia (Plurinational State of)5 2000 3 898 230 34 000 45 647 58 000 11 24 41 2001 3 967 835 17 000 22 330 28 000 4 9 18 2002 4 037 927 15 000 19 768 25 000 3 9 15 2003 4 109 060 22 000 27 568 34 000 5 12 21 2004 4 181 149 16 000 20 206 25 000 3 9 15 2005 4 254 239 21 000 27 296 33 000 5 12 21 2006 4 329 219 20 000 25 742 32 000 6 14 23 2007 4 405 658 15 000 19 799 24 000 5 11 19 2008 4 482 528 10 000 13 210 16 000 3 6 11 2009 4 559 980 10 000 13 344 16 000 2 6 10 2010 4 637 990 15 000 18 659 23 000 4 10 16 2011 4 716 464 7 600 9 680 12 000 1 4 7 2012 4 795 154 7 900 10 048 12 000 1 4 7 2013 4 873 935 8 300 10 906 14 000 3 6 11 2014 4 952 709 8 400 10 994 14 000 1 4 8 2015 5 031 238 7 300 9 315 11 000 1 3 6 2016 5 109 692 5 900 7 510 9 200 0 2 5 2017 5 187 958 4 800 6 195 7 600 0 2 4 2018 5 265 704 5 700 7 239 8 900 0 2 4 2019 5 343 014 9 900 12 654 16 000 1 4 8 2020 5 415 078 13 000 16 506 20 000 2 6 11 2021 5 480 094 12 000 14 930 18 000 1 5 10 Brazil2 2000 35 702 366 644 000 760 760 887 000 – 245 – 2001 36 177 013 406 000 467 114 535 000 – 142 – 2002 36 636 767 363 000 407 200 458 000 – 95 – 2003 37 073 743 425 000 465 651 513 000 – 104 – 2004 37 498 575 481 000 516 739 561 000 – 102 – 2005 37 919 857 624 000 658 276 709 000 – 123 – 2006 38 330 599 561 000 584 183 624 000 – 110 – 2007 38 728 229 478 000 534 516 578 000 – 93 – 2008 39 112 480 322 000 335 694 359 000 – 68 – 2009 39 487 063 316 000 328 858 351 000 – 85 – 2010 39 859 758 349 000 390 024 422 000 – 76 – 2011 40 231 615 273 000 284 024 303 000 – 70 – 2012 40 595 475 248 000 258 095 276 000 – 60 – 2013 40 949 517 177 000 197 679 214 000 – 40 – 2014 41 302 308 141 000 146 599 156 000 – 36 – 2015 41 653 206 147 000 164 589 178 000 – 35 – 2016 41 992 495 127 000 132 022 141 000 – 35 – 2017 42 326 506 198 000 220 913 239 000 – 34 – 2018 42 663 818 196 000 218 813 237 000 – 56 – 2019 42 991 924 160 000 178 650 193 000 – 37 – 2020 43 278 849 150 000 167 113 181 000 – 51 – 2021 43 508 223 145 000 162 016 175 000 – 58 – 215 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper AMERICAS Colombia2 2000 8 681 838 154 000 210 720 269 000 – 124 – 2001 8 819 707 246 000 333 024 423 000 – 168 – 2002 8 956 121 218 000 291 432 367 000 – 162 – 2003 9 089 761 193 000 254 224 319 000 – 118 – 2004 9 220 510 151 000 197 464 246 000 – 126 – 2005 9 347 293 129 000 166 899 207 000 – 87 – 2006 9 469 494 127 000 165 418 205 000 – 77 – 2007 9 587 644 133 000 173 191 215 000 – 68 – 2008 9 700 271 84 000 109 966 137 000 – 54 – 2009 9 810 657 84 000 110 555 138 000 – 28 – 2010 9 921 838 125 000 164 564 206 000 – 42 – 2011 10 030 937 68 000 90 130 113 000 – 23 – 2012 10 135 769 64 000 84 176 105 000 – 24 – 2013 10 236 614 55 000 72 346 91 000 – 10 – 2014 10 334 030 43 000 57 024 71 000 – 17 – 2015 10 431 836 63 000 84 841 109 000 – 18 – 2016 10 543 910 87 000 115 550 145 000 – 36 – 2017 10 704 576 60 000 80 963 104 000 – 19 – 2018 10 909 426 69 000 93 827 121 000 – 9 – 2019 11 110 990 89 000 119 761 154 000 – 3 – 2020 11 275 539 81 000 105 996 132 000 – 5 – 2021 11 405 251 78 000 102 418 128 000 – 11 – Costa Rica1,2 2000 1 392 717 – 1 879 – – 0 – 2001 1 418 627 – 1 363 – – 0 – 2002 1 442 918 – 1 021 – – 0 – 2003 1 466 013 – 718 – – 0 – 2004 1 488 480 – 1 289 – – 0 – 2005 1 510 560 – 3 541 – – 0 – 2006 1 532 360 – 2 903 – – 0 – 2007 1 554 006 – 1 223 – – 0 – 2008 1 575 672 – 966 – – 0 – 2009 1 597 094 – 262 – – 1 – 2010 1 617 788 – 110 – – 0 – 2011 1 637 974 – 10 – – 0 – 2012 1 657 807 – 6 – – 0 – 2013 1 677 037 – 0 – – 0 – 2014 1 695 500 – 0 – – 0 – 2015 1 713 334 – 0 – – 0 – 2016 1 730 821 – 4 – – 0 – 2017 1 747 844 – 12 – – 0 – 2018 1 764 256 – 70 – – 0 – 2019 1 779 586 – 95 – – 0 – 2020 1 793 086 – 90 – – 0 – 2021 1 803 884 – 189 – – 0 – Dominican Republic2 2000 4 704 438 1 300 1 524 1 800 – 6 – 2001 4 775 080 1 100 1 315 1 600 – 17 – 2002 4 844 517 1 400 1 685 2 000 – 11 – 2003 4 913 232 1 600 1 983 2 400 – 12 – 2004 4 981 135 2 500 3 046 3 600 – 16 – 2005 5 048 137 4 000 4 950 5 900 – 16 – 2006 5 113 905 3 700 4 535 5 400 – 10 – 2007 5 178 923 2 900 3 478 4 100 – 17 – 2008 5 245 430 1 900 2 365 2 800 – 11 – 2009 5 314 344 1 700 2 115 2 500 – 14 – 2010 5 384 681 2 600 3 202 3 800 – 15 – 2011 5 455 176 1 700 2 088 2 500 – 10 – 2012 5 525 210 1 000 1 232 1 500 – 8 – 2013 5 594 706 500 613 730 – 5 – 2014 5 663 594 480 566 660 – 4 – 2015 5 731 740 660 778 900 – 3 – 2016 5 798 808 720 851 990 – 1 – 2017 5 864 714 360 420 490 – 1 – 2018 5 929 869 450 534 620 – 1 – 2019 5 993 958 1 400 1 592 1 800 – 4 – 2020 6 058 834 870 1 019 1 200 – 2 – 2021 6 123 946 320 378 440 – 1 – 216 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper AMERICAS Ecuador1,2 2000 367 936 – 104 528 – – 66 – 2001 374 318 – 108 903 – – 84 – 2002 380 877 – 86 757 – – 64 – 2003 387 596 – 52 065 – – 46 – 2004 394 398 – 28 730 – – 37 – 2005 401 258 – 17 050 – – 22 – 2006 408 224 – 9 863 – – 9 – 2007 415 298 – 8 194 – – 8 – 2008 422 436 – 4 891 – – 5 – 2009 429 604 – 4 126 – – 6 – 2010 436 796 – 1 888 – – 0 – 2011 444 027 – 1 219 – – 0 – 2012 451 200 – 544 – – 0 – 2013 458 167 – 368 – – 0 – 2014 465 015 – 242 – – 0 – 2015 471 948 – 618 – – 0 – 2016 479 049 – 1 191 – – 0 – 2017 486 548 – 1 275 – – 0 – 2018 495 836 – 1 653 – – 0 – 2019 505 396 – 1 803 – – 0 – 2020 512 531 – 1 934 – – 3 – 2021 518 626 – 2 175 – – 0 – El Salvador1,2,3 2000 1 209 571 – 753 – – 0 – 2001 1 215 583 – 362 – – 0 – 2002 1 220 288 – 117 – – 0 – 2003 1 223 450 – 85 – – 0 – 2004 1 225 237 – 112 – – 0 – 2005 1 225 676 – 67 – – 0 – 2006 1 224 990 – 49 – – 0 – 2007 1 226 958 – 40 – – 0 – 2008 1 231 824 – 33 – – 0 – 2009 1 236 511 – 21 – – 0 – 2010 1 241 148 – 17 – – 0 – 2011 1 245 881 – 7 – – 0 – 2012 1 250 741 – 13 – – 0 – 2013 1 255 685 – 6 – – 0 – 2014 1 260 533 – 6 – – 0 – 2015 1 264 906 – 5 – – 0 – 2016 1 268 853 – 12 – – 0 – 2017 1 272 130 – 0 – – 0 – 2018 1 274 097 – 0 – – 0 – 2019 1 274 884 – 0 – – 0 – 2020 1 277 424 – 0 – – 0 – 2021 1 281 775 – 0 – – 0 – French Guiana 2000 90 932 3 900 4 428 5 300 4 9 15 2001 94 831 4 000 4 554 5 400 4 9 15 2002 98 887 3 800 4 348 5 200 3 8 13 2003 103 024 4 000 4 540 5 300 4 9 15 2004 107 167 3 200 3 580 4 200 3 7 11 2005 111 352 3 600 4 015 4 700 2 5 9 2006 115 009 4 300 4 796 5 600 2 5 9 2007 118 016 5 000 5 647 6 600 2 5 8 2008 120 901 3 500 3 884 4 500 1 4 5 2009 123 671 3 600 4 051 4 700 1 3 6 2010 126 398 1 700 2 092 2 700 1 4 6 2011 129 263 1 300 1 413 1 600 1 2 4 2012 132 368 940 1 054 1 200 0 2 3 2013 135 758 910 1 025 1 200 0 1 3 2014 139 078 480 541 620 0 0 1 2015 142 207 410 462 530 – 0 – 2016 145 409 240 268 310 – 0 – 2017 149 027 610 685 790 – 0 – 2018 153 063 570 640 740 – 0 – 2019 157 171 220 248 290 – 0 – 2020 160 987 150 164 190 – 0 – 2021 164 572 77 86 100 – 0 – 217 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper AMERICAS Guatemala 2000 8 859 661 56 000 63 676 76 000 11 27 45 2001 9 065 980 37 000 42 680 51 000 7 18 30 2002 9 277 858 37 000 42 213 50 000 9 20 32 2003 9 489 724 32 000 36 810 43 000 7 16 26 2004 9 702 560 30 000 34 124 40 000 6 14 23 2005 9 914 223 42 000 46 748 55 000 8 20 32 2006 10 125 293 32 000 36 610 43 000 6 15 25 2007 10 339 841 16 000 17 992 21 000 2 6 11 2008 10 553 367 7 500 8 421 9 800 1 3 5 2009 10 764 714 7 400 8 285 9 600 1 3 5 2010 10 978 892 7 800 9 468 12 000 1 3 6 2011 11 198 070 7 100 7 968 9 200 1 2 5 2012 11 420 660 5 600 6 262 7 300 0 2 3 2013 11 642 875 6 500 7 282 8 400 0 2 4 2014 11 862 616 5 100 5 764 6 600 0 2 3 2015 12 079 555 5 800 6 482 7 500 0 2 4 2016 12 298 404 5 200 5 857 6 700 0 2 3 2017 12 515 609 4 300 4 832 5 600 0 1 3 2018 12 720 534 3 200 3 541 4 100 0 1 2 2019 12 913 916 2 200 2 428 2 800 0 0 1 2020 13 107 463 1 100 1 241 1 400 – 0 – 2021 13 292 996 1 300 1 493 1 700 – 0 – Guyana 2000 759 051 28 000 33 628 40 000 23 50 80 2001 759 809 31 000 37 974 46 000 25 52 85 2002 760 323 25 000 30 656 37 000 21 42 69 2003 760 562 32 000 38 681 46 000 26 53 86 2004 760 424 33 000 40 416 48 000 25 51 83 2005 759 709 45 000 54 583 65 000 34 69 110 2006 758 367 24 000 27 629 32 000 19 40 62 2007 756 521 13 000 15 697 18 000 9 20 31 2008 754 150 14 000 16 365 19 000 11 23 37 2009 751 258 14 000 17 877 22 000 13 28 45 2010 747 932 24 000 29 631 35 000 24 52 83 2011 744 230 32 000 38 863 46 000 33 72 120 2012 743 966 36 000 43 572 52 000 36 76 120 2013 747 420 43 000 57 459 79 000 41 90 160 2014 751 115 17 000 22 310 30 000 13 27 48 2015 755 031 14 000 18 030 25 000 10 22 38 2016 759 087 14 000 19 317 26 000 11 25 43 2017 763 252 19 000 25 167 34 000 16 34 58 2018 785 514 23 000 30 769 42 000 18 39 67 2019 798 753 22 000 26 307 31 000 15 30 47 2020 797 202 19 000 22 159 26 000 14 28 44 2021 804 567 22 000 25 792 30 000 14 27 43 Haiti 2000 7 469 192 42 000 72 190 116 000 68 184 370 2001 7 604 548 42 000 73 498 120 000 69 188 380 2002 7 738 398 43 000 74 792 121 000 69 191 380 2003 7 873 035 44 000 76 093 123 000 72 194 400 2004 8 006 373 44 000 77 382 125 000 74 198 400 2005 8 140 753 45 000 78 681 127 000 74 201 400 2006 8 278 687 48 000 81 199 129 000 78 207 410 2007 8 416 754 43 000 73 292 115 000 70 187 380 2008 8 554 717 54 000 89 554 141 000 87 229 460 2009 8 693 546 48 000 84 023 137 000 80 215 440 2010 8 793 825 48 000 84 993 138 000 81 217 440 2011 8 893 381 49 000 81 483 126 000 79 208 410 2012 9 031 170 40 000 65 545 101 000 65 167 330 2013 9 167 566 38 000 62 551 97 000 62 160 310 2014 9 302 950 22 000 33 119 45 000 33 84 160 2015 9 437 871 21 000 32 185 44 000 32 82 150 2016 9 571 966 24 000 36 279 49 000 36 92 170 2017 9 705 706 22 000 33 122 45 000 34 84 160 2018 9 838 717 11 000 16 000 22 000 16 40 75 2019 9 970 958 12 000 17 703 24 000 18 45 82 2020 10 101 722 27 000 40 876 57 000 40 104 190 2021 10 227 487 14 000 22 546 35 000 22 57 110 218 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper AMERICAS Honduras 2000 6 029 661 38 000 51 498 66 000 9 23 41 2001 6 193 734 26 000 35 405 45 000 6 15 27 2002 6 358 632 18 000 25 251 32 000 4 11 19 2003 6 523 463 15 000 20 706 27 000 3 9 16 2004 6 687 890 18 000 25 353 33 000 4 12 20 2005 6 852 026 17 000 23 468 30 000 4 11 20 2006 7 015 602 13 000 17 218 22 000 3 8 14 2007 7 177 977 11 000 14 962 19 000 3 7 13 2008 7 338 589 8 900 11 741 15 000 2 6 9 2009 7 497 580 9 900 12 900 16 000 3 8 14 2010 7 654 855 10 000 13 308 16 000 3 7 12 2011 7 810 264 8 100 10 269 13 000 2 5 8 2012 7 964 126 6 800 8 680 11 000 1 4 7 2013 8 116 563 5 700 7 231 8 900 2 5 9 2014 8 268 002 3 600 4 553 5 600 1 3 5 2015 8 418 962 3 800 4 792 5 900 1 4 6 2016 8 569 590 4 300 5 519 6 800 2 5 8 2017 8 719 993 1 300 1 716 2 100 0 0 1 2018 8 870 363 710 933 1 200 – 0 – 2019 9 020 707 350 444 540 – 0 – 2020 9 168 292 860 1 098 1 300 0 0 1 2021 9 310 124 1 800 2 290 2 900 1 2 4 Mexico1,2 2000 2 074 916 – 7 390 – – 0 – 2001 2 107 158 – 4 996 – – 0 – 2002 2 139 442 – 4 624 – – 0 – 2003 2 171 502 – 3 819 – – 0 – 2004 2 203 651 – 3 406 – – 0 – 2005 2 235 378 – 2 967 – – 0 – 2006 2 265 999 – 2 514 – – 0 – 2007 2 296 023 – 2 361 – – 0 – 2008 2 325 311 – 2 357 – – 0 – 2009 2 354 247 – 2 703 – – 0 – 2010 2 385 686 – 1 226 – – 0 – 2011 2 419 990 – 1 124 – – 0 – 2012 2 454 025 – 833 – – 0 – 2013 2 486 562 – 495 – – 0 – 2014 2 517 624 – 656 – – 0 – 2015 2 547 177 – 517 – – 0 – 2016 2 576 207 – 551 – – 0 – 2017 2 604 192 – 736 – – 0 – 2018 2 629 093 – 803 – – 0 – 2019 2 651 808 – 618 – – 0 – 2020 2 671 164 – 356 – – 0 – 2021 2 686 148 – 242 – – 0 – Nicaragua2 2000 2 236 286 25 000 29 953 35 000 – 4 – 2001 2 266 641 11 000 13 275 16 000 – 2 – 2002 2 295 556 8 100 9 745 11 000 – 8 – 2003 2 323 516 7 100 8 507 10 000 – 7 – 2004 2 351 086 7 300 8 735 10 000 – 1 – 2005 2 380 966 7 000 8 412 9 900 – 6 – 2006 2 413 762 3 300 3 943 4 600 – 1 – 2007 2 447 653 1 400 1 717 2 000 – 0 – 2008 2 482 700 800 965 1 100 – 0 – 2009 2 518 883 640 772 910 – 0 – 2010 2 556 027 730 876 1 000 – 1 – 2011 2 593 924 970 1 171 1 400 – 1 – 2012 2 632 359 1 300 1 564 1 800 – 2 – 2013 2 671 108 1 200 1 471 1 700 – 0 – 2014 2 710 087 1 200 1 446 1 700 – 0 – 2015 2 749 338 2 400 2 886 3 400 – 1 – 2016 2 788 901 6 600 7 943 9 400 – 2 – 2017 2 828 752 11 000 13 866 16 000 – 1 – 2018 2 868 779 17 000 20 158 24 000 – 3 – 2019 2 908 802 14 000 16 717 20 000 – 1 – 2020 2 948 948 27 000 32 469 38 000 – 0 – 2021 2 990 260 24 000 29 457 35 000 – 0 – 219 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper AMERICAS Panama2 2000 2 903 964 1 000 1 091 1 200 – 1 – 2001 2 961 326 940 977 1 000 – 1 – 2002 3 019 339 2 300 2 363 2 500 – 2 – 2003 3 077 983 4 600 4 739 5 100 – 4 – 2004 3 137 676 5 200 5 365 5 700 – 2 – 2005 3 198 195 3 700 3 861 4 100 – 1 – 2006 3 258 858 1 700 1 751 1 900 – 1 – 2007 3 319 846 1 300 1 349 1 400 – 1 – 2008 3 381 434 750 783 840 – 1 – 2009 3 443 415 790 819 870 – 0 – 2010 3 505 595 420 440 470 – 1 – 2011 3 568 533 360 372 400 – 0 – 2012 3 632 566 860 888 950 – 1 – 2013 3 697 087 710 740 790 – 0 – 2014 3 762 135 960 1 005 1 100 – 0 – 2015 3 828 216 550 575 610 – 0 – 2016 3 895 198 780 809 860 – 0 – 2017 3 962 654 760 801 860 – 0 – 2018 4 029 592 710 789 850 – 0 – 2019 4 094 678 1 800 1 849 2 000 – 0 – 2020 4 154 527 2 000 2 049 2 200 – 0 – 2021 4 209 546 4 400 4 585 4 900 – 0 – Paraguay1,2,3 2000 184 457 – 6 853 – – 0 – 2001 187 615 – 2 706 – – 0 – 2002 190 314 – 2 775 – – 0 – 2003 192 717 – 1 388 – – 0 – 2004 194 987 – 692 – – 0 – 2005 197 167 – 373 – – 0 – 2006 199 248 – 820 – – 0 – 2007 201 245 – 1 337 – – 0 – 2008 203 225 – 331 – – 0 – 2009 205 292 – 80 – – 0 – 2010 207 670 – 20 – – 0 – 2011 210 381 – 1 – – 0 – 2012 213 239 – 0 – – 0 – 2013 216 203 – 0 – – 0 – 2014 219 265 – 0 – – 0 – 2015 222 406 – 0 – – 0 – 2016 225 598 – 0 – – 0 – 2017 228 794 – 0 – – 0 – 2018 231 959 – 0 – – 0 – 2019 235 080 – 0 – – 0 – 2020 238 273 – 0 – – 0 – 2021 241 336 – 0 – – 0 – Peru 2000 10 468 797 78 000 103 335 132 000 51 106 170 2001 10 610 375 83 000 105 067 128 000 45 89 140 2002 10 735 899 105 000 128 960 155 000 55 108 170 2003 10 849 343 93 000 111 816 132 000 49 95 150 2004 10 955 612 98 000 115 387 134 000 51 98 150 2005 11 055 120 92 000 108 134 125 000 41 80 120 2006 11 146 952 68 000 80 054 93 000 25 52 80 2007 11 233 088 53 000 62 633 73 000 21 43 68 2008 11 313 916 47 000 54 608 63 000 17 34 53 2009 11 393 702 45 000 52 035 60 000 15 31 48 2010 11 480 206 33 000 37 847 43 000 9 20 31 2011 11 577 669 26 000 30 924 36 000 9 19 30 2012 11 684 448 33 000 40 437 48 000 11 24 38 2013 11 798 042 51 000 62 669 75 000 23 45 71 2014 11 921 818 69 000 83 936 100 000 30 60 94 2015 12 062 391 71 000 86 896 104 000 36 70 110 2016 12 227 710 60 000 72 836 87 000 35 69 110 2017 12 413 370 60 000 72 752 87 000 32 64 100 2018 12 648 421 48 000 58 455 70 000 24 48 75 2019 12 892 292 33 000 45 729 64 000 17 35 61 2020 13 080 775 22 000 29 745 41 000 11 23 41 2021 13 242 088 19 000 23 250 28 000 9 17 28 220 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper AMERICAS Suriname1,2 2000 70 748 – 11 361 – – 24 – 2001 71 988 – 16 003 – – 23 – 2002 73 209 – 12 837 – – 15 – 2003 74 408 – 10 982 – – 18 – 2004 75 411 – 8 378 – – 7 – 2005 76 245 – 9 131 – – 1 – 2006 77 102 – 3 289 – – 1 – 2007 77 977 – 1 104 – – 1 – 2008 78 862 – 2 086 – – 0 – 2009 79 756 – 2 499 – – 0 – 2010 80 656 – 1 771 – – 1 – 2011 81 551 – 795 – – 1 – 2012 82 432 – 569 – – 0 – 2013 83 294 – 525 – – 1 – 2014 84 142 – 401 – – 1 – 2015 84 997 – 81 – – 0 – 2016 85 880 – 78 – – 0 – 2017 86 782 – 137 – – 1 – 2018 87 691 – 30 – – 0 – 2019 88 664 – 104 – – 0 – 2020 89 663 – 156 – – 0 – 2021 90 537 – 22 – – 0 – Venezuela (Bolivarian Republic of) 2000 12 213 864 31 000 35 517 42 000 13 26 41 2001 12 440 102 21 000 23 834 28 000 7 15 24 2002 12 665 464 31 000 35 029 42 000 9 19 30 2003 12 891 014 33 000 37 510 44 000 14 27 42 2004 13 113 464 49 000 54 984 65 000 15 31 48 2005 13 334 392 47 000 52 979 62 000 17 34 52 2006 13 551 040 39 000 43 638 51 000 16 33 50 2007 13 762 548 44 000 48 834 57 000 19 37 57 2008 13 966 916 33 000 37 482 43 000 13 26 41 2009 14 163 946 37 000 41 927 48 000 18 36 54 2010 14 357 511 48 000 57 905 73 000 27 53 84 2011 14 548 080 48 000 53 565 62 000 24 47 71 2012 14 735 213 55 000 61 850 71 000 29 56 86 2013 14 919 010 82 000 92 159 106 000 54 105 160 2014 15 096 629 95 000 106 079 123 000 57 110 170 2015 15 264 858 142 000 159 661 184 000 77 150 230 2016 15 370 732 251 000 281 897 325 000 140 261 400 2017 15 281 716 429 000 482 617 555 000 220 424 640 2018 14 912 826 423 000 475 212 548 000 190 373 570 2019 14 485 842 415 000 467 421 538 000 180 351 530 2020 14 245 226 198 000 223 349 258 000 100 196 300 2021 14 099 934 182 000 204 683 237 000 80 156 240 EASTERN MEDITERRANEAN Afghanistan 2000 15 063 925 843 000 1 312 939 2 022 000 410 965 1 880 2001 15 176 194 849 000 1 312 939 2 007 000 400 965 1 880 2002 16 187 207 916 000 1 382 972 2 106 000 530 1 133 2 040 2003 17 455 092 810 000 1 242 906 1 916 000 370 798 1 440 2004 18 155 313 480 000 716 954 1 072 000 150 350 660 2005 18 816 390 315 000 535 477 860 000 100 259 510 2006 19 611 675 221 000 418 216 717 000 84 222 450 2007 19 966 522 238 000 452 626 776 000 88 236 490 2008 20 370 350 211 000 402 909 709 000 72 198 410 2009 21 108 869 149 000 274 981 455 000 53 139 280 2010 21 728 881 166 000 292 015 459 000 67 165 320 2011 22 545 541 200 000 362 319 571 000 74 192 360 2012 23 483 867 122 000 220 653 356 000 31 92 190 2013 24 312 278 143 000 279 634 446 000 46 129 250 2014 25 217 981 221 000 319 142 443 000 66 152 270 2015 26 017 535 253 000 368 235 514 000 74 174 320 2016 26 697 935 532 000 733 205 985 000 150 351 610 2017 27 474 301 608 000 798 871 1 022 000 160 378 640 2018 28 278 539 485 000 633 500 810 000 130 292 490 2019 29 113 108 319 000 413 480 525 000 67 170 300 2020 30 040 186 195 000 253 150 321 000 45 110 190 2021 30 909 067 149 000 193 232 245 000 41 91 150 221 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper EASTERN MEDITERRANEAN Djibouti1 2000 556 465 1 300 1 731 2 100 1 4 8 2001 574 055 1 400 1 786 2 200 1 4 8 2002 591 783 1 400 1 841 2 300 1 4 8 2003 604 743 1 400 1 881 2 300 1 4 8 2004 613 714 1 500 1 909 2 300 1 4 9 2005 623 078 1 500 1 938 2 400 1 4 9 2006 635 142 1 500 1 976 2 400 1 5 9 2007 648 827 1 600 2 018 2 500 1 5 9 2008 662 093 1 600 2 060 2 500 2 5 9 2009 675 754 – 2 686 – 3 6 10 2010 689 325 – 1 010 – 1 2 3 2011 702 532 1 700 2 185 2 700 2 5 10 2012 715 646 1 700 2 226 2 700 2 5 10 2013 728 736 – 1 684 – 2 4 6 2014 741 735 – 9 439 – 11 24 37 2015 754 613 – 9 473 – 11 24 37 2016 767 363 – 13 822 – 14 30 46 2017 780 091 – 14 671 – 12 25 38 2018 792 813 – 24 845 – 21 43 65 2019 805 409 – 49 402 – 47 97 140 2020 817 529 – 72 332 – 63 126 190 2021 829 078 – 58 445 – 57 117 180 Egypt1,2 2000 71 371 368 – 0 – – 0 – 2001 72 854 264 – 0 – – 0 – 2002 74 393 760 – 0 – – 0 – 2003 75 963 320 – 0 – – 0 – 2004 77 522 424 – 0 – – 0 – 2005 79 075 312 – 0 – – 0 – 2006 80 629 672 – 0 – – 0 – 2007 82 218 752 – 0 – – 0 – 2008 83 844 784 – 0 – – 0 – 2009 85 501 064 – 0 – – 0 – 2010 87 252 416 – 0 – – 0 – 2011 89 200 056 – 0 – – 0 – 2012 91 240 376 – 0 – – 0 – 2013 93 377 888 – 0 – – 0 – 2014 95 592 320 – 0 – – 0 – 2015 97 723 800 – 0 – – 0 – 2016 99 784 032 – 0 – – 0 – 2017 101 789 384 – 0 – – 0 – 2018 103 740 768 – 0 – – 0 – 2019 105 618 672 – 0 – – 0 – 2020 107 465 136 – 0 – – 0 – 2021 109 262 176 – 0 – – 0 – Iran (Islamic Republic of)1,2 2000 669 208 – 19 716 – – 4 – 2001 680 750 – 19 303 – – 2 – 2002 687 409 – 15 558 – – 2 – 2003 693 817 – 23 562 – – 5 – 2004 705 119 – 13 821 – – 1 – 2005 716 564 – 18 966 – – 1 – 2006 727 725 – 15 909 – – 1 – 2007 738 381 – 15 712 – – 3 – 2008 748 580 – 8 349 – – 3 – 2009 758 834 – 4 345 – – 0 – 2010 769 567 – 1 847 – – 0 – 2011 779 461 – 1 632 – – 0 – 2012 789 482 – 756 – – 0 – 2013 801 065 – 480 – – 0 – 2014 816 408 – 358 – – 0 – 2015 835 084 – 167 – – 0 – 2016 850 556 – 81 – – 0 – 2017 862 796 – 57 – – 0 – 2018 874 155 – 0 – – 0 – 2019 883 820 – 0 – – 0 – 2020 891 232 – 0 – – 0 – 2021 897 698 – 0 – – 0 – 222 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper EASTERN MEDITERRANEAN Iraq1,2 2000 3 201 751 – 1 860 – – 0 – 2001 3 305 336 – 1 265 – – 0 – 2002 3 413 194 – 952 – – 0 – 2003 3 518 947 – 347 – – 0 – 2004 3 621 663 – 155 – – 0 – 2005 3 730 828 – 47 – – 0 – 2006 3 757 729 – 24 – – 0 – 2007 3 725 915 – 2 – – 0 – 2008 3 798 389 – 2 – – 0 – 2009 3 937 575 – 0 – – 0 – 2010 4 064 433 – 0 – – 0 – 2011 4 209 147 – 0 – – 0 – 2012 4 402 378 – 0 – – 0 – 2013 4 612 634 – 0 – – 0 – 2014 4 777 043 – 0 – – 0 – 2015 4 908 515 – 0 – – 0 – 2016 5 030 732 – 5 – – 0 – 2017 5 150 750 – 0 – – 0 – 2018 5 276 791 – 0 – – 0 – 2019 5 403 257 – 0 – – 0 – 2020 5 532 407 – 0 – – 0 – 2021 5 659 366 – 0 – – 0 – Morocco1,2,3 2000 28 554 416 – 3 – – 0 – 2001 28 930 096 – 0 – – 0 – 2002 29 301 816 – 19 – – 0 – 2003 29 661 270 – 4 – – 0 – 2004 30 033 124 – 1 – – 0 – 2005 30 431 902 – 0 – – 0 – 2006 30 833 022 – 0 – – 0 – 2007 31 232 632 – 0 – – 0 – 2008 31 634 992 – 0 – – 0 – 2009 32 042 876 – 0 – – 0 – 2010 32 464 864 – 0 – – 0 – 2011 32 903 700 – 0 – – 0 – 2012 33 352 168 – 0 – – 0 – 2013 33 803 528 – 0 – – 0 – 2014 34 248 604 – 0 – – 0 – 2015 34 680 456 – 0 – – 0 – 2016 35 107 264 – 0 – – 0 – 2017 35 528 116 – 0 – – 0 – 2018 35 927 512 – 0 – – 0 – 2019 36 304 408 – 0 – – 0 – 2020 36 688 772 – 0 – – 0 – 2021 37 076 584 – 0 – – 0 – Oman1,2 2000 2 344 253 – 6 – – 0 – 2001 2 374 653 – 2 – – 0 – 2002 2 403 659 – 6 – – 0 – 2003 2 431 600 – 6 – – 0 – 2004 2 468 855 – 0 – – 0 – 2005 2 515 192 – 0 – – 0 – 2006 2 560 649 – 0 – – 0 – 2007 2 605 700 – 4 – – 0 – 2008 2 651 028 – 0 – – 0 – 2009 2 697 537 – 0 – – 0 – 2010 2 881 914 – 0 – – 0 – 2011 3 206 870 – 0 – – 0 – 2012 3 535 579 – 0 – – 0 – 2013 3 816 680 – 0 – – 0 – 2014 4 009 267 – 0 – – 0 – 2015 4 191 776 – 0 – – 0 – 2016 4 398 070 – 0 – – 0 – 2017 4 541 854 – 0 – – 0 – 2018 4 601 157 – 0 – – 0 – 2019 4 602 768 – 0 – – 0 – 2020 4 543 399 – 0 – – 0 – 2021 4 520 471 – 0 – – 0 – 223 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper EASTERN MEDITERRANEAN Pakistan 2000 151 762 612 361 000 923 913 2 207 000 290 989 2 630 2001 156 528 540 436 000 1 037 774 2 359 000 370 1 144 2 990 2002 160 505 291 359 000 966 325 2 406 000 280 1 001 2 770 2003 164 058 125 395 000 924 733 2 204 000 320 991 2 670 2004 167 766 368 381 000 683 580 1 342 000 270 643 1 440 2005 171 426 951 376 000 845 170 2 011 000 310 923 2 500 2006 175 062 381 359 000 851 104 2 033 000 290 882 2 320 2007 178 851 822 354 000 835 398 2 025 000 280 879 2 420 2008 182 791 561 278 000 763 800 1 945 000 190 679 1 880 2009 186 912 034 441 000 1 015 691 2 286 000 340 1 000 2 500 2010 191 170 159 646 000 1 445 704 3 058 000 570 1 616 3 880 2011 195 248 335 915 000 1 905 938 3 748 000 680 1 814 4 070 2012 198 790 599 782 000 1 652 576 3 326 000 620 1 703 3 940 2013 201 869 416 723 000 1 381 009 2 670 000 460 1 165 2 560 2014 204 734 261 555 000 994 600 1 834 000 290 725 1 530 2015 207 406 024 529 000 1 000 339 2 078 000 310 765 1 790 2016 209 918 397 658 000 1 017 237 1 741 000 400 844 1 670 2017 212 724 995 582 000 819 944 1 270 000 320 641 1 180 2018 216 020 207 548 000 705 529 983 000 250 511 860 2019 219 521 856 507 000 640 380 868 000 280 544 890 2020 223 359 383 428 000 542 779 724 000 230 454 740 2021 227 493 730 401 000 505 620 678 000 230 460 760 Saudi Arabia1,2 2000 1 726 161 – 6 608 – – 0 – 2001 1 769 303 – 3 074 – – 0 – 2002 1 812 361 – 2 612 – – 0 – 2003 1 854 614 – 1 724 – – 0 – 2004 1 895 547 – 1 232 – – 0 – 2005 1 954 495 – 1 059 – – 0 – 2006 2 033 421 – 1 278 – – 0 – 2007 2 114 909 – 467 – – 0 – 2008 2 198 006 – 61 – – 0 – 2009 2 281 836 – 58 – – 0 – 2010 2 356 189 – 29 – – 0 – 2011 2 415 392 – 69 – – 0 – 2012 2 469 113 – 82 – – 0 – 2013 2 522 062 – 34 – – 0 – 2014 2 573 578 – 30 – – 0 – 2015 2 623 590 – 83 – – 0 – 2016 2 676 977 – 272 – – 0 – 2017 2 739 210 – 177 – – 0 – 2018 2 805 302 – 61 – – 0 – 2019 2 870 129 – 38 – – 0 – 2020 2 883 728 – 83 – – 0 – 2021 2 879 986 – 0 – – 0 – Somalia 2000 8 721 465 677 000 1 177 314 1 906 000 1 130 3 013 5 970 2001 9 070 747 833 000 1 283 399 1 902 000 1 330 3 285 6 200 2002 9 411 103 818 000 1 236 218 1 768 000 1 280 3 164 5 800 2003 9 758 281 857 000 1 268 524 1 814 000 1 340 3 247 5 870 2004 10 117 354 874 000 1 245 035 1 743 000 1 330 3 187 5 670 2005 10 467 292 996 000 1 392 857 1 881 000 1 530 3 565 6 280 2006 10 784 973 940 000 1 274 766 1 689 000 1 400 3 263 5 700 2007 11 118 092 825 000 1 128 218 1 510 000 1 190 2 796 4 850 2008 11 444 870 525 000 719 911 965 000 770 1 811 3 140 2009 11 730 037 325 000 454 522 618 000 490 1 143 2 000 2010 12 026 649 359 000 500 313 679 000 550 1 280 2 240 2011 12 216 837 300 000 419 303 571 000 460 1 073 1 890 2012 12 440 326 313 000 433 472 589 000 480 1 109 1 940 2013 12 852 485 381 000 534 460 729 000 570 1 352 2 380 2014 13 309 235 476 000 668 820 917 000 690 1 628 2 870 2015 13 763 906 572 000 811 764 1 112 000 840 1 989 3 530 2016 14 292 847 560 000 837 484 1 192 000 850 2 051 3 700 2017 14 864 221 513 000 820 464 1 237 000 830 2 100 3 900 2018 15 411 094 579 000 913 696 1 366 000 880 2 198 4 100 2019 15 981 300 624 000 957 015 1 400 000 940 2 311 4 260 2020 16 537 016 570 000 926 877 1 451 000 900 2 372 4 570 2021 17 065 580 688 000 1 131 892 1 767 000 1 000 2 506 4 870 224 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper EASTERN MEDITERRANEAN Sudan 2000 26 298 772 1 624 000 2 439 775 3 493 000 2 480 5 978 10 900 2001 26 947 252 1 516 000 2 326 967 3 408 000 2 300 5 702 10 600 2002 27 570 318 1 156 000 1 837 673 2 863 000 1 790 4 503 8 580 2003 28 188 976 1 061 000 1 722 030 2 663 000 1 640 4 219 8 020 2004 28 831 550 988 000 1 596 519 2 429 000 1 560 3 911 7 430 2005 29 540 576 994 000 1 614 440 2 428 000 1 600 3 956 7 510 2006 30 332 968 1 009 000 1 597 895 2 435 000 1 560 3 915 7 430 2007 31 191 164 961 000 1 450 820 2 105 000 1 460 3 555 6 560 2008 32 065 240 887 000 1 238 850 1 678 000 1 290 3 035 5 340 2009 32 948 156 850 000 1 140 986 1 493 000 1 210 2 795 4 780 2010 33 739 932 820 000 1 104 287 1 457 000 1 180 2 705 4 680 2011 34 419 624 836 000 1 120 801 1 478 000 1 190 2 746 4 700 2012 35 159 792 868 000 1 162 568 1 530 000 1 240 2 848 4 830 2013 35 990 704 924 000 1 257 489 1 683 000 1 330 3 081 5 320 2014 37 003 244 1 008 000 1 427 317 1 956 000 1 510 3 497 6 230 2015 38 171 176 1 053 000 1 611 298 2 378 000 1 620 3 948 7 300 2016 39 377 168 1 185 000 2 097 220 3 462 000 1 700 4 527 9 060 2017 40 679 828 1 075 000 2 187 618 3 956 000 1 810 5 213 11 300 2018 41 999 060 1 154 000 2 485 883 4 696 000 1 930 5 882 12 800 2019 43 232 092 1 360 000 2 835 630 5 217 000 2 240 6 673 14 400 2020 44 440 488 1 614 000 3 261 859 5 853 000 2 690 7 635 16 400 2021 45 657 200 1 643 000 3 325 874 5 906 000 2 680 7 784 16 800 Syrian Arab Republic1,2 2000 16 307 654 – 6 – – 0 – 2001 16 727 948 – 63 – – 0 – 2002 17 164 020 – 15 – – 0 – 2003 17 611 356 – 2 – – 0 – 2004 18 084 008 – 1 – – 0 – 2005 18 583 556 – 0 – – 0 – 2006 19 432 008 – 0 – – 0 – 2007 20 703 004 – 0 – – 0 – 2008 21 474 060 – 0 – – 0 – 2009 21 827 220 – 0 – – 0 – 2010 22 337 564 – 0 – – 0 – 2011 22 730 732 – 0 – – 0 – 2012 22 605 576 – 0 – – 0 – 2013 21 495 820 – 0 – – 0 – 2014 20 072 232 – 0 – – 0 – 2015 19 205 178 – 0 – – 0 – 2016 18 964 252 – 0 – – 0 – 2017 18 983 372 – 0 – – 0 – 2018 19 333 464 – 0 – – 0 – 2019 20 098 252 – 0 – – 0 – 2020 20 772 596 – 0 – – 0 – 2021 21 324 368 – 0 – – 0 – United Arab Emirates1,2,3 2000 3 275 333 – 0 – – 0 – 2001 3 454 198 – 0 – – 0 – 2002 3 633 655 – 0 – – 0 – 2003 3 813 443 – 0 – – 0 – 2004 3 993 339 – 0 – – 0 – 2005 4 280 993 – 0 – – 0 – 2006 4 898 954 – 0 – – 0 – 2007 5 872 624 – 0 – – 0 – 2008 6 988 685 – 0 – – 0 – 2009 7 992 644 – 0 – – 0 – 2010 8 481 771 – 0 – – 0 – 2011 8 575 205 – 0 – – 0 – 2012 8 664 969 – 0 – – 0 – 2013 8 751 847 – 0 – – 0 – 2014 8 835 951 – 0 – – 0 – 2015 8 916 899 – 0 – – 0 – 2016 8 994 263 – 0 – – 0 – 2017 9 068 296 – 0 – – 0 – 2018 9 140 169 – 0 – – 0 – 2019 9 211 657 – 0 – – 0 – 2020 9 287 289 – 0 – – 0 – 2021 9 365 145 – 0 – – 0 – 225 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper EASTERN MEDITERRANEAN Yemen 2000 12 010 295 453 000 1 061 470 4 602 000 800 2 656 12 100 2001 12 342 168 526 000 1 182 990 5 327 000 930 2 961 14 100 2002 12 675 615 628 000 1 307 377 6 120 000 1 080 3 283 15 500 2003 13 016 123 500 000 1 188 478 5 500 000 890 2 964 15 200 2004 13 367 241 441 000 956 179 4 233 000 780 2 391 11 600 2005 13 745 863 441 000 1 002 795 4 657 000 800 2 495 12 100 2006 14 162 111 546 000 1 201 845 5 711 000 970 3 027 15 100 2007 14 597 452 431 000 852 425 1 854 000 730 2 117 5 340 2008 15 040 675 279 000 539 905 1 223 000 490 1 361 3 570 2009 15 492 355 358 000 702 010 1 641 000 630 1 779 4 930 2010 15 952 916 642 000 1 131 912 2 166 000 1 080 2 866 6 460 2011 16 424 635 492 000 792 771 1 319 000 800 2 015 4 090 2012 16 906 745 575 000 860 962 1 313 000 900 2 197 4 240 2013 17 397 125 491 000 700 432 1 014 000 750 1 786 3 290 2014 17 893 110 412 000 587 292 851 000 620 1 498 2 760 2015 18 385 186 361 000 513 816 743 000 550 1 309 2 430 2016 18 873 534 468 000 661 252 951 000 710 1 681 3 180 2017 19 363 770 528 000 747 173 1 068 000 810 1 886 3 480 2018 19 851 258 603 000 871 031 1 264 000 930 2 211 4 200 2019 20 338 783 582 000 831 533 1 200 000 900 2 108 3 900 2020 20 814 170 562 000 800 213 1 154 000 850 2 030 3 750 2021 21 263 922 690 000 979 967 1 420 000 1 030 2 455 4 550 EUROPEAN Armenia1,2,3 2000 3 168 523 – 141 – – 0 – 2001 3 133 133 – 79 – – 0 – 2002 3 105 037 – 52 – – 0 – 2003 3 084 102 – 29 – – 0 – 2004 3 065 745 – 47 – – 0 – 2005 3 047 246 – 7 – – 0 – 2006 3 026 486 – 0 – – 0 – 2007 3 004 393 – 0 – – 0 – 2008 2 983 421 – 0 – – 0 – 2009 2 964 296 – 0 – – 0 – 2010 2 946 293 – 0 – – 0 – 2011 2 928 976 – 0 – – 0 – 2012 2 914 421 – 0 – – 0 – 2013 2 901 385 – 0 – – 0 – 2014 2 889 930 – 0 – – 0 – 2015 2 878 595 – 0 – – 0 – 2016 2 865 835 – 0 – – 0 – 2017 2 851 923 – 0 – – 0 – 2018 2 836 557 – 0 – – 0 – 2019 2 820 602 – 0 – – 0 – 2020 2 805 608 – 0 – – 0 – 2021 2 790 974 – 0 – – 0 – Azerbaijan1,2 2000 188 377 – 1 526 – – 0 – 2001 190 486 – 1 054 – – 0 – 2002 192 558 – 505 – – 0 – 2003 194 633 – 480 – – 0 – 2004 196 791 – 386 – – 0 – 2005 199 093 – 242 – – 0 – 2006 201 557 – 141 – – 0 – 2007 204 203 – 108 – – 0 – 2008 206 993 – 72 – – 0 – 2009 209 751 – 78 – – 0 – 2010 212 455 – 50 – – 0 – 2011 215 257 – 4 – – 0 – 2012 218 168 – 3 – – 0 – 2013 221 132 – 0 – – 0 – 2014 224 063 – 0 – – 0 – 2015 226 860 – 0 – – 0 – 2016 229 453 – 0 – – 0 – 2017 231 646 – 0 – – 0 – 2018 233 508 – 0 – – 0 – 2019 235 353 – 0 – – 0 – 2020 236 553 – 0 – – 0 – 2021 237 198 – 0 – – 0 – 226 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper EUROPEAN Georgia1,2 2000 42 651 – 245 – – 0 – 2001 41 444 – 438 – – 0 – 2002 40 611 – 474 – – 0 – 2003 40 178 – 316 – – 0 – 2004 39 888 – 257 – – 0 – 2005 39 611 – 155 – – 0 – 2006 39 338 – 59 – – 0 – 2007 39 064 – 24 – – 0 – 2008 38 796 – 6 – – 0 – 2009 38 552 – 1 – – 0 – 2010 38 368 – 0 – – 0 – 2011 38 216 – 0 – – 0 – 2012 38 043 – 0 – – 0 – 2013 37 860 – 0 – – 0 – 2014 37 742 – 0 – – 0 – 2015 37 711 – 0 – – 0 – 2016 37 711 – 0 – – 0 – 2017 37 719 – 0 – – 0 – 2018 37 723 – 0 – – 0 – 2019 37 708 – 0 – – 0 – 2020 37 659 – 0 – – 0 – 2021 37 579 – 0 – – 0 – Kazakhstan1,2,3 2000 15 236 253 – 0 – – 0 – 2001 15 281 285 – 0 – – 0 – 2002 15 338 963 – 0 – – 0 – 2003 15 416 712 – 0 – – 0 – 2004 15 521 923 – 0 – – 0 – 2005 15 656 248 – 0 – – 0 – 2006 15 822 748 – 0 – – 0 – 2007 16 006 136 – 0 – – 0 – 2008 16 196 517 – 0 – – 0 – 2009 16 402 369 – 0 – – 0 – 2010 16 627 837 – 0 – – 0 – 2011 16 864 916 – 0 – – 0 – 2012 17 102 864 – 0 – – 0 – 2013 17 345 732 – 0 – – 0 – 2014 17 592 298 – 0 – – 0 – 2015 17 835 908 – 0 – – 0 – 2016 18 078 552 – 0 – – 0 – 2017 18 314 814 – 0 – – 0 – 2018 18 538 100 – 0 – – 0 – 2019 18 754 258 – 0 – – 0 – 2020 18 979 244 – 0 – – 0 – 2021 19 196 464 – 0 – – 0 – Kyrgyzstan1,2,3 2000 3 849 441 – 7 – – 0 – 2001 3 885 526 – 15 – – 0 – 2002 3 920 779 – 2 725 – – 0 – 2003 3 961 055 – 461 – – 0 – 2004 4 006 891 – 91 – – 0 – 2005 4 050 628 – 225 – – 0 – 2006 4 092 492 – 318 – – 0 – 2007 4 129 917 – 96 – – 0 – 2008 4 169 423 – 18 – – 0 – 2009 4 222 986 – 1 – – 0 – 2010 4 277 343 – 3 – – 0 – 2011 4 330 822 – 0 – – 0 – 2012 4 392 921 – 0 – – 0 – 2013 4 461 256 – 0 – – 0 – 2014 4 535 245 – 0 – – 0 – 2015 4 613 684 – 0 – – 0 – 2016 4 694 273 – 0 – – 0 – 2017 4 774 583 – 0 – – 0 – 2018 4 854 325 – 0 – – 0 – 2019 4 932 441 – 0 – – 0 – 2020 5 011 401 – 0 – – 0 – 2021 5 091 639 – 0 – – 0 – 227 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper EUROPEAN Tajikistan1,2 2000 2 095 181 – 19 064 – – 0 – 2001 2 140 542 – 11 387 – – 0 – 2002 2 184 946 – 6 160 – – 0 – 2003 2 228 612 – 5 428 – – 0 – 2004 2 271 602 – 3 588 – – 0 – 2005 2 314 334 – 2 309 – – 0 – 2006 2 357 177 – 1 344 – – 0 – 2007 2 400 922 – 635 – – 0 – 2008 2 446 425 – 318 – – 0 – 2009 2 494 511 – 164 – – 0 – 2010 2 545 674 – 112 – – 0 – 2011 2 600 129 – 78 – – 0 – 2012 2 657 431 – 28 – – 0 – 2013 2 717 627 – 4 – – 0 – 2014 2 781 000 – 2 – – 0 – 2015 2 847 037 – 0 – – 0 – 2016 2 914 256 – 0 – – 0 – 2017 2 981 125 – 0 – – 0 – 2018 3 048 796 – 0 – – 0 – 2019 3 118 559 – 0 – – 0 – 2020 3 187 431 – 0 – – 0 – 2021 3 256 521 – 0 – – 0 – Türkiye1,2 2000 4 167 380 – 11 432 – – 0 – 2001 4 229 681 – 10 812 – – 0 – 2002 4 289 263 – 10 224 – – 0 – 2003 4 346 376 – 9 222 – – 0 – 2004 4 406 029 – 5 302 – – 0 – 2005 4 465 806 – 2 084 – – 0 – 2006 4 524 086 – 796 – – 0 – 2007 4 580 476 – 313 – – 0 – 2008 4 635 847 – 166 – – 0 – 2009 4 694 666 – 38 – – 0 – 2010 4 757 697 – 0 – – 0 – 2011 4 821 300 – 0 – – 0 – 2012 4 893 033 – 0 – – 0 – 2013 4 977 447 – 0 – – 0 – 2014 5 077 284 – 0 – – 0 – 2015 5 177 001 – 0 – – 0 – 2016 5 266 260 – 0 – – 0 – 2017 5 335 838 – 0 – – 0 – 2018 5 382 604 – 0 – – 0 – 2019 5 426 309 – 0 – – 0 – 2020 5 468 802 – 0 – – 0 – 2021 5 510 401 – 0 – – 0 – Turkmenistan1,2,3 2000 296 993 – 24 – – 0 – 2001 301 281 – 8 – – 0 – 2002 305 432 – 18 – – 0 – 2003 309 334 – 7 – – 0 – 2004 313 286 – 3 – – 0 – 2005 317 575 – 1 – – 0 – 2006 322 011 – 1 – – 0 – 2007 326 618 – 0 – – 0 – 2008 331 505 – 0 – – 0 – 2009 336 762 – 0 – – 0 – 2010 342 418 – 0 – – 0 – 2011 348 452 – 0 – – 0 – 2012 354 814 – 0 – – 0 – 2013 361 406 – 0 – – 0 – 2014 368 104 – 0 – – 0 – 2015 374 818 – 0 – – 0 – 2016 381 456 – 0 – – 0 – 2017 387 944 – 0 – – 0 – 2018 394 229 – 0 – – 0 – 2019 400 297 – 0 – – 0 – 2020 406 278 – 0 – – 0 – 2021 412 220 – 0 – – 0 – 228 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper EUROPEAN Uzbekistan1,2,3 2000 24 925 – 126 – – 0 – 2001 25 248 – 77 – – 0 – 2002 25 579 – 74 – – 0 – 2003 25 905 – 74 – – 0 – 2004 26 234 – 66 – – 0 – 2005 26 573 – 102 – – 0 – 2006 26 926 – 73 – – 0 – 2007 27 309 – 30 – – 0 – 2008 27 726 – 7 – – 0 – 2009 28 167 – 0 – – 0 – 2010 28 614 – 3 – – 0 – 2011 29 057 – 0 – – 0 – 2012 29 503 – 0 – – 0 – 2013 29 963 – 0 – – 0 – 2014 30 446 – 0 – – 0 – 2015 30 949 – 0 – – 0 – 2016 31 453 – 0 – – 0 – 2017 31 945 – 0 – – 0 – 2018 32 449 – 0 – – 0 – 2019 32 976 – 0 – – 0 – 2020 33 526 – 0 – – 0 – 2021 34 081 – 0 – – 0 – SOUTH‑EAST ASIA Bangladesh 2000 13 892 158 42 000 94 616 151 000 61 199 400 2001 14 158 526 43 000 96 431 152 000 63 203 400 2002 14 424 055 42 000 98 239 157 000 63 206 410 2003 14 678 189 43 000 99 970 158 000 65 210 420 2004 14 924 059 44 000 101 645 164 000 66 213 430 2005 15 152 331 46 000 103 199 165 000 67 216 440 2006 15 336 878 41 000 68 539 103 000 55 139 260 2007 15 498 937 78 000 126 937 185 000 110 263 480 2008 15 637 153 96 000 121 043 150 000 120 264 440 2009 15 775 383 68 000 80 603 94 000 85 187 300 2010 15 956 498 59 000 69 307 80 000 76 166 270 2011 16 152 189 54 000 63 432 73 000 71 155 250 2012 16 354 308 31 000 35 375 40 000 39 85 140 2013 16 562 861 28 000 31 594 35 000 35 77 120 2014 16 770 518 60 000 66 274 74 000 74 160 250 2015 16 971 459 41 000 45 478 50 000 49 105 170 2016 17 181 635 29 000 31 621 35 000 33 72 110 2017 17 397 705 30 000 33 331 37 000 34 73 110 2018 17 600 935 11 000 12 397 14 000 12 26 42 2019 17 797 959 18 000 21 202 25 000 22 47 77 2020 18 002 774 6 400 7 545 8 800 7 15 25 2021 18 210 878 7 700 8 974 10 000 7 16 27 Bhutan1,2 2000 434 533 – 5 935 – – 15 – 2001 446 393 – 5 982 – – 14 – 2002 458 095 – 6 511 – – 11 – 2003 469 623 – 3 806 – – 14 – 2004 480 993 – 2 670 – – 7 – 2005 490 859 – 1 825 – – 5 – 2006 498 212 – 1 868 – – 7 – 2007 504 394 – 793 – – 2 – 2008 510 405 – 329 – – 2 – 2009 516 281 – 1 057 – – 4 – 2010 522 081 – 436 – – 2 – 2011 527 864 – 194 – – 1 – 2012 533 647 – 82 – – 1 – 2013 539 377 – 15 – – 0 – 2014 544 904 – 19 – – 0 – 2015 550 022 – 34 – – 0 – 2016 554 823 – 15 – – 0 – 2017 559 529 – 11 – – 0 – 2018 563 951 – 6 – – 0 – 2019 567 919 – 2 – – 0 – 2020 571 654 – 22 – – 0 – 2021 575 339 – 9 – – 0 – 229 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper SOUTH‑EAST ASIA Democratic People's Republic of Korea1,2 2000 9 124 369 – 90 582 – – 0 – 2001 9 181 169 – 115 615 – – 0 – 2002 9 230 327 – 98 852 – – 0 – 2003 9 286 281 – 16 538 – – 0 – 2004 9 351 578 – 15 827 – – 0 – 2005 9 410 951 – 6 728 – – 0 – 2006 9 463 579 – 6 913 – – 0 – 2007 9 510 728 – 4 795 – – 0 – 2008 9 554 673 – 16 611 – – 0 – 2009 9 598 587 – 14 632 – – 0 – 2010 9 639 559 – 13 520 – – 0 – 2011 9 677 573 – 16 760 – – 0 – 2012 9 718 176 – 21 850 – – 0 – 2013 9 762 710 – 14 407 – – 0 – 2014 9 811 252 – 10 535 – – 0 – 2015 9 862 750 – 7 022 – – 0 – 2016 9 914 135 – 5 033 – – 0 – 2017 9 963 612 – 4 603 – – 0 – 2018 10 011 184 – 3 698 – – 0 – 2019 10 056 984 – 1 869 – – 0 – 2020 10 100 728 – 1 819 – – 0 – 2021 10 141 510 – 2 357 – – 0 – India 2000 990 121 695 15 350 000 19 692 324 25 830 000 14 300 29 561 48 400 2001 1 008 190 390 15 790 000 20 074 161 25 840 000 13 900 28 669 46 800 2002 1 026 263 749 14 720 000 18 873 095 24 610 000 13 200 27 176 44 900 2003 1 044 112 732 15 750 000 20 204 783 25 900 000 13 600 27 816 45 700 2004 1 061 725 619 17 290 000 22 459 649 29 460 000 15 100 31 229 51 000 2005 1 078 894 419 18 090 000 24 222 815 33 260 000 15 500 32 539 54 000 2006 1 095 466 041 14 620 000 19 820 336 27 740 000 13 200 27 819 47 500 2007 1 111 647 995 14 190 000 19 163 338 27 170 000 13 100 27 709 47 600 2008 1 127 573 041 14 460 000 20 063 261 29 060 000 13 800 29 850 52 100 2009 1 143 369 395 14 800 000 20 663 404 29 800 000 14 500 32 038 56 900 2010 1 159 229 377 14 800 000 20 241 628 28 330 000 14 500 30 654 53 200 2011 1 175 121 294 12 760 000 17 298 372 24 320 000 12 000 25 663 44 400 2012 1 190 880 809 10 330 000 14 056 674 19 770 000 9 590 20 485 34 800 2013 1 206 433 770 8 181 000 10 943 831 15 260 000 7 840 16 683 28 500 2014 1 221 491 095 8 380 000 11 138 430 15 460 000 9 350 20 130 34 600 2015 1 236 086 513 8 956 000 11 856 993 16 020 000 9 930 21 702 37 200 2016 1 250 821 747 8 835 000 12 411 360 17 850 000 10 100 22 399 39 900 2017 1 265 360 473 6 853 000 9 333 319 13 220 000 7 480 16 285 28 500 2018 1 279 196 649 4 642 000 6 743 582 9 436 000 4 430 9 630 16 800 2019 1 292 379 912 3 697 000 5 549 896 7 805 000 3 530 7 704 13 300 2020 1 304 784 080 2 750 000 4 133 982 5 976 000 3 170 7 316 12 900 2021 1 315 227 711 2 874 000 4 265 027 5 880 000 3 270 7 450 13 100 Indonesia 2000 214 072 416 945 000 1 141 362 1 401 000 750 1 550 2 610 2001 217 112 432 954 000 1 157 570 1 424 000 760 1 572 2 650 2002 220 115 088 970 000 1 173 580 1 440 000 760 1 593 2 700 2003 223 080 128 983 000 1 189 388 1 461 000 770 1 615 2 720 2004 225 938 592 985 000 1 087 185 1 212 000 710 1 367 2 060 2005 228 805 152 1 069 000 1 187 260 1 324 000 840 1 647 2 510 2006 231 797 424 1 250 000 1 385 963 1 548 000 1 010 1 957 2 980 2007 234 858 288 1 034 000 1 252 185 1 546 000 810 1 701 2 870 2008 237 936 544 1 047 000 1 268 598 1 561 000 820 1 723 2 900 2009 240 981 296 1 061 000 1 284 831 1 581 000 840 1 744 2 920 2010 244 016 176 1 790 000 1 991 459 2 217 000 1 630 3 209 4 900 2011 247 099 696 1 635 000 1 811 566 2 023 000 1 450 2 875 4 410 2012 250 222 688 1 618 000 1 797 022 1 999 000 1 440 2 837 4 360 2013 253 275 920 1 381 000 1 529 542 1 706 000 1 230 2 446 3 750 2014 256 229 760 1 047 000 1 163 077 1 293 000 960 1 895 2 900 2015 259 091 968 938 000 1 039 394 1 158 000 830 1 651 2 530 2016 261 850 176 980 000 1 087 349 1 212 000 950 1 893 2 900 2017 264 498 848 681 000 744 237 816 000 650 1 306 1 990 2018 267 066 848 606 000 662 497 728 000 570 1 142 1 730 2019 269 582 880 706 000 774 340 851 000 690 1 395 2 140 2020 271 857 984 704 000 769 298 845 000 700 1 415 2 170 2021 273 753 184 746 000 811 636 891 000 700 1 412 2 160 230 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper SOUTH‑EAST ASIA Myanmar 2000 27 103 504 943 000 1 355 454 2 000 000 1 190 2 742 5 050 2001 27 387 102 956 000 1 369 637 2 006 000 1 210 2 771 5 100 2002 27 664 104 965 000 1 383 490 2 034 000 1 220 2 799 5 190 2003 27 928 400 972 000 1 396 707 2 058 000 1 210 2 826 5 240 2004 28 174 897 984 000 1 409 035 2 087 000 1 230 2 851 5 240 2005 28 404 651 998 000 1 420 525 2 124 000 1 280 2 874 5 320 2006 28 621 177 653 000 1 017 913 1 574 000 870 2 041 3 870 2007 28 833 880 943 000 1 285 206 1 871 000 1 160 2 560 4 660 2008 29 002 816 1 159 000 1 573 810 2 287 000 1 460 3 222 5 890 2009 29 173 245 1 098 000 1 492 378 2 143 000 1 380 3 023 5 410 2010 29 396 528 1 102 000 1 507 212 2 134 000 1 580 3 623 6 490 2011 29 636 702 1 019 000 1 326 603 1 775 000 1 440 3 214 5 580 2012 29 888 858 1 259 000 1 754 334 2 551 000 1 520 3 412 6 310 2013 30 144 876 452 000 611 838 842 000 530 1 169 2 090 2014 30 397 292 281 000 383 705 533 000 320 729 1 310 2015 30 642 216 219 000 271 652 328 000 230 481 790 2016 30 885 287 131 000 162 032 195 000 130 274 440 2017 31 120 974 98 000 120 755 145 000 99 209 340 2018 31 345 759 88 000 108 681 131 000 79 164 270 2019 31 568 473 63 000 78 326 94 000 48 97 160 2020 31 796 420 66 000 82 144 98 000 39 77 120 2021 32 019 543 177 000 292 857 1 253 000 98 231 990 Nepal 2000 7 129 131 27 000 47 986 78 000 9 25 49 2001 7 244 248 21 000 55 778 118 000 7 28 70 2002 7 353 424 42 000 85 217 153 000 23 62 130 2003 7 455 234 30 000 72 856 151 000 15 46 110 2004 7 548 430 15 000 34 773 75 000 7 24 61 2005 7 630 041 15 000 36 030 84 000 7 26 69 2006 7 697 927 14 000 35 567 82 000 9 32 85 2007 7 754 440 18 000 42 145 91 000 12 39 96 2008 7 803 174 13 000 31 003 74 000 7 25 66 2009 7 845 380 12 000 25 643 59 000 7 21 55 2010 7 884 459 15 000 30 196 63 000 9 27 64 2011 7 914 890 9 800 18 512 39 000 5 16 39 2012 7 933 553 8 500 13 662 28 000 5 13 31 2013 7 948 318 7 000 10 164 17 000 2 6 13 2014 7 971 700 3 000 4 912 9 900 0 3 7 2015 8 014 724 2 400 4 370 9 200 0 2 6 2016 8 087 545 2 300 3 353 5 800 0 2 4 2017 8 181 084 2 200 2 690 3 400 0 0 1 2018 8 274 928 2 000 2 736 3 900 0 1 2 2019 8 369 496 380 464 590 – 0 – 2020 8 519 319 200 244 300 – 0 – 2021 8 718 556 86 106 130 – 0 – Sri Lanka1,2,3 2000 4 318 565 – 210 039 – – 77 – 2001 4 351 663 – 66 522 – – 52 – 2002 4 395 462 – 41 411 – – 30 – 2003 4 439 731 – 10 510 – – 4 – 2004 4 482 799 – 3 720 – – 1 – 2005 4 524 989 – 1 640 – – 0 – 2006 4 570 262 – 591 – – 1 – 2007 4 618 090 – 198 – – 1 – 2008 4 665 698 – 649 – – 0 – 2009 4 710 969 – 531 – – 0 – 2010 4 753 767 – 684 – – 0 – 2011 4 797 741 – 124 – – 0 – 2012 4 833 944 – 23 – – 0 – 2013 4 860 303 – 0 – – 0 – 2014 4 885 074 – 0 – – 0 – 2015 4 907 440 – 0 – – 0 – 2016 4 927 863 – 0 – – 0 – 2017 4 946 566 – 0 – – 0 – 2018 4 963 563 – 0 – – 0 – 2019 4 979 422 – 0 – – 0 – 2020 4 994 468 – 0 – – 0 – 2021 5 007 891 – 0 – – 0 – 231 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper SOUTH‑EAST ASIA Thailand1,2 2000 11 967 518 – 78 561 – – 625 – 2001 12 078 203 – 63 528 – – 424 – 2002 12 186 876 – 44 555 – – 361 – 2003 12 292 075 – 37 355 – – 204 – 2004 12 393 447 – 26 690 – – 230 – 2005 12 490 261 – 29 782 – – 161 – 2006 12 584 792 – 30 294 – – 113 – 2007 12 681 044 – 33 178 – – 97 – 2008 12 776 206 – 28 569 – – 101 – 2009 12 868 319 – 29 462 – – 70 – 2010 12 955 007 – 32 480 – – 80 – 2011 13 038 950 – 24 897 – – 43 – 2012 13 123 236 – 46 895 – – 37 – 2013 13 203 235 – 41 602 – – 47 – 2014 13 275 788 – 41 218 – – 38 – 2015 13 339 065 – 14 265 – – 33 – 2016 13 398 391 – 7 727 – – 27 – 2017 13 453 642 – 4 393 – – 15 – 2018 13 497 211 – 4 454 – – 15 – 2019 13 531 360 – 3 644 – – 13 – 2020 13 563 222 – 2 988 – – 3 – 2021 13 587 025 – 2 219 – – 0 – Timor-Leste 2000 826 105 55 000 128 146 290 000 64 244 680 2001 839 875 56 000 130 282 296 000 64 248 680 2002 855 524 70 000 104 357 142 000 86 199 350 2003 871 589 57 000 67 524 79 000 60 125 200 2004 889 711 141 000 234 045 328 000 180 449 810 2005 911 648 105 000 153 748 209 000 130 304 530 2006 935 396 102 000 164 285 235 000 140 326 590 2007 958 719 82 000 114 693 152 000 100 226 390 2008 981 022 89 000 134 166 185 000 120 271 480 2009 1 002 150 66 000 100 128 139 000 83 198 350 2010 1 023 731 68 000 95 412 127 000 85 194 330 2011 1 046 764 25 000 30 828 38 000 30 65 110 2012 1 069 995 6 100 7 263 8 500 4 9 15 2013 1 092 453 1 200 1 418 1 700 0 1 2 2014 1 114 343 400 473 550 0 0 1 2015 1 134 078 93 110 130 – 0 – 2016 1 151 712 94 112 130 – 0 – 2017 1 169 274 18 22 25 – 0 – 2018 1 186 777 – 0 – – 0 – 2019 1 204 264 – 0 – – 0 – 2020 1 222 657 3 4 4 – 0 – 2021 1 242 358 – 0 – – 0 – WESTERN PACIFIC Cambodia 2000 8 570 323 421 000 669 109 985 000 640 1 582 2 970 2001 8 725 446 282 000 388 681 524 000 390 904 1 580 2002 8 883 564 257 000 349 008 463 000 350 803 1 400 2003 9 043 340 366 000 464 899 589 000 480 1 062 1 780 2004 9 205 047 329 000 403 094 495 000 420 894 1 470 2005 9 367 851 329 000 388 706 461 000 350 705 1 120 2006 9 531 350 340 000 437 975 576 000 410 897 1 530 2007 9 698 963 140 000 191 165 272 000 180 394 710 2008 9 860 978 163 000 214 883 291 000 220 497 880 2009 10 010 797 332 000 426 267 554 000 400 853 1 430 2010 10 157 746 291 000 353 293 430 000 310 644 1 040 2011 10 306 505 320 000 368 041 425 000 310 641 1 000 2012 10 456 963 226 000 260 016 302 000 190 383 600 2013 10 607 625 146 000 168 806 197 000 120 231 360 2014 10 756 937 207 000 240 449 280 000 200 399 630 2015 10 903 118 189 000 218 837 256 000 180 374 590 2016 11 049 549 107 000 124 137 145 000 100 204 320 2017 11 195 304 175 000 202 696 237 000 160 336 520 2018 11 332 888 235 000 272 272 319 000 140 265 400 2019 11 461 955 121 000 140 073 164 000 52 102 160 2020 11 595 695 37 000 43 341 51 000 13 27 42 2021 11 731 591 17 000 19 064 22 000 4 10 16 232 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper WESTERN PACIFIC China1,2,3 2000 528 671 513 – 8 025 – – 39 – 2001 532 285 148 – 21 237 – – 30 – 2002 535 708 905 – 25 520 – – 42 – 2003 539 032 609 – 28 491 – – 52 – 2004 542 354 708 – 27 197 – – 31 – 2005 545 730 071 – 21 936 – – 48 – 2006 549 159 074 – 35 383 – – 38 – 2007 552 683 257 – 29 304 – – 18 – 2008 556 302 513 – 16 650 – – 23 – 2009 560 049 121 – 9 287 – – 12 – 2010 563 840 590 – 4 990 – – 19 – 2011 567 564 448 – 1 308 – – 33 – 2012 571 523 044 – 244 – – 0 – 2013 575 512 689 – 83 – – 0 – 2014 579 314 007 – 53 – – 0 – 2015 582 879 678 – 39 – – 0 – 2016 586 298 295 – 1 – – 0 – 2017 589 805 615 – 0 – – 0 – 2018 592 646 781 – 0 – – 0 – 2019 594 651 988 – 0 – – 0 – 2020 595 934 137 – 0 – – 0 – 2021 596 337 181 – 0 – – 0 – Lao People's Democratic Republic 2000 2 825 890 71 000 89 755 111 000 98 221 370 2001 2 872 124 47 000 62 084 79 000 66 152 260 2002 2 917 078 37 000 50 404 66 000 52 124 220 2003 2 960 248 33 000 45 710 62 000 46 110 200 2004 3 001 408 28 000 40 333 56 000 40 100 180 2005 3 045 534 23 000 35 002 51 000 34 86 160 2006 3 094 250 31 000 48 965 74 000 47 123 240 2007 3 143 555 30 000 45 688 67 000 46 115 220 2008 3 192 733 28 000 41 446 59 000 42 104 200 2009 3 241 681 33 000 47 466 66 000 49 120 220 2010 3 290 327 31 000 43 766 59 000 45 109 200 2011 3 338 671 22 000 30 366 40 000 30 73 130 2012 3 386 790 58 000 79 939 106 000 74 174 310 2013 3 434 630 50 000 68 570 91 000 55 125 220 2014 3 481 831 64 000 87 770 116 000 61 134 230 2015 3 531 765 45 000 62 489 83 000 38 80 140 2016 3 585 851 19 000 26 862 36 000 15 32 55 2017 3 641 296 11 000 14 609 19 000 9 21 36 2018 3 697 018 11 000 15 654 21 000 10 23 41 2019 3 752 719 7 600 10 541 14 000 5 11 19 2020 3 808 576 4 100 5 674 7 500 3 7 12 2021 3 863 554 4 600 6 403 8 500 2 6 11 Malaysia1,2 2000 917 806 – 12 705 – – 35 – 2001 941 700 – 12 780 – – 46 – 2002 965 697 – 11 019 – – 38 – 2003 989 576 – 6 338 – – 21 – 2004 1 013 329 – 6 154 – – 35 – 2005 1 036 941 – 5 569 – – 33 – 2006 1 060 376 – 5 294 – – 21 – 2007 1 083 704 – 4 048 – – 15 – 2008 1 106 571 – 6 071 – – 20 – 2009 1 128 688 – 5 955 – – 23 – 2010 1 148 709 – 5 194 – – 13 – 2011 1 167 365 – 3 954 – – 11 – 2012 1 186 408 – 3 662 – – 11 – 2013 1 205 392 – 1 028 – – 10 – 2014 1 224 258 – 596 – – 4 – 2015 1 242 753 – 242 – – 4 – 2016 1 261 056 – 266 – – 2 – 2017 1 279 032 – 85 – – 1 – 2018 1 295 970 – 0 – – 0 – 2019 1 312 160 – 0 – – 0 – 2020 1 327 999 – 0 – – 0 – 2021 1 342 954 – 0 – – 0 – 233 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper WESTERN PACIFIC Papua New Guinea 2000 5 508 297 442 000 1 462 249 2 655 000 810 3 142 6 990 2001 5 698 489 492 000 1 443 554 2 578 000 850 3 075 6 550 2002 5 892 596 387 000 1 235 844 2 243 000 620 2 635 5 730 2003 6 090 980 436 000 1 333 205 2 406 000 690 2 816 6 210 2004 6 293 166 603 000 1 708 217 3 070 000 960 3 517 7 610 2005 6 498 818 465 000 1 413 497 2 580 000 710 2 796 6 060 2006 6 708 217 530 000 1 481 681 2 668 000 810 2 924 6 280 2007 6 921 066 390 000 1 251 261 2 275 000 680 2 692 5 850 2008 7 137 988 381 000 1 180 316 2 137 000 620 2 508 5 510 2009 7 358 890 594 000 1 563 409 2 760 000 980 3 378 7 180 2010 7 583 269 381 000 1 079 284 1 913 000 670 2 354 5 120 2011 7 806 637 324 000 899 054 1 609 000 560 2 032 4 430 2012 8 026 545 408 000 1 249 648 2 563 000 720 2 739 6 420 2013 8 245 627 780 000 1 420 220 2 240 000 1 270 3 452 6 740 2014 8 464 153 943 000 1 613 869 2 520 000 1 240 3 142 6 110 2015 8 682 174 579 000 884 727 1 249 000 780 1 860 3 410 2016 8 899 169 819 000 1 208 966 1 658 000 1 070 2 568 4 600 2017 9 114 796 804 000 1 230 223 1 741 000 1 040 2 510 4 570 2018 9 329 227 839 000 1 301 773 1 845 000 1 070 2 571 4 710 2019 9 542 486 784 000 1 124 906 1 490 000 960 2 250 3 970 2020 9 749 640 1 012 000 1 470 120 1 974 000 1 260 2 962 5 310 2021 9 949 437 856 000 1 237 112 1 652 000 1 040 2 449 4 340 Philippines 2000 45 273 459 60 000 79 974 103 000 88 204 350 2001 46 242 054 42 000 56 147 73 000 62 143 250 2002 47 205 780 53 000 70 585 91 000 77 180 320 2003 48 168 224 76 000 100 493 128 000 110 257 450 2004 49 134 961 67 000 91 225 119 000 100 233 410 2005 50 095 357 60 000 83 163 110 000 73 167 290 2006 51 048 109 72 000 102 993 137 000 87 203 360 2007 52 011 873 78 000 112 751 152 000 91 218 390 2008 52 993 876 50 000 74 621 102 000 59 144 260 2009 53 978 012 40 000 58 824 80 000 49 116 210 2010 54 959 319 37 000 53 512 71 000 48 113 200 2011 55 947 279 17 000 23 918 31 000 20 47 83 2012 56 931 289 14 000 19 171 25 000 15 35 61 2013 57 899 838 13 000 17 518 22 000 15 36 63 2014 58 843 596 10 000 14 318 19 000 13 29 53 2015 59 834 436 20 000 27 904 37 000 26 63 110 2016 60 905 260 12 000 17 308 23 000 16 38 70 2017 61 987 318 12 000 16 586 22 000 15 36 66 2018 63 050 263 7 700 10 900 15 000 10 25 46 2019 64 102 545 9 500 13 536 18 000 13 31 56 2020 65 153 787 11 000 15 096 20 000 14 33 61 2021 66 134 861 7 500 10 609 14 000 9 22 40 Republic of Korea1,2 2000 3 275 201 – 4 183 – – 0 – 2001 3 294 535 – 2 556 – – 0 – 2002 3 312 446 – 1 799 – – 0 – 2003 3 327 781 – 1 171 – – 0 – 2004 3 340 909 – 864 – – 0 – 2005 3 352 270 – 1 369 – – 0 – 2006 3 363 454 – 2 051 – – 0 – 2007 3 375 442 – 2 227 – – 1 – 2008 3 387 903 – 1 063 – – 0 – 2009 3 401 161 – 898 – – 1 – 2010 3 416 912 – 1 267 – – 1 – 2011 3 441 891 – 505 – – 2 – 2012 3 474 392 – 394 – – 0 – 2013 3 506 875 – 383 – – 0 – 2014 3 539 063 – 557 – – 0 – 2015 3 569 608 – 627 – – 0 – 2016 3 591 698 – 602 – – 0 – 2017 3 605 814 – 436 – – 0 – 2018 3 617 383 – 501 – – 0 – 2019 3 626 267 – 485 – – 0 – 2020 3 629 128 – 356 – – 0 – 2021 3 628 109 – 274 – – 0 – 234 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper WESTERN PACIFIC Solomon Islands 2000 425 678 134 000 254 582 419 000 180 476 950 2001 435 991 153 000 285 929 463 000 200 521 1 040 2002 446 252 156 000 280 109 455 000 200 513 1 010 2003 456 603 167 000 239 181 346 000 200 456 830 2004 467 067 189 000 337 823 543 000 250 652 1 300 2005 477 661 153 000 286 015 471 000 210 547 1 100 2006 488 495 150 000 281 888 467 000 210 549 1 120 2007 499 572 111 000 155 491 223 000 140 310 560 2008 510 840 67 000 93 119 133 000 80 182 330 2009 522 554 54 000 75 305 108 000 63 142 260 2010 534 990 65 000 91 425 130 000 73 163 300 2011 548 183 44 000 62 676 93 000 48 108 200 2012 562 085 39 000 52 221 73 000 41 89 160 2013 576 541 40 000 53 689 75 000 39 83 150 2014 591 401 26 000 34 803 46 000 26 56 93 2015 606 533 33 000 39 811 49 000 28 57 92 2016 621 820 72 000 84 179 101 000 51 103 160 2017 637 197 80 000 103 587 141 000 66 134 230 2018 652 656 73 000 86 680 103 000 55 109 170 2019 668 243 108 000 139 245 188 000 79 158 270 2020 684 279 98 000 112 768 133 000 63 123 190 2021 700 772 126 000 151 437 183 000 83 162 260 Vanuatu2 2000 192 074 13 000 23 167 38 000 13 34 67 2001 197 034 13 000 18 702 27 000 11 24 45 2002 202 125 25 000 36 655 53 000 23 52 96 2003 207 258 28 000 42 687 64 000 29 68 130 2004 212 422 22 000 30 560 41 000 21 46 78 2005 217 632 17 000 25 624 39 000 15 35 64 2006 222 923 14 000 22 943 35 000 13 30 57 2007 228 345 15 000 27 312 123 000 13 36 170 2008 233 952 13 000 26 771 122 000 12 37 170 2009 239 689 8 100 14 887 25 000 8 22 47 2010 245 453 15 000 20 972 30 000 12 27 48 2011 251 294 8 900 11 631 16 000 6 14 24 2012 257 313 6 400 8 394 11 000 – 0 – 2013 263 534 4 100 5 326 7 200 – 0 – 2014 269 927 1 900 2 427 3 300 – 0 – 2015 276 438 690 807 950 – 0 – 2016 283 218 3 200 4 173 5 600 – 0 – 2017 290 239 1 700 2 266 3 100 – 0 – 2018 297 298 890 1 167 1 600 – 0 – 2019 304 404 800 1 047 1 400 – 0 – 2020 311 685 700 910 1 200 – 0 – 2021 319 137 440 576 780 – 0 – Viet Nam 2000 58 222 974 158 000 201 414 268 000 190 421 730 2001 58 824 822 147 000 185 145 240 000 180 380 650 2002 59 432 490 105 000 131 451 171 000 120 271 460 2003 60 046 786 78 000 96 592 124 000 92 197 340 2004 60 662 466 47 000 56 559 72 000 54 115 200 2005 61 274 810 34 000 40 604 51 000 38 79 130 2006 61 871 553 37 000 43 620 54 000 43 92 150 2007 62 468 862 24 000 28 022 34 000 25 53 86 2008 63 084 224 16 000 17 911 22 000 17 37 60 2009 63 736 960 21 000 22 853 26 000 22 47 76 2010 64 420 951 21 000 22 959 26 000 21 45 71 2011 65 112 329 19 000 20 206 23 000 17 36 56 2012 65 814 096 22 000 23 838 27 000 20 41 63 2013 66 526 328 19 000 20 760 23 000 17 34 53 2014 67 239 562 18 000 19 060 21 000 14 29 44 2015 67 944 047 10 000 11 283 13 000 7 16 24 2016 68 633 226 4 600 5 024 5 600 3 7 12 2017 69 301 322 5 000 5 485 6 100 4 9 14 2018 69 950 815 3 500 3 777 4 200 2 6 9 2019 70 586 389 3 600 3 856 4 300 3 6 10 2020 71 229 019 1 500 1 657 1 900 1 2 3 2021 71 832 867 420 453 510 – 0 – 235 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper REGIONAL SUMMARY African 2000 566 541 740 195 000 000 211 089 820 229 000 000 815 000 840 915 875 000 2001 582 446 660 197 000 000 215 879 554 237 000 000 806 000 834 330 873 000 2002 598 937 190 196 000 000 213 799 385 234 000 000 769 000 795 664 831 000 2003 615 967 580 199 000 000 216 401 199 237 000 000 748 000 774 451 810 000 2004 633 618 001 198 000 000 216 335 188 242 000 000 725 000 754 012 807 000 2005 651 866 078 196 000 000 212 856 354 235 000 000 704 000 729 200 771 000 2006 670 719 418 195 000 000 212 073 244 234 000 000 696 000 721 873 764 000 2007 690 210 022 195 000 000 211 773 290 232 000 000 679 000 704 916 742 000 2008 710 329 157 196 000 000 211 953 110 230 000 000 660 000 684 925 717 000 2009 731 052 709 199 000 000 215 949 937 237 000 000 643 000 671 222 714 000 2010 752 483 002 199 000 000 216 716 235 239 000 000 623 000 653 024 700 000 2011 774 632 776 198 000 000 214 366 744 234 000 000 590 000 616 838 656 000 2012 797 177 551 199 000 000 214 135 861 233 000 000 556 000 583 635 622 000 2013 820 127 488 197 000 000 213 649 463 232 000 000 542 000 570 160 613 000 2014 843 365 783 191 000 000 211 298 150 233 000 000 516 000 549 364 601 000 2015 866 950 179 192 000 000 211 088 430 232 000 000 509 000 541 632 598 000 2016 891 196 712 193 000 000 210 772 642 231 000 000 511 000 542 311 600 000 2017 915 840 445 201 000 000 218 442 674 238 000 000 522 000 555 248 619 000 2018 940 840 305 198 000 000 215 442 566 236 000 000 508 000 541 171 614 000 2019 966 465 940 199 000 000 217 915 997 240 000 000 509 000 544 165 628 000 2020 992 736 767 208 000 000 231 933 554 260 000 000 559 000 599 400 722 000 2021 1 019 238 524 210 000 000 233 829 474 262 000 000 553 000 593 470 728 000 Americas 2000 109 669 858 1 400 000 1 548 657 1 710 000 776 919 1 124 2001 111 470 861 1 169 000 1 296 757 1 431 000 699 832 1 039 2002 113 238 792 1 078 000 1 182 832 1 297 000 622 765 976 2003 114 966 694 1 067 000 1 159 333 1 262 000 581 725 938 2004 116 666 896 1 069 000 1 146 570 1 235 000 567 712 919 2005 118 351 356 1 201 000 1 273 211 1 359 000 539 688 907 2006 120 011 317 1 033 000 1 097 170 1 175 000 445 584 791 2007 121 648 634 907 000 988 539 1 073 000 381 504 696 2008 123 257 009 643 000 696 347 761 000 322 470 699 2009 124 845 461 635 000 687 600 753 000 322 464 686 2010 126 403 005 746 000 818 245 901 000 354 502 728 2011 127 972 394 568 000 615 178 671 000 321 464 674 2012 129 580 259 545 000 585 401 634 000 308 430 607 2013 131 176 924 530 000 575 545 630 000 337 470 647 2014 132 766 381 444 000 475 260 510 000 259 348 449 2015 134 355 812 531 000 572 722 620 000 288 390 505 2016 135 910 096 637 000 688 498 749 000 379 530 693 2017 137 328 934 878 000 946 216 1 032 000 452 665 902 2018 138 565 250 861 000 929 469 1 014 000 391 572 773 2019 139 710 625 824 000 894 123 979 000 337 510 703 2020 140 873 258 599 000 646 320 700 000 296 418 556 2021 141 983 798 554 000 596 552 646 000 247 334 436 Eastern Mediterranean 2000 341 863 678 5 500 000 6 945 341 11 100 000 8 500 13 609 25 600 2001 350 735 504 5 600 000 7 169 562 11 900 000 8 900 14 063 25 700 2002 359 751 191 5 200 000 6 751 568 11 900 000 8 400 13 090 25 900 2003 368 629 707 5 000 000 6 374 197 11 100 000 7 700 12 228 24 300 2004 377 175 619 4 200 000 5 215 386 8 900 000 6 600 10 487 20 600 2005 385 908 992 4 200 000 5 412 749 9 400 000 7 000 11 203 22 200 2006 395 462 430 4 100 000 5 363 013 10 100 000 7 000 11 315 24 500 2007 405 585 796 3 800 000 4 737 690 6 500 000 6 100 9 591 14 700 2008 415 713 313 2 900 000 3 675 847 5 200 000 4 500 7 092 10 800 2009 425 906 791 2 800 000 3 595 279 5 300 000 4 400 6 862 10 800 2010 435 916 580 3 400 000 4 477 117 6 400 000 5 500 8 634 13 400 2011 445 578 067 3 500 000 4 605 018 6 500 000 5 100 7 845 11 500 2012 454 556 616 3 300 000 4 333 295 6 100 000 5 100 7 954 11 500 2013 462 332 268 3 300 000 4 155 222 5 600 000 4 900 7 517 10 700 2014 469 824 969 3 300 000 4 006 998 5 000 000 4 800 7 524 10 800 2015 477 583 738 3 500 000 4 315 175 5 700 000 5 200 8 209 12 100 2016 485 733 390 4 300 000 5 360 578 6 900 000 5 900 9 484 14 500 2017 494 550 984 4 100 000 5 388 975 7 300 000 6 000 10 243 16 800 2018 504 052 289 4 200 000 5 634 545 7 800 000 6 300 11 137 18 700 2019 513 985 511 4 200 000 5 727 478 8 200 000 6 700 11 903 20 400 2020 524 073 331 4 100 000 5 857 293 8 500 000 7 000 12 727 21 600 2021 534 204 371 4 400 000 6 195 030 9 000 000 7 500 13 413 22 600 236 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper REGIONAL SUMMARY European 2000 29 069 724 – 32 565 – – 0 – 2001 29 228 626 – 23 870 – – 0 – 2002 29 403 168 – 20 232 – – 0 – 2003 29 606 907 – 16 017 – – 0 – 2004 29 848 389 – 9 740 – – 0 – 2005 30 117 114 – 5 125 – – 0 – 2006 30 412 821 – 2 732 – – 0 – 2007 30 719 038 – 1 206 – – 0 – 2008 31 036 653 – 587 – – 0 – 2009 31 392 060 – 282 – – 0 – 2010 31 776 699 – 168 – – 0 – 2011 32 177 125 – 82 – – 0 – 2012 32 601 198 – 31 – – 0 – 2013 33 053 808 – 4 – – 0 – 2014 33 536 112 – 2 – – 0 – 2015 34 022 563 – 0 – – 0 – 2016 34 499 249 – 0 – – 0 – 2017 34 947 537 – 0 – – 0 – 2018 35 358 291 – 0 – – 0 – 2019 35 758 503 – 0 – – 0 – 2020 36 166 502 – 0 – – 0 – 2021 36 567 077 – 0 – – 0 – South-East Asia 2000 1 278 989 994 18 500 000 22 845 005 28 800 000 19 000 35 038 54 000 2001 1 300 990 001 18 800 000 23 135 506 29 100 000 19 000 33 981 52 000 2002 1 322 946 704 17 800 000 21 909 307 27 700 000 18 000 32 437 50 000 2003 1 344 613 982 18 600 000 23 099 437 29 100 000 18 000 32 860 50 000 2004 1 365 910 125 20 200 000 25 375 239 32 500 000 20 000 36 371 56 000 2005 1 386 715 302 21 100 000 27 163 552 35 900 000 21 000 37 772 61 000 2006 1 406 971 688 17 500 000 22 532 269 30 500 000 18 000 32 435 52 000 2007 1 426 866 515 16 900 000 22 023 468 29 900 000 18 000 32 598 53 000 2008 1 446 440 732 17 600 000 23 238 039 31 900 000 19 000 35 458 58 000 2009 1 465 841 005 17 900 000 23 692 669 33 200 000 20 000 37 285 63 000 2010 1 485 377 183 18 700 000 23 982 334 32 100 000 21 000 37 955 60 000 2011 1 505 013 663 16 000 000 20 591 288 27 700 000 18 000 32 032 51 000 2012 1 524 559 214 13 900 000 17 733 180 23 600 000 16 000 26 879 42 000 2013 1 543 823 823 10 400 000 13 184 411 17 400 000 11 000 20 429 32 000 2014 1 562 491 726 10 000 000 12 808 643 17 000 000 12 000 22 955 38 000 2015 1 580 600 235 10 400 000 13 239 318 17 600 000 12 000 23 974 39 000 2016 1 598 773 314 10 100 000 13 708 602 19 500 000 12 000 24 667 42 000 2017 1 616 651 707 7 800 000 10 243 361 14 100 000 9 000 17 888 30 000 2018 1 633 707 805 5 400 000 7 538 051 10 300 000 6 000 10 978 18 000 2019 1 650 038 669 4 600 000 6 429 743 8 800 000 5 000 9 256 15 000 2020 1 665 413 306 3 600 000 4 998 046 6 700 000 5 000 8 826 15 000 2021 1 678 483 995 4 000 000 5 383 185 7 300 000 5 000 9 109 15 000 Western Pacific 2000 653 883 215 1 778 000 2 805 163 4 049 000 3 400 6 154 10 100 2001 659 517 343 1 481 000 2 476 815 3 653 000 2 800 5 275 8 900 2002 664 966 933 1 329 000 2 192 394 3 223 000 2 500 4 658 7 900 2003 670 323 405 1 446 000 2 358 767 3 501 000 2 700 5 039 8 600 2004 675 685 483 1 557 000 2 702 026 4 067 000 2 900 5 623 9 800 2005 681 096 945 1 350 000 2 301 485 3 499 000 2 300 4 496 7 900 2006 686 547 801 1 485 000 2 462 793 3 650 000 2 600 4 877 8 300 2007 692 114 639 981 000 1 847 269 2 893 000 1 800 3 852 7 200 2008 697 811 578 862 000 1 672 851 2 693 000 1 600 3 552 6 600 2009 703 667 553 1 251 000 2 225 151 3 412 000 2 200 4 714 8 600 2010 709 598 266 967 000 1 676 662 2 517 000 1 700 3 488 6 200 2011 715 484 602 863 000 1 421 659 2 134 000 1 500 2 997 5 400 2012 721 618 925 877 000 1 697 527 2 977 000 1 400 3 472 7 200 2013 727 779 079 1 119 000 1 756 383 2 558 000 1 800 3 971 7 200 2014 733 724 735 1 316 000 2 013 902 2 938 000 1 900 3 793 6 700 2015 739 470 550 931 000 1 246 766 1 622 000 1 300 2 454 4 000 2016 745 129 142 1 073 000 1 471 518 1 921 000 1 400 2 954 5 000 2017 750 857 933 1 145 000 1 575 973 2 101 000 1 500 3 047 5 100 2018 755 870 299 1 234 000 1 692 724 2 236 000 1 500 2 999 5 100 2019 760 009 156 1 091 000 1 433 689 1 811 000 1 300 2 558 4 300 2020 763 423 945 1 195 000 1 649 922 2 152 000 1 400 3 154 5 400 2021 765 840 463 1 047 000 1 425 928 1 839 000 1 200 2 649 4 600 237 ANNEx 4 – F. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND ESTIMATED MALARIA CASES AND DEAThS, 2000–2021 WHO region Country/area Year Population denominator for incidence and mortality rate Cases Deaths Lower Point Upper Lower Point Upper REGIONAL SUMMARY Total 2000 2 980 018 209 229 000 000 245 266 552 266 000 000 866 000 896 635 938 000 2001 3 034 388 995 231 000 000 249 982 065 273 000 000 855 000 888 481 933 000 2002 3 089 243 978 228 000 000 245 855 718 268 000 000 816 000 846 614 890 000 2003 3 144 108 275 232 000 000 249 408 950 272 000 000 794 000 825 303 870 000 2004 3 198 904 513 232 000 000 250 784 149 278 000 000 773 000 807 205 866 000 2005 3 254 055 787 231 000 000 249 012 476 272 000 000 751 000 783 359 832 000 2006 3 310 125 475 226 000 000 243 531 222 268 000 000 739 000 771 084 820 000 2007 3 367 144 644 224 000 000 241 371 462 263 000 000 721 000 751 461 794 000 2008 3 424 588 442 224 000 000 241 236 782 262 000 000 700 000 731 497 773 000 2009 3 482 705 579 228 000 000 246 150 919 269 000 000 685 000 720 547 771 000 2010 3 541 554 735 229 000 000 247 670 761 272 000 000 667 000 703 603 757 000 2011 3 600 858 627 225 000 000 241 599 970 262 000 000 629 000 660 176 704 000 2012 3 660 093 763 222 000 000 238 485 295 258 000 000 593 000 622 370 664 000 2013 3 718 293 390 217 000 000 233 321 029 253 000 000 572 000 602 547 648 000 2014 3 775 709 706 211 000 000 230 602 955 253 000 000 549 000 583 984 639 000 2015 3 832 983 077 211 000 000 230 462 411 253 000 000 540 000 576 659 635 000 2016 3 891 241 903 214 000 000 232 001 838 253 000 000 545 000 579 946 642 000 2017 3 950 177 540 219 000 000 236 597 200 257 000 000 553 000 587 091 654 000 2018 4 008 394 239 214 000 000 231 237 356 252 000 000 532 000 566 857 642 000 2019 4 065 968 404 213 000 000 232 401 031 255 000 000 532 000 568 392 654 000 2020 4 122 687 109 222 000 000 245 085 135 273 000 000 583 000 624 525 747 000 2021 4 176 318 228 224 000 000 247 430 170 276 000 000 577 000 618 975 754 000 Data as of 22 November 2022 NMP: national malaria programme; WHO: World Health Organization. “–” refers to not applicable. 1 The number of indigenous malaria cases registered by the NMPs is reported here without further adjustments. 2 The number of indigenous malaria deaths registered by the NMPs is reported here without further adjustments. 3 Certified malaria free countries are included in this listing for historical purposes. 4 South Sudan became an independent state on 9 July 2011 and a Member State of WHO on 27 September 2011. South Sudan and Sudan have distinct epidemiological profiles comprising high transmission and low transmission areas respectively. For this reason, data up to June 2011 from the Sudanese high transmission areas (10 southern states, which correspond to South Sudan) and low transmission areas (15 northern states which correspond to contemporary Sudan) are reported separately. 5 Data were submitted for Bolivia (Plurinational State of) after the closure of data analysis for the report (total indigenous cases: 11 017 used in burden estimation instead of 9959 in 2021). Note: Population denominator for incidence and mortality rate is based on the United Nations population, times the proportion of the population at risk at baseline. 238 W O RL D M AL AR IA R EP O RT 2 02 2 239 ANNEx 4 – G. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND REPORTED MALARIA CASES BY PLACE OF CARE, 2021 WHO region Country/area Population Public sector Private sector Community level UN population At risk (low + high) At risk (high) Number of people living in active foci Presumed Confirmed Presumed Confirmed Presumed Confirmed AFRICAN Angola 34 503 776 34 503 776 34 503 776 – 843 346 8 213 612 112 309 Benin 12 996 895 12 996 895 12 996 895 – 177 572 1 932 123 109 708 489 485 454 760 Botswana 2 588 423 1 708 359 109 024 – 724 5 Burkina Faso 22 100 684 22 100 684 22 100 684 – 362 794 10 923 336 311 111 620 556 247 746 Burundi 12 551 213 12 551 213 12 551 213 – 1 995 5 590 025 783 557 325 468 364 Cabo Verde 587 925 152 861 0 0 21 4 Cameroon 27 198 628 27 198 628 19 311 025 – 49 458 1 787 028 36 156 1 282 493 265 653 Central African Republic 5 457 154 5 457 154 5 457 154 – 190 364 1 653 733 28 118 257 872 2 931 90 544 Chad 17 179 740 17 007 943 11 571 413 – 300 177 1 081 279 54 13 209 93 089 324 051 Comoros 821 625 821 625 390 929 469 420 5 887 2 831 1 829 Congo 5 835 806 5 835 806 5 835 806 – 131 788 4 189 616 4 Côte d'Ivoire 27 478 248 27 478 248 27 478 248 – 321 809 6 245 474 17 088 220 519 829 075 Democratic Republic of the Congo 95 894 120 95 894 120 93 017 296 – 3 247 358 5 23 249 165 5 Equatorial Guinea 1 634 466 1 634 466 1 634 466 – Eritrea 3 620 312 3 620 312 2 570 421 – 573 25 090 18 373 Eswatini 1 192 271 333 836 0 – 374 199 8 Ethiopia 120 283 024 81 792 456 32 716 982 – 87 821 1 335 568 2 786 61 583 Gabon 2 341 179 2 341 179 2 341 179 – 76 238 4 64 957 4 Gambia 2 639 916 2 639 916 2 639 916 – 75 419 1 452 416 Ghana 32 833 032 32 833 032 32 833 032 – 184 130 2 947 600 73 481 871 497 72 347 1 928 488 Guinea 13 531 906 13 531 906 13 531 906 – 1 966 720 1 012 454 642 Guinea-Bissau 2 060 721 2 060 721 2 060 721 – Kenya 53 005 616 53 005 616 37 207 292 – 427 117 6 3 828 757 6 Liberia 5 193 416 5 193 416 5 193 416 – 56 511 586 821 15 031 223 590 102 025 Madagascar* 28 915 652 28 915 652 25 378 689 – 2 238 982 100 121 Malawi 19 889 742 19 889 742 19 889 742 – 33 923 5 873 486 1 075 014 Mali 21 904 984 21 904 984 19 966 831 – 341 195 2 863 982 4 512 63 441 11 533 276 707 Mauritania 4 614 974 4 614 974 2 975 181 – 30 900 18 660 Mayotte 316 014 69 523 15 800 – Mozambique 32 077 072 32 077 072 32 077 072 – 7 465 8 822 472 3 320 1 273 335 Namibia 2 530 151 1 998 819 1 167 943 – 13 212 4 526 Niger 25 252 722 25 252 722 25 252 722 – 615 202 3 973 854 70 853 Nigeria 213 401 328 213 401 328 162 991 666 – 1 984 856 19 757 602 298 755 1 567 584 Rwanda 13 461 888 13 461 888 13 461 888 – 245 793 266 276 651 601 Sao Tome and Principe 223 107 223 107 223 107 – 2 227 503 Senegal 16 876 720 16 876 720 16 779 341 – 6 083 403 393 3 084 11 943 1 756 121 514 Sierra Leone 8 420 641 8 420 641 8 420 641 – 74 884 1 669 779 5 505 206 281 9 527 77 842 South Africa 59 392 256 5 939 226 2 375 690 7 022 322 5 812 South Sudan1 10 748 272 10 748 272 10 748 272 – 904 682 1 261 203 227 740 756 024 Togo 8 644 829 8 644 829 8 644 829 – 181 994 251 937 562 433 Uganda 45 853 776 45 853 776 45 853 776 – 488 843 10 158 662 319 525 1 775 936 2 401 789 United Republic of Tanzania2 63 588 336 63 588 336 46 208 649 – 6 640 3 918 606 2 147 538 212 Mainland 61 788 786 61 788 786 45 105 813 – 6 640 3 913 258 2 147 537 210 Zanzibar 1 799 550 1 799 550 1 102 836 – 5 348 1 002 Zambia 19 473 124 19 473 124 19 473 124 – 390 101 5 785 915 983 227 Zimbabwe 15 993 524 12 634 884 4 576 706 4 496 523 56 268 2 108 74 761 AMERICAS Belize 400 031 276 021 0 0 0 0 Bolivia (Plurinational State of) 12 079 472 5 435 762 301 745 – 9 744 5 215 Brazil 214 326 224 42 865 245 4 929 503 – 163 585 4 Colombia 51 516 560 11 333 643 5 176 383 2 578 386 73 979 6 Costa Rica 5 153 957 1 803 885 51 539 24 952 227 5 Dominican Republic 11 117 873 6 114 830 157 317 – 291 Ecuador 17 797 736 533 932 162 671 565 066 2 436 6 French Guiana 297 449 163 597 27 454 – 143 Guatemala 17 608 484 13 206 363 2 402 325 796 086 759 2 512 Guyana 804 567 804 567 87 810 – 20 828 5 22 Haiti 11 447 569 10 188 336 2 774 776 – 5 279 1 842 2 392 Honduras 10 278 345 9 353 294 2 619 744 124 197 701 16 940 240 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – G. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND REPORTED MALARIA CASES BY PLACE OF CARE, 2021 WHO region Country/area Population Public sector Private sector Community level UN population At risk (low + high) At risk (high) Number of people living in active foci Presumed Confirmed Presumed Confirmed Presumed Confirmed AFRICAN Angola 34 503 776 34 503 776 34 503 776 – 843 346 8 213 612 112 309 Benin 12 996 895 12 996 895 12 996 895 – 177 572 1 932 123 109 708 489 485 454 760 Botswana 2 588 423 1 708 359 109 024 – 724 5 Burkina Faso 22 100 684 22 100 684 22 100 684 – 362 794 10 923 336 311 111 620 556 247 746 Burundi 12 551 213 12 551 213 12 551 213 – 1 995 5 590 025 783 557 325 468 364 Cabo Verde 587 925 152 861 0 0 21 4 Cameroon 27 198 628 27 198 628 19 311 025 – 49 458 1 787 028 36 156 1 282 493 265 653 Central African Republic 5 457 154 5 457 154 5 457 154 – 190 364 1 653 733 28 118 257 872 2 931 90 544 Chad 17 179 740 17 007 943 11 571 413 – 300 177 1 081 279 54 13 209 93 089 324 051 Comoros 821 625 821 625 390 929 469 420 5 887 2 831 1 829 Congo 5 835 806 5 835 806 5 835 806 – 131 788 4 189 616 4 Côte d'Ivoire 27 478 248 27 478 248 27 478 248 – 321 809 6 245 474 17 088 220 519 829 075 Democratic Republic of the Congo 95 894 120 95 894 120 93 017 296 – 3 247 358 5 23 249 165 5 Equatorial Guinea 1 634 466 1 634 466 1 634 466 – Eritrea 3 620 312 3 620 312 2 570 421 – 573 25 090 18 373 Eswatini 1 192 271 333 836 0 – 374 199 8 Ethiopia 120 283 024 81 792 456 32 716 982 – 87 821 1 335 568 2 786 61 583 Gabon 2 341 179 2 341 179 2 341 179 – 76 238 4 64 957 4 Gambia 2 639 916 2 639 916 2 639 916 – 75 419 1 452 416 Ghana 32 833 032 32 833 032 32 833 032 – 184 130 2 947 600 73 481 871 497 72 347 1 928 488 Guinea 13 531 906 13 531 906 13 531 906 – 1 966 720 1 012 454 642 Guinea-Bissau 2 060 721 2 060 721 2 060 721 – Kenya 53 005 616 53 005 616 37 207 292 – 427 117 6 3 828 757 6 Liberia 5 193 416 5 193 416 5 193 416 – 56 511 586 821 15 031 223 590 102 025 Madagascar* 28 915 652 28 915 652 25 378 689 – 2 238 982 100 121 Malawi 19 889 742 19 889 742 19 889 742 – 33 923 5 873 486 1 075 014 Mali 21 904 984 21 904 984 19 966 831 – 341 195 2 863 982 4 512 63 441 11 533 276 707 Mauritania 4 614 974 4 614 974 2 975 181 – 30 900 18 660 Mayotte 316 014 69 523 15 800 – Mozambique 32 077 072 32 077 072 32 077 072 – 7 465 8 822 472 3 320 1 273 335 Namibia 2 530 151 1 998 819 1 167 943 – 13 212 4 526 Niger 25 252 722 25 252 722 25 252 722 – 615 202 3 973 854 70 853 Nigeria 213 401 328 213 401 328 162 991 666 – 1 984 856 19 757 602 298 755 1 567 584 Rwanda 13 461 888 13 461 888 13 461 888 – 245 793 266 276 651 601 Sao Tome and Principe 223 107 223 107 223 107 – 2 227 503 Senegal 16 876 720 16 876 720 16 779 341 – 6 083 403 393 3 084 11 943 1 756 121 514 Sierra Leone 8 420 641 8 420 641 8 420 641 – 74 884 1 669 779 5 505 206 281 9 527 77 842 South Africa 59 392 256 5 939 226 2 375 690 7 022 322 5 812 South Sudan1 10 748 272 10 748 272 10 748 272 – 904 682 1 261 203 227 740 756 024 Togo 8 644 829 8 644 829 8 644 829 – 181 994 251 937 562 433 Uganda 45 853 776 45 853 776 45 853 776 – 488 843 10 158 662 319 525 1 775 936 2 401 789 United Republic of Tanzania2 63 588 336 63 588 336 46 208 649 – 6 640 3 918 606 2 147 538 212 Mainland 61 788 786 61 788 786 45 105 813 – 6 640 3 913 258 2 147 537 210 Zanzibar 1 799 550 1 799 550 1 102 836 – 5 348 1 002 Zambia 19 473 124 19 473 124 19 473 124 – 390 101 5 785 915 983 227 Zimbabwe 15 993 524 12 634 884 4 576 706 4 496 523 56 268 2 108 74 761 AMERICAS Belize 400 031 276 021 0 0 0 0 Bolivia (Plurinational State of) 12 079 472 5 435 762 301 745 – 9 744 5 215 Brazil 214 326 224 42 865 245 4 929 503 – 163 585 4 Colombia 51 516 560 11 333 643 5 176 383 2 578 386 73 979 6 Costa Rica 5 153 957 1 803 885 51 539 24 952 227 5 Dominican Republic 11 117 873 6 114 830 157 317 – 291 Ecuador 17 797 736 533 932 162 671 565 066 2 436 6 French Guiana 297 449 163 597 27 454 – 143 Guatemala 17 608 484 13 206 363 2 402 325 796 086 759 2 512 Guyana 804 567 804 567 87 810 – 20 828 5 22 Haiti 11 447 569 10 188 336 2 774 776 – 5 279 1 842 2 392 Honduras 10 278 345 9 353 294 2 619 744 124 197 701 16 940 241 ANNEx 4 – G. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND REPORTED MALARIA CASES BY PLACE OF CARE, 2021 WHO region Country/area Population Public sector Private sector Community level UN population At risk (low + high) At risk (high) Number of people living in active foci Presumed Confirmed Presumed Confirmed Presumed Confirmed AMERICAS Mexico 126 705 136 2 534 103 126 705 197 924 275 Nicaragua 6 850 540 3 014 238 587 981 593 810 19 050 4 273 Panama 4 351 267 4 220 729 183 362 – 3 077 0 1 287 Peru 33 715 472 13 149 034 1 687 796 – 18 140 Suriname 612 985 91 948 26 027 1 261 47 4 26 Venezuela (Bolivarian Republic of) 28 199 868 14 099 934 5 848 229 11 356 587 194 057 6 EASTERN MEDITERRANEAN Afghanistan 40 099 464 30 876 587 10 918 282 – 91 58 108 4 3 515 12 24 640 Djibouti 1 105 557 829 168 388 138 – 58 445 471 Iran (Islamic Republic of) 87 923 432 879 234 0 306 646 999 Pakistan 231 402 112 226 774 070 66 914 548 – 557 229 524 662 169 573 Saudi Arabia* 35 950 396 2 876 032 0 241 840 2 616 4 Somalia 17 065 580 17 065 580 8 685 868 – 37 681 12 967 5 Sudan 45 657 200 45 657 200 39 676 106 – 2 312 910 1 619 625 28 120 Yemen 32 981 640 21 108 250 12 688 696 – 53 023 128 894 11 495 36 959 14 486 SOUTH‑EAST ASIA Bangladesh 169 356 256 18 629 188 2 138 969 – 7 294 6 Bhutan 777 486 575 340 101 073 7 836 23 4 Democratic People's Republic of Korea 25 971 908 10 129 044 1 458 842 – 2 357 India 1 407 563 904 1 309 034 431 170 751 577 – 161 753 6 Indonesia3 273 753 184 273 753 184 17 503 778 – 228 840 42 684 33 083 Myanmar* 53 798 084 32 278 850 8 506 015 – 79 001 6 Nepal* 30 034 988 8 710 147 1 569 928 87 907 144 275 133 69 29 Thailand3 71 601 104 13 604 210 1 585 248 222 477 2 307 207 765 Timor-Leste 1 320 942 1 241 685 447 370 – 0 0 0 WESTERN PACIFIC Cambodia 16 589 023 11 778 206 7 983 633 22 750 1 732 2 650 Lao People's Democratic Republic 7 425 057 3 861 030 3 863 554 42 417 2 247 201 1 478 Malaysia3 33 573 872 1 342 955 1 007 216 508 3 688 4 Papua New Guinea 9 949 437 9 949 437 9 352 470 – 103 635 651 963 Philippines3 113 880 328 66 050 590 7 760 944 417 033 1 329 112 2 856 Republic of Korea* 51 830 140 3 628 110 0 – 14 280 Solomon Islands 707 851 700 772 700 772 – 45 619 4 84 139 4 Vanuatu 319 137 319 137 277 409 – 322 Viet Nam 97 468 032 72 126 344 6 625 389 89 500 452 15 REGIONAL SUMMARY African 1 115 109 208 1 016 683 787 844 534 463 11 988 265 11 343 825 138 955 231 1 227 844 9 387 467 422 243 13 624 412 Americas 553 263 535 139 189 461 27 151 367 16 238 269 0 512 618 0 2 106 0 9 430 Eastern Mediterranean 492 185 381 346 066 121 139 271 638 548 486 2 404 262 2 111 178 12 161 210 518 12 67 246 South-East Asia 2 034 177 856 1 667 956 079 204 062 800 318 220 144 481 850 133 42 960 0 33 877 Western Pacific 331 742 877 169 756 581 37 571 387 572 208 149 254 745 886 0 593 0 6 999 Total 4 526 478 857 3 339 652 029 1 252 591 655 29 665 448 13 897 485 142 806 763 1 240 138 9 643 644 422 255 13 741 964 RDT: rapid diagnostic test; UN: United Nations; WHO: World Health Organization. “–” refers to not applicable or data not available. * Confirmed cases are corrected for double counting of microscopy and RDTs in the public sector unless further specified by the country. 1 In May 2013, South Sudan was reassigned to the WHO African Region (WHA resolution 66.21, https://apps.who.int/gb/ebwha/pdf_files/ WHA66/A66_R21-en.pdf). 2 Where national data for the United Republic of Tanzania are unavailable, refer to Mainland and Zanzibar. 3 Figures include non-human malaria cases. 4 Figures reported for the public sector include cases detected in the private sector. Data as of 23 November 2022 5 Figures reported for the public sector include cases detected at the community level. 6 Figures reported for the public sector include cases detected at the community level and in the private sector. Note: Figures include imported cases. 242 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – G. POPULATION DENOMINATOR FOR CASE INCIDENCE AND MORTALITY RATE, AND REPORTED MALARIA CASES BY PLACE OF CARE, 2021 WHO region Country/area Population Public sector Private sector Community level UN population At risk (low + high) At risk (high) Number of people living in active foci Presumed Confirmed Presumed Confirmed Presumed Confirmed AMERICAS Mexico 126 705 136 2 534 103 126 705 197 924 275 Nicaragua 6 850 540 3 014 238 587 981 593 810 19 050 4 273 Panama 4 351 267 4 220 729 183 362 – 3 077 0 1 287 Peru 33 715 472 13 149 034 1 687 796 – 18 140 Suriname 612 985 91 948 26 027 1 261 47 4 26 Venezuela (Bolivarian Republic of) 28 199 868 14 099 934 5 848 229 11 356 587 194 057 6 EASTERN MEDITERRANEAN Afghanistan 40 099 464 30 876 587 10 918 282 – 91 58 108 4 3 515 12 24 640 Djibouti 1 105 557 829 168 388 138 – 58 445 471 Iran (Islamic Republic of) 87 923 432 879 234 0 306 646 999 Pakistan 231 402 112 226 774 070 66 914 548 – 557 229 524 662 169 573 Saudi Arabia* 35 950 396 2 876 032 0 241 840 2 616 4 Somalia 17 065 580 17 065 580 8 685 868 – 37 681 12 967 5 Sudan 45 657 200 45 657 200 39 676 106 – 2 312 910 1 619 625 28 120 Yemen 32 981 640 21 108 250 12 688 696 – 53 023 128 894 11 495 36 959 14 486 SOUTH‑EAST ASIA Bangladesh 169 356 256 18 629 188 2 138 969 – 7 294 6 Bhutan 777 486 575 340 101 073 7 836 23 4 Democratic People's Republic of Korea 25 971 908 10 129 044 1 458 842 – 2 357 India 1 407 563 904 1 309 034 431 170 751 577 – 161 753 6 Indonesia3 273 753 184 273 753 184 17 503 778 – 228 840 42 684 33 083 Myanmar* 53 798 084 32 278 850 8 506 015 – 79 001 6 Nepal* 30 034 988 8 710 147 1 569 928 87 907 144 275 133 69 29 Thailand3 71 601 104 13 604 210 1 585 248 222 477 2 307 207 765 Timor-Leste 1 320 942 1 241 685 447 370 – 0 0 0 WESTERN PACIFIC Cambodia 16 589 023 11 778 206 7 983 633 22 750 1 732 2 650 Lao People's Democratic Republic 7 425 057 3 861 030 3 863 554 42 417 2 247 201 1 478 Malaysia3 33 573 872 1 342 955 1 007 216 508 3 688 4 Papua New Guinea 9 949 437 9 949 437 9 352 470 – 103 635 651 963 Philippines3 113 880 328 66 050 590 7 760 944 417 033 1 329 112 2 856 Republic of Korea* 51 830 140 3 628 110 0 – 14 280 Solomon Islands 707 851 700 772 700 772 – 45 619 4 84 139 4 Vanuatu 319 137 319 137 277 409 – 322 Viet Nam 97 468 032 72 126 344 6 625 389 89 500 452 15 REGIONAL SUMMARY African 1 115 109 208 1 016 683 787 844 534 463 11 988 265 11 343 825 138 955 231 1 227 844 9 387 467 422 243 13 624 412 Americas 553 263 535 139 189 461 27 151 367 16 238 269 0 512 618 0 2 106 0 9 430 Eastern Mediterranean 492 185 381 346 066 121 139 271 638 548 486 2 404 262 2 111 178 12 161 210 518 12 67 246 South-East Asia 2 034 177 856 1 667 956 079 204 062 800 318 220 144 481 850 133 42 960 0 33 877 Western Pacific 331 742 877 169 756 581 37 571 387 572 208 149 254 745 886 0 593 0 6 999 Total 4 526 478 857 3 339 652 029 1 252 591 655 29 665 448 13 897 485 142 806 763 1 240 138 9 643 644 422 255 13 741 964 RDT: rapid diagnostic test; UN: United Nations; WHO: World Health Organization. “–” refers to not applicable or data not available. * Confirmed cases are corrected for double counting of microscopy and RDTs in the public sector unless further specified by the country. 1 In May 2013, South Sudan was reassigned to the WHO African Region (WHA resolution 66.21, https://apps.who.int/gb/ebwha/pdf_files/ WHA66/A66_R21-en.pdf). 2 Where national data for the United Republic of Tanzania are unavailable, refer to Mainland and Zanzibar. 3 Figures include non-human malaria cases. 4 Figures reported for the public sector include cases detected in the private sector. Data as of 23 November 2022 5 Figures reported for the public sector include cases detected at the community level. 6 Figures reported for the public sector include cases detected at the community level and in the private sector. Note: Figures include imported cases. 243 ANNEx 4 – h. REPORTED MALARIA CASES BY METhOD OF CONFIRMATION, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 AFRICAN Algeria1,2,3 Suspected cases 12 224 11 974 15 790 12 762 8 690 8 000 6 628 6 469 10 081 8 620 11 197 7 220 Presumed and confirmed 408 191 887 603 266 747 432 453 1 242 1 014 2 726 1 164 Microscopy examined 12 224 11 974 15 790 12 762 8 690 8 000 6 628 6 469 10 081 8 620 11 197 7 220 Microscopy positive 408 191 887 603 266 747 432 453 1 242 1 014 2 726 1 164 RDT examined RDT positive Imported cases 396 187 828 587 260 727 420 446 1 241 1 014 2 725 1 164 Angola Suspected cases 4 591 529 4 469 357 4 849 418 5 273 305 6 134 471 6 839 963 7 649 902 11 050 353 10 870 446 14 341 390 13 232 340 15 624 710 Presumed and confirmed 3 687 574 3 501 953 3 031 546 3 144 100 3 180 021 3 254 270 4 301 146 4 500 221 5 928 260 7 530 788 7 156 110 9 169 267 Microscopy examined 1 947 349 1 765 933 2 245 223 3 025 258 3 398 029 3 345 693 4 183 727 7 493 969 5 066 780 5 643 654 5 216 938 6 663 417 Microscopy positive 1 324 264 1 147 473 1 056 563 1 462 941 1 431 313 1 396 773 2 058 128 2 199 810 2 442 500 2 557 385 2 359 788 3 424 950 RDT examined 639 476 833 753 1 069 483 1 103 815 1 855 400 3 009 305 2 959 282 2 931 055 5 025 981 8 221 926 7 456 619 8 117 947 RDT positive 358 606 484 809 440 271 536 927 867 666 1 372 532 1 736 125 1 675 082 2 708 075 4 497 593 4 239 539 4 900 971 Benin Suspected cases 1 928 016 2 431 902 2 091 263 2 930 569 2 733 611 2 184 524 2 705 456 2 880 743 3 958 782 3 572 959 4 226 652 Presumed and confirmed 1 873 015 1 786 864 2 069 728 1 717 115 2 122 011 2 042 684 1 667 005 1 968 532 2 255 946 3 084 525* 2 632 324 3 163 648* Microscopy examined 88 134 243 008 291 479 155 205 296 264 267 405 267 492 349 191 432 001 359 137 552 545 Microscopy positive 68 745 99 368 108 714 108 061 104 601 208 823 258 519 294 518 242 780 355 271 RDT examined 475 986 825 005 1 173 271 1 962 591 2 116 289 1 860 904 2 403 344 2 251 418 3 338 134 3 086 562 3 371 041 RDT positive 354 223 705 839 991 234 1 200 524 1 613 565 1 506 189 1 725 089 1 717 293 2 601 360 2 273 685 2 521 097 Botswana2 Suspected cases 1 298 12 986 12 605 13 979 16 564 9 148 9 825** Presumed and confirmed 12 196 1 138ˆ 308 506 1 485 346 725 1 911 585 272 953 729ˆ Microscopy examined 5 178 5 223 872 707 729 Microscopy positive 1 046 432 729 RDT examined 167 1 284 7 806 7 380 13 107 15 857 9 148 9 825 RDT positive 3 193 456 1 346 332 723 1 909 585 272 953 729 Imported cases 30 30 48 64 62 51 103 69 26 Burkina Faso Suspected cases 6 037 806 5 446 870 7 852 299 7 857 296 9 272 755 9 783 385 12 006 793 14 811 872 14 931 136 18 116 942 15 357 249 17 892 801 Presumed and confirmed 5 723 481 5 024 697 6 970 700 7 146 026 8 278 408 8 286 453 9 799 818 12 255 671 11 991 146 6475027* 11567698* 12 465 543 Microscopy examined 177 879 400 005 223 372 183 971 198 947 222 190 191 208 133 101 157 824 270 289 203 529 339 369 Microscopy positive 88 540 83 857 90 089 82 875 83 259 92 589 80 077 46 411 56 989 52 582 81 017 187 199 RDT examined 940 985 450 281 4 516 273 4 296 350 6 224 055 8 290 188 11 795 178 12 980 360 13 061 136 15 997 219 13 680 757 16 879 527 RDT positive 715 999 344 256 3 767 957 3 686 176 5 345 396 6 922 857 9 699 334 10 510 849 10 221 981 5 824 844 10 519 323 11 604 439 Burundi Suspected cases 5 590 736 4 780 117 4 270 100 7 507 441 7 831 895 8 761 333 13 022 128 13 956 707 8 734 322 16 214 258 8 571 445 12 379 963 Presumed and confirmed 4 255 301 3 307 158 2 600 286 4 567 428 4 987 388 5 512 414 8 902 503 9 259 694 5 149 436 9 983 843 4 732 339 6 618 492 Microscopy examined 2 825 558 2 859 720 2 659 372 4 123 012 4 471 998 3 254 670 3 941 251 3 814 355 1 542 232 3 858 517 1 786 568 2 667 318 Microscopy positive 1 599 908 1 485 332 1 484 676 2 366 134 2 718 391 1 964 862 2 520 622 2 269 831 1 148 316 1 759 011 756 441 1 191 319 RDT examined 273 324 188 476 1 177 132 2 995 339 3 098 808 5 422 959 8 971 550 9 678 610 7 009 165 12 331 431 6 772 641 9 593 269 RDT positive 163 539 89 905 682 014 1 812 204 2 007 908 3 463 848 6 272 554 6 526 121 3 818 195 8 200 522 3 963 662 5 424 395 Cabo Verde2,3 Suspected cases 26 508 8 715 10 621 6 894 6 620** 8 906** 16 573** 16 623 7 867** 4 399** 4 327** Presumed and confirmed 47 36 36 46 46ˆ 27ˆ 77ˆ 446ˆ 21 40ˆ 10 21 Microscopy examined 8 715 10 621 6 894 3 117 8 393 3 857 16 623 5 596 1 246 4 327 Microscopy positive 47 36 46 46 27 77 446 21 40 10 21 RDT examined 26 508 6 620 8 906 16 573 7 867 4 399 4 279 RDT positive 36 46 27 77 446 40 0 0 Imported cases 29 35 24 20 20 28 23 18 39 10 20 Cameroon Suspected cases 3 134 048 3 031 461 4 132 326 3 709 906 3 378 923 4 665 318 5 098 975 5 036 256 4 743 338 4 567 624 5 192 692 Presumed and confirmed 1 845 691 1 899 928 1 728 723 2 285 412 1 369 518 2 381 592 2 615 750 3 607 898 3 550 183 3 011 133 2 974 819 3 420 788* Microscopy examined 1 110 308 1 182 610 1 236 306 1 086 095 1 024 306 1 373 802 627 709 658 017 1 527 436 1 304 972 1 505 989 Microscopy positive 592 351 810 367 390 130 428 888 1 097 615 956 647 1 075 831 RDT examined 141 686 186 784 653 189 1 254 293 1 166 306 3 151 919 3 108 156 3 085 689 2 716 410 2 840 269 3 335 377 RDT positive 17 874 66 656 42 581 600 930 1 665 786 1 854 658 1 828 745 1 722 188 1 933 546 2 259 343 Central African Republic Suspected cases 518 761 546 095 625 301 1 218 246 2 095 095 1 533 258 1 367 986 3 393 641 2 730 158 3 069 218 Presumed and confirmed 66 484 221 980 500 806 454 532 495 238 953 535 1 607 079 1 296 277 995 157 2 708 497* 1 980 804 2 223 562 Microscopy examined 63 695 55 943 139 241 189 481 112 007 163 370 265 673 237 910 246 348 Microscopy positive 36 943 41 436 106 524 144 924 28 855 117 267 196 413 177 742 189 114 RDT examined 105 521 191 569 369 208 724 303 1 537 852 536 887 1 181 578 2 781 622 2 181 204 2 504 262 RDT positive 87 566 126 758 253 652 492 309 1 094 393 383 058 854 852 2 220 547 1 563 228 1 813 035 Chad Suspected cases 743 471 722 654 1 737 195 1 641 285 2 032 301 2 943 595 1 941 489 2 779 742 2 955 271 2 584 679 Presumed and confirmed 544 243 528 454 660 575 1 272 841 1 513 772 1 490 556 1 402 215 1 962 372 1 364 706 1 910 518 1 890 264* 1 811 859 Microscopy examined 89 749 69 789 1 063 293 1 584 525 190 006 211 816 250 117 284 426 Microscopy positive 75 342 86 348 7 710 206 082 160 260 149 574 720 765 1 064 354 137 501 152 127 193 816 191 628 RDT examined 309 927 114 122 621 469 1 137 455 937 775 861 561 1 359 070 1 751 483 2 260 256 1 873 598 1 788 058 RDT positive 125 106 94 778 548 483 753 772 637 472 574 003 898 018 1 227 205 1 480 402 1 350 378 1 226 911 244 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – h. REPORTED MALARIA CASES BY METhOD OF CONFIRMATION, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 AFRICAN Algeria1,2,3 Suspected cases 12 224 11 974 15 790 12 762 8 690 8 000 6 628 6 469 10 081 8 620 11 197 7 220 Presumed and confirmed 408 191 887 603 266 747 432 453 1 242 1 014 2 726 1 164 Microscopy examined 12 224 11 974 15 790 12 762 8 690 8 000 6 628 6 469 10 081 8 620 11 197 7 220 Microscopy positive 408 191 887 603 266 747 432 453 1 242 1 014 2 726 1 164 RDT examined RDT positive Imported cases 396 187 828 587 260 727 420 446 1 241 1 014 2 725 1 164 Angola Suspected cases 4 591 529 4 469 357 4 849 418 5 273 305 6 134 471 6 839 963 7 649 902 11 050 353 10 870 446 14 341 390 13 232 340 15 624 710 Presumed and confirmed 3 687 574 3 501 953 3 031 546 3 144 100 3 180 021 3 254 270 4 301 146 4 500 221 5 928 260 7 530 788 7 156 110 9 169 267 Microscopy examined 1 947 349 1 765 933 2 245 223 3 025 258 3 398 029 3 345 693 4 183 727 7 493 969 5 066 780 5 643 654 5 216 938 6 663 417 Microscopy positive 1 324 264 1 147 473 1 056 563 1 462 941 1 431 313 1 396 773 2 058 128 2 199 810 2 442 500 2 557 385 2 359 788 3 424 950 RDT examined 639 476 833 753 1 069 483 1 103 815 1 855 400 3 009 305 2 959 282 2 931 055 5 025 981 8 221 926 7 456 619 8 117 947 RDT positive 358 606 484 809 440 271 536 927 867 666 1 372 532 1 736 125 1 675 082 2 708 075 4 497 593 4 239 539 4 900 971 Benin Suspected cases 1 928 016 2 431 902 2 091 263 2 930 569 2 733 611 2 184 524 2 705 456 2 880 743 3 958 782 3 572 959 4 226 652 Presumed and confirmed 1 873 015 1 786 864 2 069 728 1 717 115 2 122 011 2 042 684 1 667 005 1 968 532 2 255 946 3 084 525* 2 632 324 3 163 648* Microscopy examined 88 134 243 008 291 479 155 205 296 264 267 405 267 492 349 191 432 001 359 137 552 545 Microscopy positive 68 745 99 368 108 714 108 061 104 601 208 823 258 519 294 518 242 780 355 271 RDT examined 475 986 825 005 1 173 271 1 962 591 2 116 289 1 860 904 2 403 344 2 251 418 3 338 134 3 086 562 3 371 041 RDT positive 354 223 705 839 991 234 1 200 524 1 613 565 1 506 189 1 725 089 1 717 293 2 601 360 2 273 685 2 521 097 Botswana2 Suspected cases 1 298 12 986 12 605 13 979 16 564 9 148 9 825** Presumed and confirmed 12 196 1 138ˆ 308 506 1 485 346 725 1 911 585 272 953 729ˆ Microscopy examined 5 178 5 223 872 707 729 Microscopy positive 1 046 432 729 RDT examined 167 1 284 7 806 7 380 13 107 15 857 9 148 9 825 RDT positive 3 193 456 1 346 332 723 1 909 585 272 953 729 Imported cases 30 30 48 64 62 51 103 69 26 Burkina Faso Suspected cases 6 037 806 5 446 870 7 852 299 7 857 296 9 272 755 9 783 385 12 006 793 14 811 872 14 931 136 18 116 942 15 357 249 17 892 801 Presumed and confirmed 5 723 481 5 024 697 6 970 700 7 146 026 8 278 408 8 286 453 9 799 818 12 255 671 11 991 146 6475027* 11567698* 12 465 543 Microscopy examined 177 879 400 005 223 372 183 971 198 947 222 190 191 208 133 101 157 824 270 289 203 529 339 369 Microscopy positive 88 540 83 857 90 089 82 875 83 259 92 589 80 077 46 411 56 989 52 582 81 017 187 199 RDT examined 940 985 450 281 4 516 273 4 296 350 6 224 055 8 290 188 11 795 178 12 980 360 13 061 136 15 997 219 13 680 757 16 879 527 RDT positive 715 999 344 256 3 767 957 3 686 176 5 345 396 6 922 857 9 699 334 10 510 849 10 221 981 5 824 844 10 519 323 11 604 439 Burundi Suspected cases 5 590 736 4 780 117 4 270 100 7 507 441 7 831 895 8 761 333 13 022 128 13 956 707 8 734 322 16 214 258 8 571 445 12 379 963 Presumed and confirmed 4 255 301 3 307 158 2 600 286 4 567 428 4 987 388 5 512 414 8 902 503 9 259 694 5 149 436 9 983 843 4 732 339 6 618 492 Microscopy examined 2 825 558 2 859 720 2 659 372 4 123 012 4 471 998 3 254 670 3 941 251 3 814 355 1 542 232 3 858 517 1 786 568 2 667 318 Microscopy positive 1 599 908 1 485 332 1 484 676 2 366 134 2 718 391 1 964 862 2 520 622 2 269 831 1 148 316 1 759 011 756 441 1 191 319 RDT examined 273 324 188 476 1 177 132 2 995 339 3 098 808 5 422 959 8 971 550 9 678 610 7 009 165 12 331 431 6 772 641 9 593 269 RDT positive 163 539 89 905 682 014 1 812 204 2 007 908 3 463 848 6 272 554 6 526 121 3 818 195 8 200 522 3 963 662 5 424 395 Cabo Verde2,3 Suspected cases 26 508 8 715 10 621 6 894 6 620** 8 906** 16 573** 16 623 7 867** 4 399** 4 327** Presumed and confirmed 47 36 36 46 46ˆ 27ˆ 77ˆ 446ˆ 21 40ˆ 10 21 Microscopy examined 8 715 10 621 6 894 3 117 8 393 3 857 16 623 5 596 1 246 4 327 Microscopy positive 47 36 46 46 27 77 446 21 40 10 21 RDT examined 26 508 6 620 8 906 16 573 7 867 4 399 4 279 RDT positive 36 46 27 77 446 40 0 0 Imported cases 29 35 24 20 20 28 23 18 39 10 20 Cameroon Suspected cases 3 134 048 3 031 461 4 132 326 3 709 906 3 378 923 4 665 318 5 098 975 5 036 256 4 743 338 4 567 624 5 192 692 Presumed and confirmed 1 845 691 1 899 928 1 728 723 2 285 412 1 369 518 2 381 592 2 615 750 3 607 898 3 550 183 3 011 133 2 974 819 3 420 788* Microscopy examined 1 110 308 1 182 610 1 236 306 1 086 095 1 024 306 1 373 802 627 709 658 017 1 527 436 1 304 972 1 505 989 Microscopy positive 592 351 810 367 390 130 428 888 1 097 615 956 647 1 075 831 RDT examined 141 686 186 784 653 189 1 254 293 1 166 306 3 151 919 3 108 156 3 085 689 2 716 410 2 840 269 3 335 377 RDT positive 17 874 66 656 42 581 600 930 1 665 786 1 854 658 1 828 745 1 722 188 1 933 546 2 259 343 Central African Republic Suspected cases 518 761 546 095 625 301 1 218 246 2 095 095 1 533 258 1 367 986 3 393 641 2 730 158 3 069 218 Presumed and confirmed 66 484 221 980 500 806 454 532 495 238 953 535 1 607 079 1 296 277 995 157 2 708 497* 1 980 804 2 223 562 Microscopy examined 63 695 55 943 139 241 189 481 112 007 163 370 265 673 237 910 246 348 Microscopy positive 36 943 41 436 106 524 144 924 28 855 117 267 196 413 177 742 189 114 RDT examined 105 521 191 569 369 208 724 303 1 537 852 536 887 1 181 578 2 781 622 2 181 204 2 504 262 RDT positive 87 566 126 758 253 652 492 309 1 094 393 383 058 854 852 2 220 547 1 563 228 1 813 035 Chad Suspected cases 743 471 722 654 1 737 195 1 641 285 2 032 301 2 943 595 1 941 489 2 779 742 2 955 271 2 584 679 Presumed and confirmed 544 243 528 454 660 575 1 272 841 1 513 772 1 490 556 1 402 215 1 962 372 1 364 706 1 910 518 1 890 264* 1 811 859 Microscopy examined 89 749 69 789 1 063 293 1 584 525 190 006 211 816 250 117 284 426 Microscopy positive 75 342 86 348 7 710 206 082 160 260 149 574 720 765 1 064 354 137 501 152 127 193 816 191 628 RDT examined 309 927 114 122 621 469 1 137 455 937 775 861 561 1 359 070 1 751 483 2 260 256 1 873 598 1 788 058 RDT positive 125 106 94 778 548 483 753 772 637 472 574 003 898 018 1 227 205 1 480 402 1 350 378 1 226 911 245 ANNEx 4 – h. REPORTED MALARIA CASES BY METhOD OF CONFIRMATION, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 AFRICAN Comoros2 Suspected cases 159 976 135 248 168 043 185 779 103 545 117 762 116 692 229 445 119 592 185 045** 175 364 168 942 Presumed and confirmed 103 670 76 661 65 139 62 565 2 465 2 101 1 734 3 896 19 682 17 697ˆ 4 546* 10 547* Microscopy examined 87 595 63 217 125 030 154 824 93 444 89 634 71 902 130 134 90 956 158 670 133 024 120 785 Microscopy positive 35 199 22 278 45 507 46 130 1 987 963 559 1 325 9 197 19 029 3 339 7 631 RDT examined 5 249 20 226 27 714 21 546 9 839 27 911 44 523 99 311 24 567 46 172 42 340 48 157 RDT positive 1 339 2 578 4 333 7 026 216 921 908 2 571 6 416 20 535 1 207 2 916 Imported cases 98 0 10 Congo Suspected cases 209 169 290 346 300 592 466 254 322 916 385 729 594 237 146 262 563 787 Presumed and confirmed 446 656 277 263 120 319 183 026 248 159 264 574 374 252 297 652 324 615 545 796 103 692 321 404* Microscopy examined 69 375 88 764 87 547 202 922 153 203 178 017 166 278 80 110 234 919 Microscopy positive 37 744 120 319 43 232 54 523 51 529 134 612 127 939 116 903 117 837 55 348 41 491 RDT examined 19 746 60 927 53 998 197 080 RDT positive 11 800 37 235 36 190 148 125 Côte d'Ivoire Suspected cases 2 607 856 3 423 623 6 003 033 6 418 571 5 216 344 5 560 136 7 262 684 6 706 148 8 280 575 7 992 806 9 629 227 Presumed and confirmed 1 721 461 2 588 004 2 795 919 4 725 798 4 658 774 3 606 725 3 754 504 4 152 065 5 189 974 5 950 336 5 083 548 7 633 965 Microscopy examined 49 828 195 546 395 914 568 562 811 426 975 507 1 221 845 1 132 659 1 447 694 1 437 608 1 572 997 Microscopy positive 62 726 29 976 107 563 215 104 306 926 478 870 579 566 588 969 696 124 918 371 932 627 1 089 041 RDT examined 1 572 785 3 405 647 4 904 066 4 174 097 4 584 629 5 923 555 5 042 040 6 152 962 4 837 781 7 197 967 RDT positive 1 033 064 2 309 222 3 405 905 2 897 034 3 174 938 3 445 812 4 070 353 5 016 807 4 048 013 6 206 027 Democratic Republic of the Congo Suspected cases 10 568 756 12 018 784 11 993 189 14 877 406 15 064 146 17 617 219 23 443 227 23 195 284 23 833 694 32 067 354 32 954 190 32 438 379 Presumed and confirmed 9 252 959 9 442 144 9 128 398 11 368 481 10 288 519 12 538 805 16 888 006 16 888 842 18 208 440 21 608 681* 24 959 997 26 496 523* Microscopy examined 3 678 849 4 226 533 4 329 318 4 126 129 3 533 165 2 877 585 2 810 067 1 981 621 1 926 455 2 152 433 2 067 978 2 450 106 Microscopy positive 2 374 930 2 700 818 2 656 864 2 611 478 2 126 554 1 902 640 1 847 143 1 291 717 995 577 1 128 371 1 214 424 1 646 553 RDT examined 54 728 2 912 088 3 327 071 6 102 683 11 530 981 14 739 634 20 566 284 21 117 823 20 671 006 26 963 687 28 054 832 26 740 915 RDT positive 42 850 1 861 163 2 134 734 4 108 409 8 161 965 10 636 165 14 973 987 15 501 285 15 976 630 20 480 310 21 376 223 21 602 612 Equatorial Guinea Suspected cases 83 639 40 704 45 792 59 358 57 129 68 058 318 779 91 217 43 533 94 656 Presumed and confirmed 78 095 37 267 20 890 28 438 20 417 15 142 147 714 15 725 10 926 43 897 Microscopy examined 42 585 23 004 33 245 27 039 47 322 21 831 239 938 13 127 8 395 43 417 Microscopy positive 39 636 20 601 13 196 11 235 17 685 8 564 125 623 6 800 4 135 14 787 RDT examined 16 772 2 899 6 826 20 286 9 807 46 227 78 841 78 090 33 174 33 246 RDT positive 14 177 1 865 1 973 5 170 2 732 6 578 22 091 8 925 4 827 11 117 Eritrea Suspected cases 96 792 97 479 138 982 134 183 186 358 155 782 139 798 205 836 253 687 452 673 346 565 343 054 Presumed and confirmed 53 750 39 567 42 178 34 678 55 491 32 974 80 450 55 588 48 326 93 878 75 756 44 036 Microscopy examined 79 024 67 190 84 861 81 541 63 766 59 268 83 599 74 962 70 465 116 666 101 966 98 068 Microscopy positive 13 894 15 308 11 557 10 890 10 993 8 332 24 251 14 519 10 325 18 117 17 936 10 114 RDT examined 25 570 33 758 39 281 117 635 91 576 129 291 181 336 336 007 241 982 244 413 RDT positive 22 088 19 540 10 258 10 427 39 541 19 704 39 486 36 115 75 761 56 105 33 349 Eswatini2 Suspected cases 1 249 3 212 10 285 34 866 27 979 12 894 Presumed and confirmed 1 722 797 626 962 711 475ˆ 317ˆ 1 127ˆ 957ˆ 589 325 581 Microscopy examined 1 249 371 1 526 15 434 8 047 8 246 Microscopy positive 87 130 345 488 711 43 141 68 957 207 121 207 RDT examined 2 841 8 759 19 432 19 932 4 575 RDT positive 181 419 217 474 452 458 1 594 11 382 204 374 Imported cases 170 153 234 322 157 67 687 271 337 82 76 Ethiopia Suspected cases 5 420 110 5 487 972 5 962 646 9 243 894 7 457 765 5 987 580 6 611 801 6 471 958 5 913 799 6 708 222 7 509 602 6 716 458 Presumed and confirmed 4 107 396 3 549 613 3 876 745 3 316 013 2 513 863 2 174 707 1 962 996 1 755 748 1 206 891 1 015 792 1 848 231 1 487 758 Microscopy examined 2 509 543 3 418 719 3 778 479 8 573 335 7 062 717 5 679 932 6 367 309 6 246 949 5 668 995 6 596 925 7 400 644 6 625 851 Microscopy positive 1 196 829 1 480 360 1 692 578 2 645 454 2 118 815 1 867 059 1 718 504 1 530 739 962 087 904 495 1 743 755 1 397 151 RDT examined RDT positive Imported cases 836 Gabon Suspected cases 233 770 238 483 256 531 256 183 285 489 202 989 212 092 1 022 022 214 286 206 908 229 360 Presumed and confirmed 185 105 178 822 188 089 185 196 185 996 217 287 161 508 157 639 797 278 142 917 127 500 141 195 Microscopy examined 54 714 66 018 90 185 90 275 79 308 62 658 70 820 264 676 75 819 80 266 104 166 Microscopy positive 12 816 18 694 26 432 27 687 20 390 22 419 28 297 88 112 31 184 33 349 44 330 RDT examined 7 887 4 129 10 132 11 812 12 761 2 738 18 877 71 787 47 712 48 330 38 736 RDT positive 1 120 1 059 2 550 4 213 3 477 1 496 6 947 23 607 21 998 20 310 20 627 Gambia Suspected cases 492 062 862 442 889 494 603 424 908 082 874 690 656 212 706 868 602 947 537 205 615 187 Presumed and confirmed 194 009 268 020 313 469 281 550 168 256 255 403 164 421 83 668 88 654 53 386 75 801 77 287 Microscopy examined 290 842 172 241 156 580 236 329 286 111 272 604 165 793 77 491 171 668 150 585 198 658 116 147 Microscopy positive 52 245 71 588 29 325 65 666 66 253 49 649 26 397 11 343 14 510 10 982 22 226 12 753 RDT examined 123 564 705 862 614 128 317 313 626 423 707 215 573 093 533 994 452 362 338 547 496 330 RDT positive 64 108 196 432 284 144 176 847 102 003 196 699 136 342 66 697 72 938 42 404 53 575 64 534 246 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – h. REPORTED MALARIA CASES BY METhOD OF CONFIRMATION, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 AFRICAN Comoros2 Suspected cases 159 976 135 248 168 043 185 779 103 545 117 762 116 692 229 445 119 592 185 045** 175 364 168 942 Presumed and confirmed 103 670 76 661 65 139 62 565 2 465 2 101 1 734 3 896 19 682 17 697ˆ 4 546* 10 547* Microscopy examined 87 595 63 217 125 030 154 824 93 444 89 634 71 902 130 134 90 956 158 670 133 024 120 785 Microscopy positive 35 199 22 278 45 507 46 130 1 987 963 559 1 325 9 197 19 029 3 339 7 631 RDT examined 5 249 20 226 27 714 21 546 9 839 27 911 44 523 99 311 24 567 46 172 42 340 48 157 RDT positive 1 339 2 578 4 333 7 026 216 921 908 2 571 6 416 20 535 1 207 2 916 Imported cases 98 0 10 Congo Suspected cases 209 169 290 346 300 592 466 254 322 916 385 729 594 237 146 262 563 787 Presumed and confirmed 446 656 277 263 120 319 183 026 248 159 264 574 374 252 297 652 324 615 545 796 103 692 321 404* Microscopy examined 69 375 88 764 87 547 202 922 153 203 178 017 166 278 80 110 234 919 Microscopy positive 37 744 120 319 43 232 54 523 51 529 134 612 127 939 116 903 117 837 55 348 41 491 RDT examined 19 746 60 927 53 998 197 080 RDT positive 11 800 37 235 36 190 148 125 Côte d'Ivoire Suspected cases 2 607 856 3 423 623 6 003 033 6 418 571 5 216 344 5 560 136 7 262 684 6 706 148 8 280 575 7 992 806 9 629 227 Presumed and confirmed 1 721 461 2 588 004 2 795 919 4 725 798 4 658 774 3 606 725 3 754 504 4 152 065 5 189 974 5 950 336 5 083 548 7 633 965 Microscopy examined 49 828 195 546 395 914 568 562 811 426 975 507 1 221 845 1 132 659 1 447 694 1 437 608 1 572 997 Microscopy positive 62 726 29 976 107 563 215 104 306 926 478 870 579 566 588 969 696 124 918 371 932 627 1 089 041 RDT examined 1 572 785 3 405 647 4 904 066 4 174 097 4 584 629 5 923 555 5 042 040 6 152 962 4 837 781 7 197 967 RDT positive 1 033 064 2 309 222 3 405 905 2 897 034 3 174 938 3 445 812 4 070 353 5 016 807 4 048 013 6 206 027 Democratic Republic of the Congo Suspected cases 10 568 756 12 018 784 11 993 189 14 877 406 15 064 146 17 617 219 23 443 227 23 195 284 23 833 694 32 067 354 32 954 190 32 438 379 Presumed and confirmed 9 252 959 9 442 144 9 128 398 11 368 481 10 288 519 12 538 805 16 888 006 16 888 842 18 208 440 21 608 681* 24 959 997 26 496 523* Microscopy examined 3 678 849 4 226 533 4 329 318 4 126 129 3 533 165 2 877 585 2 810 067 1 981 621 1 926 455 2 152 433 2 067 978 2 450 106 Microscopy positive 2 374 930 2 700 818 2 656 864 2 611 478 2 126 554 1 902 640 1 847 143 1 291 717 995 577 1 128 371 1 214 424 1 646 553 RDT examined 54 728 2 912 088 3 327 071 6 102 683 11 530 981 14 739 634 20 566 284 21 117 823 20 671 006 26 963 687 28 054 832 26 740 915 RDT positive 42 850 1 861 163 2 134 734 4 108 409 8 161 965 10 636 165 14 973 987 15 501 285 15 976 630 20 480 310 21 376 223 21 602 612 Equatorial Guinea Suspected cases 83 639 40 704 45 792 59 358 57 129 68 058 318 779 91 217 43 533 94 656 Presumed and confirmed 78 095 37 267 20 890 28 438 20 417 15 142 147 714 15 725 10 926 43 897 Microscopy examined 42 585 23 004 33 245 27 039 47 322 21 831 239 938 13 127 8 395 43 417 Microscopy positive 39 636 20 601 13 196 11 235 17 685 8 564 125 623 6 800 4 135 14 787 RDT examined 16 772 2 899 6 826 20 286 9 807 46 227 78 841 78 090 33 174 33 246 RDT positive 14 177 1 865 1 973 5 170 2 732 6 578 22 091 8 925 4 827 11 117 Eritrea Suspected cases 96 792 97 479 138 982 134 183 186 358 155 782 139 798 205 836 253 687 452 673 346 565 343 054 Presumed and confirmed 53 750 39 567 42 178 34 678 55 491 32 974 80 450 55 588 48 326 93 878 75 756 44 036 Microscopy examined 79 024 67 190 84 861 81 541 63 766 59 268 83 599 74 962 70 465 116 666 101 966 98 068 Microscopy positive 13 894 15 308 11 557 10 890 10 993 8 332 24 251 14 519 10 325 18 117 17 936 10 114 RDT examined 25 570 33 758 39 281 117 635 91 576 129 291 181 336 336 007 241 982 244 413 RDT positive 22 088 19 540 10 258 10 427 39 541 19 704 39 486 36 115 75 761 56 105 33 349 Eswatini2 Suspected cases 1 249 3 212 10 285 34 866 27 979 12 894 Presumed and confirmed 1 722 797 626 962 711 475ˆ 317ˆ 1 127ˆ 957ˆ 589 325 581 Microscopy examined 1 249 371 1 526 15 434 8 047 8 246 Microscopy positive 87 130 345 488 711 43 141 68 957 207 121 207 RDT examined 2 841 8 759 19 432 19 932 4 575 RDT positive 181 419 217 474 452 458 1 594 11 382 204 374 Imported cases 170 153 234 322 157 67 687 271 337 82 76 Ethiopia Suspected cases 5 420 110 5 487 972 5 962 646 9 243 894 7 457 765 5 987 580 6 611 801 6 471 958 5 913 799 6 708 222 7 509 602 6 716 458 Presumed and confirmed 4 107 396 3 549 613 3 876 745 3 316 013 2 513 863 2 174 707 1 962 996 1 755 748 1 206 891 1 015 792 1 848 231 1 487 758 Microscopy examined 2 509 543 3 418 719 3 778 479 8 573 335 7 062 717 5 679 932 6 367 309 6 246 949 5 668 995 6 596 925 7 400 644 6 625 851 Microscopy positive 1 196 829 1 480 360 1 692 578 2 645 454 2 118 815 1 867 059 1 718 504 1 530 739 962 087 904 495 1 743 755 1 397 151 RDT examined RDT positive Imported cases 836 Gabon Suspected cases 233 770 238 483 256 531 256 183 285 489 202 989 212 092 1 022 022 214 286 206 908 229 360 Presumed and confirmed 185 105 178 822 188 089 185 196 185 996 217 287 161 508 157 639 797 278 142 917 127 500 141 195 Microscopy examined 54 714 66 018 90 185 90 275 79 308 62 658 70 820 264 676 75 819 80 266 104 166 Microscopy positive 12 816 18 694 26 432 27 687 20 390 22 419 28 297 88 112 31 184 33 349 44 330 RDT examined 7 887 4 129 10 132 11 812 12 761 2 738 18 877 71 787 47 712 48 330 38 736 RDT positive 1 120 1 059 2 550 4 213 3 477 1 496 6 947 23 607 21 998 20 310 20 627 Gambia Suspected cases 492 062 862 442 889 494 603 424 908 082 874 690 656 212 706 868 602 947 537 205 615 187 Presumed and confirmed 194 009 268 020 313 469 281 550 168 256 255 403 164 421 83 668 88 654 53 386 75 801 77 287 Microscopy examined 290 842 172 241 156 580 236 329 286 111 272 604 165 793 77 491 171 668 150 585 198 658 116 147 Microscopy positive 52 245 71 588 29 325 65 666 66 253 49 649 26 397 11 343 14 510 10 982 22 226 12 753 RDT examined 123 564 705 862 614 128 317 313 626 423 707 215 573 093 533 994 452 362 338 547 496 330 RDT positive 64 108 196 432 284 144 176 847 102 003 196 699 136 342 66 697 72 938 42 404 53 575 64 534 247 ANNEx 4 – h. REPORTED MALARIA CASES BY METhOD OF CONFIRMATION, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 AFRICAN Ghana Suspected cases 5 107 626 5 121 411 12 656 535 8 507 245 10 748 502 15 946 366 15 742 112 19 069 870 15 542 218 11 977 117 10 388 444 12 071 445 Presumed and confirmed 3 900 311 4 207 941 10 754 320 7 259 892 8 566 002 11 678 306 11 451 328 13 472 089 11 154 400 6 703 687 5 604 746 6 077 543 Microscopy examined 2 031 674 1 172 838 4 219 097 1 394 249 1 987 959 2 023 581 2 594 918 2 495 536 2 659 067 3 004 989 3 108 665 3 652 265 Microscopy positive 1 029 384 624 756 2 971 699 721 898 970 448 934 304 1 189 012 1 089 799 1 105 348 1 160 426 1 401 009 1 327 675 RDT examined 247 278 781 892 1 438 284 1 496 746 3 610 453 7 901 575 7 124 845 10 105 400 6 660 205 8 383 708 6 847 836 8 057 955 RDT positive 42 253 416 504 783 467 921 744 2 445 464 4 722 792 4 239 967 5 913 356 3 826 106 4 954 841 3 771 794 4 419 910 Guinea Suspected cases 1 276 057 1 254 937 1 503 035 2 134 543 2 608 481 3 733 346 3 431 504 4 162 127 Presumed and confirmed 1 117 182 1 189 016 1 261 951 775 341 1 595 828 895 016 992 146 1 335 323 1 246 598 2 143 225 2 548 635 2 422 374 Microscopy examined 43 549 116 767 78 377 79 233 99 083 131 715 184 697 191 421 234 269 Microscopy positive 20 936 5 450 191 421 63 353 82 818 52 211 53 805 64 211 77 119 112 966 117 568 141 621 RDT examined 139 066 1 092 523 1 423 802 2 035 460 2 445 164 3 498 748 3 205 353 3 879 319 RDT positive 90 124 148 837 147 904 577 389 758 768 938 341 1 271 112 1 137 877 2 030 259 1 891 408 2 280 753 Guinea-Bissau Suspected cases 195 006 300 233 237 398 238 580 330 533 413 727 398 429 498 879 469 640 497 916 Presumed and confirmed 140 143 174 986 129 684 132 176 102 945 150 085 156 523 152 619 171 075 160 907 Microscopy examined 48 799 57 698 61 048 58 909 106 882 123 810 146 708 157 970 149 423 151 262 Microscopy positive 30 239 21 320 23 547 17 733 35 546 45 789 53 014 53 770 45 564 45 675 RDT examined 56 455 139 531 97 047 102 079 218 130 289 917 251 669 340 909 320 217 341 365 RDT positive 20 152 50 662 26 834 36 851 61 878 104 296 103 457 98 849 125 511 115 232 Kenya Suspected cases 7 557 454 13 127 058 12 883 521 14 742 401 15 204 056 16 037 285 16 290 286 15 362 146 18 435 472 8 911 133 14 060 361 13 748 015 Presumed and confirmed 6 071 583 11 120 812 9 335 951 9 790 796 9 698 529 8 219 230 8 647 072 8 462 076 10 875 734 5 019 389* 6 875 369* 4 255 874 Microscopy examined 2 384 402 3 009 051 4 836 617 6 606 885 7 444 865 7 772 329 6 167 609 5 952 353 4 282 912 6 591 588 7 503 911 Microscopy positive 898 531 1 002 805 1 426 719 2 060 608 2 415 950 1 025 508 1 569 045 2 215 665 827 947 4 656 702 1 646 648 1 496 769 RDT examined 164 424 719 849 912 217 2 087 003 4 540 401 4 554 743 5 594 916 514 579 4 179 731 5 816 987 RDT positive 26 752 314 521 435 605 1 015 769 1 495 751 1 391 361 1 490 143 362 687 2 012 522 2 331 988 Liberia Suspected cases 3 087 659 2 896 874 2 441 800 2 202 213 2 450 878 2 403 783 3 105 390 2 034 027 1 726 913 1 507 881 Presumed and confirmed 2 675 816 2 488 331 1 805 546 1 483 676 1 083 513 1 835 238 2 343 410 1 366 176 1 041 800 983 978 Microscopy examined 335 973 728 443 772 362 818 352 1 318 801 509 062 649 096 715 643 640 901 478 408 Microscopy positive 212 927 577 641 507 967 496 269 302 708 305 981 381 781 425 639 325 658 304 818 RDT examined 998 043 1 601 259 1 276 521 1 144 405 929 788 1 001 194 1 304 021 1 045 323 960 057 957 931 RDT positive 709 246 1 343 518 904 662 747 951 578 516 635 730 809 356 667 476 590 187 607 618 Madagascar2 Suspected cases 719 967 805 701 1 066 564 1 156 468 1 357 857 2 386 641 2 567 451 2 610 069 2 439 906 2 866 191 3 829 230 4 571 944** Presumed and confirmed 293 910 255 814 456 795 472 644 688 852 1 366 205 1 216 077 1 163 807 1 078 140 984 304 1 950 471 2 339 103ˆ Microscopy examined 24 393 34 813 38 453 42 573 37 362 39 604 33 085 34 265 43 759 40 619 30 406 12 495 Microscopy positive 2 173 3 447 3 667 4 947 3 853 4 748 3 734 5 134 7 400 5 932 5 075 6 759 RDT examined 604 114 739 572 974 216 1 074 701 1 102 567 1 920 489 2 004 313 2 397 849 2 290 797 2 685 182 3 798 824 4 571 944 RDT positive 200 277 221 051 399 233 428 503 467 071 934 909 682 290 980 718 965 390 964 896 1 945 396 2 339 103 Imported cases 712 1 167 7 116 1 732 203 Malawi Suspected cases 5 734 906 6 528 505 5 787 441 7 703 651 8 518 905 9 239 462 10 530 601 11 513 684 10 994 966 12 645 404 13 303 404 Presumed and confirmed 6 851 108 5 338 701 4 922 596 3 906 838 5 065 703 4 933 416 5 165 386 5 936 348 5 865 476 5 205 920 7 169 642 6 982 423 Microscopy examined 119 996 406 907 132 475 198 534 216 643 240 212 127 752 129 575 103 754 166 959 173 924 Microscopy positive 50 526 283 138 44 501 77 635 75 923 96 538 46 099 34 735 30 328 81 201 67 963 RDT examined 580 708 2 763 986 3 029 020 5 344 724 7 030 084 8 661 237 9 413 944 11 384 109 10 861 320 12 439 185 13 075 023 RDT positive 253 973 1 281 846 1 236 391 2 827 675 3 585 315 4 730 835 4 901 344 5 830 741 5 153 779 7 057 864 6 880 537 Mali Suspected cases 3 351 419 2 628 593 3 076 029 3 246 800 4 692 412 3 778 535 3 624 885 3 725 896 5 232 430 4 382 988 4 908 357 Presumed and confirmed 2 191 285 1 961 070 2 204 724 2 510 534 2 593 880 3 543 576 2 465 914 2 456 639 2 614 104 3 607 237 3 379 651 3 561 370 Microscopy examined 397 723 437 903 594 303 736 392 773 071 Microscopy positive 97 995 190 337 219 637 243 151 235 212 276 673 301 880 468 011 541 755 582 149 RDT examined 1 399 921 974 558 2 072 435 2 692 773 3 603 344 3 623 719 3 047 741 3 019 364 4 252 425 2 927 529 3 778 046 RDT positive 239 787 307 035 788 487 1 316 603 1 820 216 2 211 357 2 075 886 2 000 545 2 043 595 2 753 524 2 124 511 2 621 981 Mauritania Suspected cases 239 795 191 726 209 955 190 446 203 991 233 362 192 980 214 087 221 121 155 658 160 032 96 044 Presumed and confirmed 234 041 182 909 206 685 182 947 172 326 195 740 171 348 182 677 175 841 135 120 144 709 49 560 Microscopy examined 5 449 3 752 1 865 5 510 Microscopy positive 909 1 130 255 957 RDT examined 2 299 7 991 3 293 3 576 47 500 60 253 50 788 51 515 75 889 35 407 27 748 49 972 RDT positive 1 085 1 796 1 633 630 15 835 22 631 29 156 20 105 30 609 14 869 12 425 18 660 Mayotte2 Suspected cases 2 023 1 214 1 463 Presumed and confirmed 396 92 72 82 15 11 28 19 47 Microscopy examined 2 023 1 214 1 463 Microscopy positive 396 92 72 82 15 11 28 19 47 RDT examined RDT positive Imported cases 236 51 47 71 14 10 10 10 44 248 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – h. REPORTED MALARIA CASES BY METhOD OF CONFIRMATION, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 AFRICAN Ghana Suspected cases 5 107 626 5 121 411 12 656 535 8 507 245 10 748 502 15 946 366 15 742 112 19 069 870 15 542 218 11 977 117 10 388 444 12 071 445 Presumed and confirmed 3 900 311 4 207 941 10 754 320 7 259 892 8 566 002 11 678 306 11 451 328 13 472 089 11 154 400 6 703 687 5 604 746 6 077 543 Microscopy examined 2 031 674 1 172 838 4 219 097 1 394 249 1 987 959 2 023 581 2 594 918 2 495 536 2 659 067 3 004 989 3 108 665 3 652 265 Microscopy positive 1 029 384 624 756 2 971 699 721 898 970 448 934 304 1 189 012 1 089 799 1 105 348 1 160 426 1 401 009 1 327 675 RDT examined 247 278 781 892 1 438 284 1 496 746 3 610 453 7 901 575 7 124 845 10 105 400 6 660 205 8 383 708 6 847 836 8 057 955 RDT positive 42 253 416 504 783 467 921 744 2 445 464 4 722 792 4 239 967 5 913 356 3 826 106 4 954 841 3 771 794 4 419 910 Guinea Suspected cases 1 276 057 1 254 937 1 503 035 2 134 543 2 608 481 3 733 346 3 431 504 4 162 127 Presumed and confirmed 1 117 182 1 189 016 1 261 951 775 341 1 595 828 895 016 992 146 1 335 323 1 246 598 2 143 225 2 548 635 2 422 374 Microscopy examined 43 549 116 767 78 377 79 233 99 083 131 715 184 697 191 421 234 269 Microscopy positive 20 936 5 450 191 421 63 353 82 818 52 211 53 805 64 211 77 119 112 966 117 568 141 621 RDT examined 139 066 1 092 523 1 423 802 2 035 460 2 445 164 3 498 748 3 205 353 3 879 319 RDT positive 90 124 148 837 147 904 577 389 758 768 938 341 1 271 112 1 137 877 2 030 259 1 891 408 2 280 753 Guinea-Bissau Suspected cases 195 006 300 233 237 398 238 580 330 533 413 727 398 429 498 879 469 640 497 916 Presumed and confirmed 140 143 174 986 129 684 132 176 102 945 150 085 156 523 152 619 171 075 160 907 Microscopy examined 48 799 57 698 61 048 58 909 106 882 123 810 146 708 157 970 149 423 151 262 Microscopy positive 30 239 21 320 23 547 17 733 35 546 45 789 53 014 53 770 45 564 45 675 RDT examined 56 455 139 531 97 047 102 079 218 130 289 917 251 669 340 909 320 217 341 365 RDT positive 20 152 50 662 26 834 36 851 61 878 104 296 103 457 98 849 125 511 115 232 Kenya Suspected cases 7 557 454 13 127 058 12 883 521 14 742 401 15 204 056 16 037 285 16 290 286 15 362 146 18 435 472 8 911 133 14 060 361 13 748 015 Presumed and confirmed 6 071 583 11 120 812 9 335 951 9 790 796 9 698 529 8 219 230 8 647 072 8 462 076 10 875 734 5 019 389* 6 875 369* 4 255 874 Microscopy examined 2 384 402 3 009 051 4 836 617 6 606 885 7 444 865 7 772 329 6 167 609 5 952 353 4 282 912 6 591 588 7 503 911 Microscopy positive 898 531 1 002 805 1 426 719 2 060 608 2 415 950 1 025 508 1 569 045 2 215 665 827 947 4 656 702 1 646 648 1 496 769 RDT examined 164 424 719 849 912 217 2 087 003 4 540 401 4 554 743 5 594 916 514 579 4 179 731 5 816 987 RDT positive 26 752 314 521 435 605 1 015 769 1 495 751 1 391 361 1 490 143 362 687 2 012 522 2 331 988 Liberia Suspected cases 3 087 659 2 896 874 2 441 800 2 202 213 2 450 878 2 403 783 3 105 390 2 034 027 1 726 913 1 507 881 Presumed and confirmed 2 675 816 2 488 331 1 805 546 1 483 676 1 083 513 1 835 238 2 343 410 1 366 176 1 041 800 983 978 Microscopy examined 335 973 728 443 772 362 818 352 1 318 801 509 062 649 096 715 643 640 901 478 408 Microscopy positive 212 927 577 641 507 967 496 269 302 708 305 981 381 781 425 639 325 658 304 818 RDT examined 998 043 1 601 259 1 276 521 1 144 405 929 788 1 001 194 1 304 021 1 045 323 960 057 957 931 RDT positive 709 246 1 343 518 904 662 747 951 578 516 635 730 809 356 667 476 590 187 607 618 Madagascar2 Suspected cases 719 967 805 701 1 066 564 1 156 468 1 357 857 2 386 641 2 567 451 2 610 069 2 439 906 2 866 191 3 829 230 4 571 944** Presumed and confirmed 293 910 255 814 456 795 472 644 688 852 1 366 205 1 216 077 1 163 807 1 078 140 984 304 1 950 471 2 339 103ˆ Microscopy examined 24 393 34 813 38 453 42 573 37 362 39 604 33 085 34 265 43 759 40 619 30 406 12 495 Microscopy positive 2 173 3 447 3 667 4 947 3 853 4 748 3 734 5 134 7 400 5 932 5 075 6 759 RDT examined 604 114 739 572 974 216 1 074 701 1 102 567 1 920 489 2 004 313 2 397 849 2 290 797 2 685 182 3 798 824 4 571 944 RDT positive 200 277 221 051 399 233 428 503 467 071 934 909 682 290 980 718 965 390 964 896 1 945 396 2 339 103 Imported cases 712 1 167 7 116 1 732 203 Malawi Suspected cases 5 734 906 6 528 505 5 787 441 7 703 651 8 518 905 9 239 462 10 530 601 11 513 684 10 994 966 12 645 404 13 303 404 Presumed and confirmed 6 851 108 5 338 701 4 922 596 3 906 838 5 065 703 4 933 416 5 165 386 5 936 348 5 865 476 5 205 920 7 169 642 6 982 423 Microscopy examined 119 996 406 907 132 475 198 534 216 643 240 212 127 752 129 575 103 754 166 959 173 924 Microscopy positive 50 526 283 138 44 501 77 635 75 923 96 538 46 099 34 735 30 328 81 201 67 963 RDT examined 580 708 2 763 986 3 029 020 5 344 724 7 030 084 8 661 237 9 413 944 11 384 109 10 861 320 12 439 185 13 075 023 RDT positive 253 973 1 281 846 1 236 391 2 827 675 3 585 315 4 730 835 4 901 344 5 830 741 5 153 779 7 057 864 6 880 537 Mali Suspected cases 3 351 419 2 628 593 3 076 029 3 246 800 4 692 412 3 778 535 3 624 885 3 725 896 5 232 430 4 382 988 4 908 357 Presumed and confirmed 2 191 285 1 961 070 2 204 724 2 510 534 2 593 880 3 543 576 2 465 914 2 456 639 2 614 104 3 607 237 3 379 651 3 561 370 Microscopy examined 397 723 437 903 594 303 736 392 773 071 Microscopy positive 97 995 190 337 219 637 243 151 235 212 276 673 301 880 468 011 541 755 582 149 RDT examined 1 399 921 974 558 2 072 435 2 692 773 3 603 344 3 623 719 3 047 741 3 019 364 4 252 425 2 927 529 3 778 046 RDT positive 239 787 307 035 788 487 1 316 603 1 820 216 2 211 357 2 075 886 2 000 545 2 043 595 2 753 524 2 124 511 2 621 981 Mauritania Suspected cases 239 795 191 726 209 955 190 446 203 991 233 362 192 980 214 087 221 121 155 658 160 032 96 044 Presumed and confirmed 234 041 182 909 206 685 182 947 172 326 195 740 171 348 182 677 175 841 135 120 144 709 49 560 Microscopy examined 5 449 3 752 1 865 5 510 Microscopy positive 909 1 130 255 957 RDT examined 2 299 7 991 3 293 3 576 47 500 60 253 50 788 51 515 75 889 35 407 27 748 49 972 RDT positive 1 085 1 796 1 633 630 15 835 22 631 29 156 20 105 30 609 14 869 12 425 18 660 Mayotte2 Suspected cases 2 023 1 214 1 463 Presumed and confirmed 396 92 72 82 15 11 28 19 47 Microscopy examined 2 023 1 214 1 463 Microscopy positive 396 92 72 82 15 11 28 19 47 RDT examined RDT positive Imported cases 236 51 47 71 14 10 10 10 44 249 ANNEx 4 – h. REPORTED MALARIA CASES BY METhOD OF CONFIRMATION, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 AFRICAN Mozambique Suspected cases 6 097 263 7 059 112 6 265 567 8 565 263 12 612 456 15 057 398 17 490 954 17 463 976 18 791 446 21 180 727 19 516 184 19 238 020 Presumed and confirmed 3 381 371 3 344 413 3 296 332 4 261 529 7 452 733 8 306 986 10 373 341 9 981 277 10 339 330 11 781 516 11 331 009 10 106 592 Microscopy examined 1 950 933 2 504 720 2 546 213 2 058 998 2 295 823 2 313 129 1 886 154 1 699 589 1 909 051 1 669 097 1 293 955 833 970 Microscopy positive 644 568 1 093 742 886 143 774 891 1 009 496 735 750 674 697 700 282 743 435 608 016 473 160 271 441 RDT examined 2 287 536 2 966 853 2 276 298 5 526 908 10 271 075 12 660 097 14 922 332 15 675 711 16 847 537 19 465 040 18 209 905 18 393 265 RDT positive 878 009 663 132 967 133 2 507 281 6 397 679 7 487 064 9 016 176 9 192 319 9 561 037 11 126 910 10 845 525 9 824 366 Namibia2 Suspected cases 39 855 61 145** 10 844 32 495** 185 078** 207 612** 308 414** 616 513** 394 822** 295 367** 258 145** 263 202** Presumed and confirmed 25 889 14 071ˆ 3 163 4 775ˆ 15 692ˆ 12 050ˆ 24 869ˆ 66 141ˆ 36 451ˆ 3 404ˆ 13 636ˆ 13 738ˆ Microscopy examined 14 522 13 262 7 875 1 507 1 894 1 471 1 778 1 778 1 215 511 809 245 Microscopy positive 556 335 194 136 222 118 329 364 289 301 168 100 RDT examined 48 599 32 495 185 078 207 612 308 414 616 513 394 822 295 367 258 145 263 202 RDT positive 1 525 4 775 15 692 12 050 24 869 66 141 36 451 3 404 13 636 13 738 Imported cases 2 888 3 980 11 874 4 021 1 064 1 305 759 Niger Suspected cases 11 231 308 5 315 185 7 818 305 5 584 223 7 100 212 4 671 411 7 347 200 4 013 178 4 810 919 5 582 958 6 343 569 6 472 227 Presumed and confirmed 4 231 896 4 401 099 6 398 943 4 333 905 5 247 235 3 937 742 5 166 336 2 761 268 3 358 058 3 771 451 4 377 938 4 659 909 Microscopy examined 165 514 130 658 1 781 505 1 799 299 2 872 710 295 229 3 198 194 203 583 213 795 303 115 337 657 264 694 Microscopy positive 49 285 68 529 1 119 929 1 176 711 1 953 279 206 660 2 120 515 125 856 121 657 211 783 223 601 198 998 RDT examined 7 476 672 1 622 013 1 967 117 1 824 610 2 944 035 2 830 548 3 240 780 3 809 595 4 285 516 5 279 843 5 915 007 5 624 653 RDT positive 593 489 770 056 1 209 331 1 196 880 2 010 489 2 185 448 2 137 595 2 635 412 2 924 793 3 559 668 4 154 337 3 845 709 Nigeria Suspected cases 5 221 656 11 789 970 21 659 831 20 558 467 20 243 915 29 113 322 25 106 551 25 381 459 29 489 245 27 370 935 30 202 722 Presumed and confirmed 3 873 463 4 306 945 6 938 519 12 830 911 17 257 495 16 702 261 23 956 669 20 219 268 20 482 380 23 376 793 21 580 055 23 608 797 Microscopy examined 672 185 1 953 399 1 633 960 1 681 469 839 849 901 141 1 055 444 1 428 731 3 298 156 3 086 039 3 405 012 Microscopy positive 523 513 1 233 654 556 871 618 363 749 118 1 023 273 2 476 514 2 312 163 2 559 742 RDT examined 45 924 242 526 2 898 052 7 194 960 10 191 825 10 770 388 17 853 794 16 919 717 18 018 372 22 621 211 21 030 081 24 514 099 RDT positive 27 674 7 338 668 7 511 712 12 979 919 12 338 760 13 524 751 17 330 401 16 013 077 18 765 444 Rwanda Suspected cases 2 883 666 1 802 382 3 095 386 3 064 585 4 178 206 6 093 114 7 502 174 11 186 029 9 666 424 8 829 176 6 879 911 5 576 953 Presumed and confirmed 669 322 273 293 483 470 962 618 1 623 176 2 505 794 3 380 568 5 940 533 4 231 883 3 612 822 2 043 392 1 163 670* Microscopy examined 2 708 973 1 602 271 2 904 793 2 862 877 4 010 202 5 811 267 6 603 261 6 637 571 5 501 455 4 576 495 3 181 252 3 175 568 Microscopy positive 638 669 208 858 422 224 879 316 1 541 189 2 354 400 2 916 902 2 927 780 1 657 793 1 144 762 493 480 259 161 RDT examined 174 693 200 111 190 593 201 708 168 004 281 847 898 913 4 548 458 4 164 969 4 252 681 3 698 659 2 401 385 RDT positive 30 653 64 435 61 246 83 302 81 987 151 394 463 666 3 012 753 2 574 090 2 468 060 1 549 912 904 509 Sao Tome and Principe2 Suspected cases 76 593 117 279 126 897 108 634 91 445 84 348 121 409 96 612 169 883 202 207 195 365 163 496 Presumed and confirmed 3 146 8 442 12 550 9 243 1 754 2 058 2 238 2 241 2 940 2 742 1 944 2 730 Microscopy examined 48 366 83 355 103 773 73 866 33 355 11 941 3 682 2 146 13 186 4 071 30 265 6 739 Microscopy positive 2 233 6 373 10 706 6 352 569 140 33 109 148 306 1 544 2 397 RDT examined 28 227 33 924 23 124 34 768 58 090 72 407 117 727 94 466 156 697 198 136 165 100 156 757 RDT positive 913 2 069 1 844 2 891 1 185 1 918 2 205 2 132 2 792 2 436 400 333 Imported cases 2 0 2 3 10 11 11 Senegal2 Suspected cases 721 687 633 380 686 047 867 157 727 918 1 421 221 1 559 054 2 035 693 2 096 124 2 010 398 2 206 842 2 632 540 Presumed and confirmed 390 015 328 276 404 762 475 144 296 367 502 084 356 272 398 377 536 745 359 246 452 984 547 773 Microscopy examined 27 793 18 325 19 946 24 205 19 343 26 556 38 748 21 639 12 881 11 356 13 641 15 963 Microscopy positive 16 226 12 770 14 144 17 522 11 262 17 846 9 918 10 463 3 997 2 496 3 881 5 222 RDT examined 634 210 560 898 555 724 734 495 677 832 1 384 834 1 513 574 2 011 383 2 077 442 1 994 504 2 185 530 2 605 654 RDT positive 314 105 261 349 280 241 349 165 254 362 474 407 339 622 385 243 526 947 352 212 441 432 531 628 Imported cases 0 0 0 292 45 0 0 Sierra Leone Suspected cases 2 327 928 1 150 747 2 579 296 2 576 550 2 647 375 2 337 297 2 996 959 2 935 447 2 895 596 4 169 146 1 860 018 3 174 623 Presumed and confirmed 934 028 861 491 1 945 859 1 715 851 1 898 852 1 569 606 1 845 727 1 741 512 1 781 855 2 445 392 1 223 397 2 043 818 Microscopy examined 718 473 46 280 194 787 185 403 66 277 75 025 120 917 10 910 20 155 140 768 149 100 137 649 Microscopy positive 218 473 30 670 104 533 76 077 39 414 37 820 60 458 5 717 8 719 35 055 71 001 71 448 RDT examined 1 609 455 886 994 1 975 972 2 377 254 2 056 722 2 176 042 2 805 621 2 834 261 2 827 417 3 990 491 1 212 527 2 947 058 RDT positive 715 555 613 348 1 432 789 1 625 881 1 335 062 1 445 556 1 714 848 1 645 519 1 725 112 2 372 450 654 005 1 882 454 South Africa2 Suspected cases 276 669 382 434 151 344 603 726 540 913 43 515 63 277 56 257 98 562 99 066** Presumed and confirmed 8 060 9 866 6 621 8 645 11 705 4 959 4 323 29 615 10 789 13 833 8 126ˆ 5 812ˆ Microscopy examined 178 387 121 291 364 021 300 291 13 917 20 653 4 654 1 190 Microscopy positive 3 787 5 986 1 632 2 572 4 101 785 1 219 9 592 2 666 477 622 1 190 RDT examined 276 669 204 047 30 053 239 705 240 622 29 598 42 624 56 257 11 982 99 066 RDT positive 4 273 3 880 4 989 6 073 7 604 4 174 3 104 20 023 8 123 13 356 8 126 6 727 Imported cases 3 568 3 075 6 234 5 742 8 890 3 663 2 854 South Sudan4 Suspected cases 2 492 473 3 814 332 566 043 5 391 360 6 405 779 5 258 306 4 038 950 Presumed and confirmed 900 283 1 473 653 1 125 039 1 855 501 2 433 991 3 789 475 555 957 4 054 795 4 697 506 4 064 662 1 805 371*+ 3 149 649 Microscopy examined 27 321 22 721 6 954 800 067 1 204 4 689 33 656 300 439 Microscopy positive 900 283 112 024 225 371 262 520 18 344 11 272 2 357 335 642 634 1 237 16 535 173 612 RDT examined 102 538 26 507 10 751 2 024 503 1 805 912 3 092 697 280 150 2 606 089 RDT positive 53 033 13 099 5 262 1 152 363 98 209 1 902 505 192 095 1 843 615 250 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – h. REPORTED MALARIA CASES BY METhOD OF CONFIRMATION, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 AFRICAN Mozambique Suspected cases 6 097 263 7 059 112 6 265 567 8 565 263 12 612 456 15 057 398 17 490 954 17 463 976 18 791 446 21 180 727 19 516 184 19 238 020 Presumed and confirmed 3 381 371 3 344 413 3 296 332 4 261 529 7 452 733 8 306 986 10 373 341 9 981 277 10 339 330 11 781 516 11 331 009 10 106 592 Microscopy examined 1 950 933 2 504 720 2 546 213 2 058 998 2 295 823 2 313 129 1 886 154 1 699 589 1 909 051 1 669 097 1 293 955 833 970 Microscopy positive 644 568 1 093 742 886 143 774 891 1 009 496 735 750 674 697 700 282 743 435 608 016 473 160 271 441 RDT examined 2 287 536 2 966 853 2 276 298 5 526 908 10 271 075 12 660 097 14 922 332 15 675 711 16 847 537 19 465 040 18 209 905 18 393 265 RDT positive 878 009 663 132 967 133 2 507 281 6 397 679 7 487 064 9 016 176 9 192 319 9 561 037 11 126 910 10 845 525 9 824 366 Namibia2 Suspected cases 39 855 61 145** 10 844 32 495** 185 078** 207 612** 308 414** 616 513** 394 822** 295 367** 258 145** 263 202** Presumed and confirmed 25 889 14 071ˆ 3 163 4 775ˆ 15 692ˆ 12 050ˆ 24 869ˆ 66 141ˆ 36 451ˆ 3 404ˆ 13 636ˆ 13 738ˆ Microscopy examined 14 522 13 262 7 875 1 507 1 894 1 471 1 778 1 778 1 215 511 809 245 Microscopy positive 556 335 194 136 222 118 329 364 289 301 168 100 RDT examined 48 599 32 495 185 078 207 612 308 414 616 513 394 822 295 367 258 145 263 202 RDT positive 1 525 4 775 15 692 12 050 24 869 66 141 36 451 3 404 13 636 13 738 Imported cases 2 888 3 980 11 874 4 021 1 064 1 305 759 Niger Suspected cases 11 231 308 5 315 185 7 818 305 5 584 223 7 100 212 4 671 411 7 347 200 4 013 178 4 810 919 5 582 958 6 343 569 6 472 227 Presumed and confirmed 4 231 896 4 401 099 6 398 943 4 333 905 5 247 235 3 937 742 5 166 336 2 761 268 3 358 058 3 771 451 4 377 938 4 659 909 Microscopy examined 165 514 130 658 1 781 505 1 799 299 2 872 710 295 229 3 198 194 203 583 213 795 303 115 337 657 264 694 Microscopy positive 49 285 68 529 1 119 929 1 176 711 1 953 279 206 660 2 120 515 125 856 121 657 211 783 223 601 198 998 RDT examined 7 476 672 1 622 013 1 967 117 1 824 610 2 944 035 2 830 548 3 240 780 3 809 595 4 285 516 5 279 843 5 915 007 5 624 653 RDT positive 593 489 770 056 1 209 331 1 196 880 2 010 489 2 185 448 2 137 595 2 635 412 2 924 793 3 559 668 4 154 337 3 845 709 Nigeria Suspected cases 5 221 656 11 789 970 21 659 831 20 558 467 20 243 915 29 113 322 25 106 551 25 381 459 29 489 245 27 370 935 30 202 722 Presumed and confirmed 3 873 463 4 306 945 6 938 519 12 830 911 17 257 495 16 702 261 23 956 669 20 219 268 20 482 380 23 376 793 21 580 055 23 608 797 Microscopy examined 672 185 1 953 399 1 633 960 1 681 469 839 849 901 141 1 055 444 1 428 731 3 298 156 3 086 039 3 405 012 Microscopy positive 523 513 1 233 654 556 871 618 363 749 118 1 023 273 2 476 514 2 312 163 2 559 742 RDT examined 45 924 242 526 2 898 052 7 194 960 10 191 825 10 770 388 17 853 794 16 919 717 18 018 372 22 621 211 21 030 081 24 514 099 RDT positive 27 674 7 338 668 7 511 712 12 979 919 12 338 760 13 524 751 17 330 401 16 013 077 18 765 444 Rwanda Suspected cases 2 883 666 1 802 382 3 095 386 3 064 585 4 178 206 6 093 114 7 502 174 11 186 029 9 666 424 8 829 176 6 879 911 5 576 953 Presumed and confirmed 669 322 273 293 483 470 962 618 1 623 176 2 505 794 3 380 568 5 940 533 4 231 883 3 612 822 2 043 392 1 163 670* Microscopy examined 2 708 973 1 602 271 2 904 793 2 862 877 4 010 202 5 811 267 6 603 261 6 637 571 5 501 455 4 576 495 3 181 252 3 175 568 Microscopy positive 638 669 208 858 422 224 879 316 1 541 189 2 354 400 2 916 902 2 927 780 1 657 793 1 144 762 493 480 259 161 RDT examined 174 693 200 111 190 593 201 708 168 004 281 847 898 913 4 548 458 4 164 969 4 252 681 3 698 659 2 401 385 RDT positive 30 653 64 435 61 246 83 302 81 987 151 394 463 666 3 012 753 2 574 090 2 468 060 1 549 912 904 509 Sao Tome and Principe2 Suspected cases 76 593 117 279 126 897 108 634 91 445 84 348 121 409 96 612 169 883 202 207 195 365 163 496 Presumed and confirmed 3 146 8 442 12 550 9 243 1 754 2 058 2 238 2 241 2 940 2 742 1 944 2 730 Microscopy examined 48 366 83 355 103 773 73 866 33 355 11 941 3 682 2 146 13 186 4 071 30 265 6 739 Microscopy positive 2 233 6 373 10 706 6 352 569 140 33 109 148 306 1 544 2 397 RDT examined 28 227 33 924 23 124 34 768 58 090 72 407 117 727 94 466 156 697 198 136 165 100 156 757 RDT positive 913 2 069 1 844 2 891 1 185 1 918 2 205 2 132 2 792 2 436 400 333 Imported cases 2 0 2 3 10 11 11 Senegal2 Suspected cases 721 687 633 380 686 047 867 157 727 918 1 421 221 1 559 054 2 035 693 2 096 124 2 010 398 2 206 842 2 632 540 Presumed and confirmed 390 015 328 276 404 762 475 144 296 367 502 084 356 272 398 377 536 745 359 246 452 984 547 773 Microscopy examined 27 793 18 325 19 946 24 205 19 343 26 556 38 748 21 639 12 881 11 356 13 641 15 963 Microscopy positive 16 226 12 770 14 144 17 522 11 262 17 846 9 918 10 463 3 997 2 496 3 881 5 222 RDT examined 634 210 560 898 555 724 734 495 677 832 1 384 834 1 513 574 2 011 383 2 077 442 1 994 504 2 185 530 2 605 654 RDT positive 314 105 261 349 280 241 349 165 254 362 474 407 339 622 385 243 526 947 352 212 441 432 531 628 Imported cases 0 0 0 292 45 0 0 Sierra Leone Suspected cases 2 327 928 1 150 747 2 579 296 2 576 550 2 647 375 2 337 297 2 996 959 2 935 447 2 895 596 4 169 146 1 860 018 3 174 623 Presumed and confirmed 934 028 861 491 1 945 859 1 715 851 1 898 852 1 569 606 1 845 727 1 741 512 1 781 855 2 445 392 1 223 397 2 043 818 Microscopy examined 718 473 46 280 194 787 185 403 66 277 75 025 120 917 10 910 20 155 140 768 149 100 137 649 Microscopy positive 218 473 30 670 104 533 76 077 39 414 37 820 60 458 5 717 8 719 35 055 71 001 71 448 RDT examined 1 609 455 886 994 1 975 972 2 377 254 2 056 722 2 176 042 2 805 621 2 834 261 2 827 417 3 990 491 1 212 527 2 947 058 RDT positive 715 555 613 348 1 432 789 1 625 881 1 335 062 1 445 556 1 714 848 1 645 519 1 725 112 2 372 450 654 005 1 882 454 South Africa2 Suspected cases 276 669 382 434 151 344 603 726 540 913 43 515 63 277 56 257 98 562 99 066** Presumed and confirmed 8 060 9 866 6 621 8 645 11 705 4 959 4 323 29 615 10 789 13 833 8 126ˆ 5 812ˆ Microscopy examined 178 387 121 291 364 021 300 291 13 917 20 653 4 654 1 190 Microscopy positive 3 787 5 986 1 632 2 572 4 101 785 1 219 9 592 2 666 477 622 1 190 RDT examined 276 669 204 047 30 053 239 705 240 622 29 598 42 624 56 257 11 982 99 066 RDT positive 4 273 3 880 4 989 6 073 7 604 4 174 3 104 20 023 8 123 13 356 8 126 6 727 Imported cases 3 568 3 075 6 234 5 742 8 890 3 663 2 854 South Sudan4 Suspected cases 2 492 473 3 814 332 566 043 5 391 360 6 405 779 5 258 306 4 038 950 Presumed and confirmed 900 283 1 473 653 1 125 039 1 855 501 2 433 991 3 789 475 555 957 4 054 795 4 697 506 4 064 662 1 805 371*+ 3 149 649 Microscopy examined 27 321 22 721 6 954 800 067 1 204 4 689 33 656 300 439 Microscopy positive 900 283 112 024 225 371 262 520 18 344 11 272 2 357 335 642 634 1 237 16 535 173 612 RDT examined 102 538 26 507 10 751 2 024 503 1 805 912 3 092 697 280 150 2 606 089 RDT positive 53 033 13 099 5 262 1 152 363 98 209 1 902 505 192 095 1 843 615 251 ANNEx 4 – h. REPORTED MALARIA CASES BY METhOD OF CONFIRMATION, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 AFRICAN Togo Suspected cases 2 035 303 906 276 1 590 266 1 550 804 2 255 010 2 356 048 2 577 029 2 747 984 3 009 800 3 531 375 3 091 060 Presumed and confirmed 1 441 199 519 452 909 129 965 832 1 524 339 1 610 711 1 746 334 1 756 582 2 002 877 2 406 091 996 364 Microscopy examined 478 354 502 977 579 507 560 538 621 119 643 815 501 516 482 664 446 404 492 629 623 623 Microscopy positive 206 071 224 619 260 535 272 984 310 207 317 578 231 919 209 626 229 267 269 526 245 956 RDT examined 1 134 779 390 611 1 010 759 990 266 1 633 891 1 712 233 2 075 513 2 265 320 2 563 396 3 038 746 2 332 983 RDT positive 812 958 282 145 648 594 692 848 1 214 132 1 293 133 1 514 415 1 546 956 1 773 610 2 136 565 750 408 Uganda Suspected cases 15 320 926 12 362 291 16 845 771 26 145 615 19 201 136 22 952 246 27 257 784 22 319 643 17 111 650 25 756 835 27 434 176 25 048 071 Presumed and confirmed 13 217 617 11 991 843 13 591 932 16 541 563 13 724 345 13 696 889 14 008 604 11 667 831 8 522 824 15 592 793 17 475 040£ 15 144 755£ Microscopy examined 3 705 284 385 928 3 466 571 3 718 588 2 048 185 3 684 722 4 492 090 5 515 931 1 606 330 4 691 859 4 284 114 4 029 037 Microscopy positive 1 628 595 134 726 1 413 149 1 502 362 578 289 1 248 576 1 542 091 1 694 441 458 909 1 622 576 1 647 933 1 513 116 RDT examined 64 607 300 761 2 449 526 7 387 826 7 060 545 12 983 382 18 492 939 16 803 712 12 741 670 19 454 545 22 004 158 20 210 666 RDT positive 37 987 181 515 1 249 109 3 053 650 6 164 171 8 193 758 9 973 390 5 300 265 12 359 786 12 548 724 11 131 747 United Republic of Tanzania2 Suspected cases 15 452 268 15 442 493 14 659 506 15 177 829 25 197 621 20 829 480 17 881 657 20 276 522 22 784 288 20 981 250 22 471 902 20 794 633 Presumed and confirmed 12 893 899 10 165 442 8 478 109 8 587 728 7 403 952 8 406 354 6 624 054 5 988 136 6 219 125 6 003 332 6 015 706 4 465 605 Microscopy examined 3 701 608 5 800 195 7 077 411 6 888 029 727 130 673 223 1 386 389 2 888 538 3 015 052 1 840 897 1 696 487 1 534 975 Microscopy positive 1 277 388 1 813 654 1 772 736 1 481 759 572 289 412 702 1 262 679 916 742 831 903 366 673 296 052 229 485 RDT examined 136 123$ 1 628 092 1 091 615 1 256 762 17 746 946 16 652 731 15 633 676 17 144 755 19 603 825 18 861 368 20 620 288 19 210 871 RDT positive 1 974$ 337 582 214 893 72 931 108 118 4 490 126 4 499 783 4 828 165 5 221 811 5 546 911 5 681 861 4 227 333 Imported cases 719$ 1 583$ 2 550$ 1 754$ 3 286$ 4 314$ 4 319$ Mainland Suspected cases 15 180 191 14 986 775 14 122 756 14 649 872 24 880 179 20 451 119 17 526 829 19 930 496 22 440 865 20 570 343 22 009 560 20 273 766 Presumed and confirmed 12 819 556 10 160 953 8 474 952 8 585 128 7 399 316 8 400 537 6 617 261 5 982 270 6 215 115 5 996 369 6 001 518 4 459 255 Microscopy examined 3 637 659 5 656 907 6 931 025 6 804 085 592 320 532 118 1 285 720 2 826 948 2 937 666 1 768 635 1 627 724 1 480 697 Microscopy positive 1 277 024 1 813 179 1 772 062 1 481 275 571 598 411 741 1 261 650 915 887 830 668 364 890 293 049 228 379 RDT examined 1 315 662 701 477 813 103 17 566 750 16 416 675 15 379 517 16 861 141 19 338 466 18 711 960 20 344 043 18 784 282 RDT positive 333 568 212 636 71 169 106 609 4 486 470 4 494 019 4 823 976 5 219 714 5 541 731 5 670 676 4 222 089 Zanzibar2 Suspected cases 272 077 455 718 536 750 527 957 317 442 378 361 354 828 346 026 343 423 427 029 462 342 520 867 Presumed and confirmed 74 343 4 489 3 157 2 600 4 636 5 817 6 793 5 866 4 010 6 963 14 188* 6 350 Microscopy examined 63 949 143 288 146 386 83 944 134 810 141 105 100 669 61 590 77 386 72 262 68 763 54 278 Microscopy positive 364 475 674 484 691 961 1 029 855 1 235 1 783 3 003 1 106 RDT examined 136 123 312 430 390 138 443 659 180 196 236 056 254 159 283 614 265 359 149 408 276 245 426 589 RDT positive 1 974 4 014 2 257 1 762 1 509 3 656 5 764 4 189 2 097 5 180 11 185 5 244 Imported cases 719 1 583 2 550 1 754 3 286 4 314 4 319 Zambia Suspected cases 7 859 740 8 116 962 9 627 862 10 952 323 10 055 407 11 340 409 15 491 235 13 957 528 Presumed and confirmed 4 229 839 4 607 908 4 695 400 5 465 122 5 972 933 5 094 123 5 976 192 6 054 679 5 195 723 5 360 020 8 698 304 7 159 243 Microscopy examined 180 697 275 323 398 195 446 750 Microscopy positive 49 855 78 474 128 291 117 585 RDT examined 5 964 354 7 207 500 8 502 989 10 403 283 9 718 666 10 852 416 14 513 049 13 120 677 RDT positive 4 077 547 4 184 661 4 851 319 5 505 639 4 989 824 5 068 876 7 992 924 6 651 557 Zimbabwe2 Suspected cases 912 618 480 011 734 997 1 128 954 1 504 682 1 693 630 1 499 675 1 828 301 1 293 392 1 324 299 1 389 065 922 001 Presumed and confirmed 648 965ˆ 321 901 277 736 423 702 572 944 484 794 384 029 767 069 264 018 308 173 447 381 133 137 Microscopy examined 10 004 7 823 13 949 28 745 55 192 102 566 2 771 Microscopy positive 249 379 1 966 773 1 069 2 420 2 415 2 986 3 522 RDT examined 513 032 470 007 727 174 1 115 005 1 453 689 1 638 438 1 330 069 1 533 030 1 290 621 1 297 197 1 356 433 904 833 RDT positive 249 379 319 935 276 963 422 633 548 276 482 379 314 003 468 276 264 018 308 173 447 381 133 137 Imported cases 180 358 768 672 AMERICAS Argentina1,2 Suspected cases 2 547 7 872 7 027 4 913 5 691 3 862 3 479 2 114 345 Presumed and confirmed 121 28 16 11 15 11 9 18ˆ 28 0 0 Microscopy examined 2 547 7 872 7 027 4 913 5 691 3 862 3 479 2 114 345 Microscopy positive 121 28 16 11 15 11 9 16 28 0 0 RDT examined RDT positive 2 Imported cases 55 28 16 11 15 11 9 18 23 Belize2,3 Suspected cases 27 366 22 996 20 789 25 351 24 122 26 367 20 936 26 995 17 642 19 731 10711** 10 893** Presumed and confirmed 150 79 37 26 19 13ˆ 5 9ˆ 7 2 0 0 Microscopy examined 27 366 22 996 20 789 25 351 24 122 26 367 20 936 26 995 17 642 19 731 10 711 10 893 Microscopy positive 150 79 37 26 19 13 5 9 7 2 0 0 RDT examined 114 7 278 RDT positive 5 3 0 0 Imported cases 7 4 4 0 4 1 2 4 2 0 0 Relapse cases 0 0 2 0 0 0 0 0 0 0 0 252 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – h. REPORTED MALARIA CASES BY METhOD OF CONFIRMATION, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 AFRICAN Togo Suspected cases 2 035 303 906 276 1 590 266 1 550 804 2 255 010 2 356 048 2 577 029 2 747 984 3 009 800 3 531 375 3 091 060 Presumed and confirmed 1 441 199 519 452 909 129 965 832 1 524 339 1 610 711 1 746 334 1 756 582 2 002 877 2 406 091 996 364 Microscopy examined 478 354 502 977 579 507 560 538 621 119 643 815 501 516 482 664 446 404 492 629 623 623 Microscopy positive 206 071 224 619 260 535 272 984 310 207 317 578 231 919 209 626 229 267 269 526 245 956 RDT examined 1 134 779 390 611 1 010 759 990 266 1 633 891 1 712 233 2 075 513 2 265 320 2 563 396 3 038 746 2 332 983 RDT positive 812 958 282 145 648 594 692 848 1 214 132 1 293 133 1 514 415 1 546 956 1 773 610 2 136 565 750 408 Uganda Suspected cases 15 320 926 12 362 291 16 845 771 26 145 615 19 201 136 22 952 246 27 257 784 22 319 643 17 111 650 25 756 835 27 434 176 25 048 071 Presumed and confirmed 13 217 617 11 991 843 13 591 932 16 541 563 13 724 345 13 696 889 14 008 604 11 667 831 8 522 824 15 592 793 17 475 040£ 15 144 755£ Microscopy examined 3 705 284 385 928 3 466 571 3 718 588 2 048 185 3 684 722 4 492 090 5 515 931 1 606 330 4 691 859 4 284 114 4 029 037 Microscopy positive 1 628 595 134 726 1 413 149 1 502 362 578 289 1 248 576 1 542 091 1 694 441 458 909 1 622 576 1 647 933 1 513 116 RDT examined 64 607 300 761 2 449 526 7 387 826 7 060 545 12 983 382 18 492 939 16 803 712 12 741 670 19 454 545 22 004 158 20 210 666 RDT positive 37 987 181 515 1 249 109 3 053 650 6 164 171 8 193 758 9 973 390 5 300 265 12 359 786 12 548 724 11 131 747 United Republic of Tanzania2 Suspected cases 15 452 268 15 442 493 14 659 506 15 177 829 25 197 621 20 829 480 17 881 657 20 276 522 22 784 288 20 981 250 22 471 902 20 794 633 Presumed and confirmed 12 893 899 10 165 442 8 478 109 8 587 728 7 403 952 8 406 354 6 624 054 5 988 136 6 219 125 6 003 332 6 015 706 4 465 605 Microscopy examined 3 701 608 5 800 195 7 077 411 6 888 029 727 130 673 223 1 386 389 2 888 538 3 015 052 1 840 897 1 696 487 1 534 975 Microscopy positive 1 277 388 1 813 654 1 772 736 1 481 759 572 289 412 702 1 262 679 916 742 831 903 366 673 296 052 229 485 RDT examined 136 123$ 1 628 092 1 091 615 1 256 762 17 746 946 16 652 731 15 633 676 17 144 755 19 603 825 18 861 368 20 620 288 19 210 871 RDT positive 1 974$ 337 582 214 893 72 931 108 118 4 490 126 4 499 783 4 828 165 5 221 811 5 546 911 5 681 861 4 227 333 Imported cases 719$ 1 583$ 2 550$ 1 754$ 3 286$ 4 314$ 4 319$ Mainland Suspected cases 15 180 191 14 986 775 14 122 756 14 649 872 24 880 179 20 451 119 17 526 829 19 930 496 22 440 865 20 570 343 22 009 560 20 273 766 Presumed and confirmed 12 819 556 10 160 953 8 474 952 8 585 128 7 399 316 8 400 537 6 617 261 5 982 270 6 215 115 5 996 369 6 001 518 4 459 255 Microscopy examined 3 637 659 5 656 907 6 931 025 6 804 085 592 320 532 118 1 285 720 2 826 948 2 937 666 1 768 635 1 627 724 1 480 697 Microscopy positive 1 277 024 1 813 179 1 772 062 1 481 275 571 598 411 741 1 261 650 915 887 830 668 364 890 293 049 228 379 RDT examined 1 315 662 701 477 813 103 17 566 750 16 416 675 15 379 517 16 861 141 19 338 466 18 711 960 20 344 043 18 784 282 RDT positive 333 568 212 636 71 169 106 609 4 486 470 4 494 019 4 823 976 5 219 714 5 541 731 5 670 676 4 222 089 Zanzibar2 Suspected cases 272 077 455 718 536 750 527 957 317 442 378 361 354 828 346 026 343 423 427 029 462 342 520 867 Presumed and confirmed 74 343 4 489 3 157 2 600 4 636 5 817 6 793 5 866 4 010 6 963 14 188* 6 350 Microscopy examined 63 949 143 288 146 386 83 944 134 810 141 105 100 669 61 590 77 386 72 262 68 763 54 278 Microscopy positive 364 475 674 484 691 961 1 029 855 1 235 1 783 3 003 1 106 RDT examined 136 123 312 430 390 138 443 659 180 196 236 056 254 159 283 614 265 359 149 408 276 245 426 589 RDT positive 1 974 4 014 2 257 1 762 1 509 3 656 5 764 4 189 2 097 5 180 11 185 5 244 Imported cases 719 1 583 2 550 1 754 3 286 4 314 4 319 Zambia Suspected cases 7 859 740 8 116 962 9 627 862 10 952 323 10 055 407 11 340 409 15 491 235 13 957 528 Presumed and confirmed 4 229 839 4 607 908 4 695 400 5 465 122 5 972 933 5 094 123 5 976 192 6 054 679 5 195 723 5 360 020 8 698 304 7 159 243 Microscopy examined 180 697 275 323 398 195 446 750 Microscopy positive 49 855 78 474 128 291 117 585 RDT examined 5 964 354 7 207 500 8 502 989 10 403 283 9 718 666 10 852 416 14 513 049 13 120 677 RDT positive 4 077 547 4 184 661 4 851 319 5 505 639 4 989 824 5 068 876 7 992 924 6 651 557 Zimbabwe2 Suspected cases 912 618 480 011 734 997 1 128 954 1 504 682 1 693 630 1 499 675 1 828 301 1 293 392 1 324 299 1 389 065 922 001 Presumed and confirmed 648 965ˆ 321 901 277 736 423 702 572 944 484 794 384 029 767 069 264 018 308 173 447 381 133 137 Microscopy examined 10 004 7 823 13 949 28 745 55 192 102 566 2 771 Microscopy positive 249 379 1 966 773 1 069 2 420 2 415 2 986 3 522 RDT examined 513 032 470 007 727 174 1 115 005 1 453 689 1 638 438 1 330 069 1 533 030 1 290 621 1 297 197 1 356 433 904 833 RDT positive 249 379 319 935 276 963 422 633 548 276 482 379 314 003 468 276 264 018 308 173 447 381 133 137 Imported cases 180 358 768 672 AMERICAS Argentina1,2 Suspected cases 2 547 7 872 7 027 4 913 5 691 3 862 3 479 2 114 345 Presumed and confirmed 121 28 16 11 15 11 9 18ˆ 28 0 0 Microscopy examined 2 547 7 872 7 027 4 913 5 691 3 862 3 479 2 114 345 Microscopy positive 121 28 16 11 15 11 9 16 28 0 0 RDT examined RDT positive 2 Imported cases 55 28 16 11 15 11 9 18 23 Belize2,3 Suspected cases 27 366 22 996 20 789 25 351 24 122 26 367 20 936 26 995 17 642 19 731 10711** 10 893** Presumed and confirmed 150 79 37 26 19 13ˆ 5 9ˆ 7 2 0 0 Microscopy examined 27 366 22 996 20 789 25 351 24 122 26 367 20 936 26 995 17 642 19 731 10 711 10 893 Microscopy positive 150 79 37 26 19 13 5 9 7 2 0 0 RDT examined 114 7 278 RDT positive 5 3 0 0 Imported cases 7 4 4 0 4 1 2 4 2 0 0 Relapse cases 0 0 2 0 0 0 0 0 0 0 0 253 ANNEx 4 – h. REPORTED MALARIA CASES BY METhOD OF CONFIRMATION, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 AMERICAS Bolivia (Plurinational State of)2 Suspected cases 140 857 150 662 132 904 144 049 124 900 159 167 155 407 151 697 139 938 137 473 136 795 139 279 Presumed and confirmed 13 769 7 143 7 415 7 342 7 401 6 907 5 553 4 587 5 354 9 357 12 187 9 959 Microscopy examined 133 463 143 272 121 944 133 260 124 900 159 167 155 407 151 697 139 938 110 028 Microscopy positive 12 252 6 108 6 293 6 272 7 401 6 907 5 553 4 334 5 261 8 118 8 507 7 404 RDT examined 7 394 7 390 10 960 10 789 27 445 RDT positive 1 517 1 035 1 122 1 070 253 93 1 239 3 680 2 555 Imported cases 33 11 15 12 19 7 0 Brazil2,5 Suspected cases 2 711 433 2 477 821 2 349 341 1 893 018 1 756 460 1 590 403 1 364 912 1 696 063 1 800 465 1 591 308 1 232 321 1 234 266 Presumed and confirmed 334 668 267 146 242 758 206 206+ 162 929+ 169 390+ 152 160+ 238 517+ 244 042+ 190 740+ 172 401 163 585+ Microscopy examined 2 711 432 2 476 335 2 325 775 1 873 518 1 744 640 1 573 538 1 341 639 1 656 685 1 754 244 1 539 938 1 163 048 1 138 847 Microscopy positive 334 667 266 713 237 978 174 048 142 744 139 844 124 210 184 876 181 967 146 868 127 403 119 735 RDT examined 1 1 486 23 566 19 500 11 820 16 865 23 273 39 378 46 221 51 370 69 273 95 419 RDT positive 1 433 4 780 3 719 1 384 3 318 5 034 9 549 12 606 10 586 17 785 20 651 Imported cases 8 923 4 856 4 932 5 068 4 867 6 816 4 165 1 811 1 365 Relapse cases 27 660 20 185 23 229 22 916 44 092 49 469 33 286 27 213 23 199 Colombia2 Suspected cases 521 342 418 032 416 767 327 055 403 532 328 434 296 091 254 380 208 538 295 406 212 399 239 060 Presumed and confirmed 117 650 64436+ 60 179 51 722+ 40 768 55 866+ 83 227+ 54 102+ 63 143+ 80 415+ 76 236+ 73 979+ Microscopy examined 521 342 396 861 346 599 284 332 325 713 316 451 242 973 244 732 195 286 283 471 202 736 222 155 Microscopy positive 117 637 60 121 50 938 44 293 36 166 48 059 57 515 38 349 42 810 47 806 40 155 34 114 RDT examined 21 171 70 168 42 723 77 819 11 983 53 118 9 648 13 252 11 935 9 663 16 905 RDT positive 13 4 188 9 241 7 403 4 602 3 535 5 655 5 056 3 407 3 703 5 284 5 396 Imported cases 7 785 618 1 297 1 948 2 306 457 758 Costa Rica2 Suspected cases 15 599 10 690 7 485 16 774 4 420 7 373** 5 160** 9 680** 9 000** 10 631 7 754** 12 383** Presumed and confirmed 137 17 8 6 6 8ˆ 13ˆ 25ˆ 152ˆ 149 141ˆ 232ˆ Microscopy examined 15 599 10 690 7 485 16 774 4 420 7 373 5 160 9 680 9 000 10 631 4 200 2 001 Microscopy positive 137 17 8 6 6 8 13 25 110 149 141 232 RDT examined 3 2 3 700 3 647 12 383 RDT positive 3 2 3 44 93 128 Imported cases 4 6 1 4 5 8 9 13 38 45 34 27 Relapse cases 0 0 0 2 1 0 0 0 2 4 4 12 Dominican Republic2 Suspected cases 469 052** 477 555 506 583 502 683 416 729 317 257** 251 245 226 988** 132 775** 143 366** 59 826** 48 139** Presumed and confirmed 2 482ˆ 1 616 952 579 496 661ˆ 755+ 398ˆ 484ˆ 1 314ˆ 829 291 Microscopy examined 469 052 421 405 415 808 431 683 362 304 317 257 228 795 226 988 132 775 143 366 59 826 48 139 Microscopy positive 2 482 1 616 952 579 496 661 487 398 322 1 314 829 17 RDT examined 26 585 56 150 90 775 71 000 54 425 7 659 22 450 87 397 42 425 55 000 7 570 26 839 RDT positive 932 129 80 74 221 1 313 274 Imported cases 106 37 30 65 57 51 23 3 7 Relapse cases 0 0 3 0 23 3 Ecuador2 Suspected cases 488 830 460 785 459 157 397 628 370 825 261 824 311 920 306 894 237 995** 177 742 163 990 184 862 Presumed and confirmed 1 888 1 233 560 378 242 686ˆ 1 424 1 380ˆ 1 806ˆ 1 909 2 089+ 2 436+ Microscopy examined 481 030 460 785 459 157 397 628 370 825 261 824 311 920 306 894 237 995 177 742 163 607 149 568 Microscopy positive 1 888 1 233 560 378 242 686 1 424 1 380 1 589 1 428 1 618 1 778 RDT examined 7 800 6 782 35 294 RDT positive 6 6 217 481 383 467 Imported cases 14 14 10 59 233 105 153 106 67 70 Relapse cases 0 2 0 65 183 El Salvador1,2 Suspected cases 115 256 100 883** 124 885 103 748 106 915 89 267 81 904 70 022 52 216** 89 992 18 868 Presumed and confirmed 26 15ˆ 20 7 8 9 14 4 2ˆ 3 0 Microscopy examined 115 256 100 883 124 885 103 748 106 915 89 267 81 904 70 022 52 216 89 992 18 868 Microscopy positive 26 15 20 7 8 9 14 4 2 3 0 RDT examined 1 1 RDT positive 1 1 Imported cases 9 8 7 1 2 4 1 3 2 3 0 French Guiana2 Suspected cases 14 373 14 429 13 638 22 327 14 651 11 558 9 430 6 238 Presumed and confirmed 1 632ˆ 1 209ˆ 900ˆ 875+ 448 434 258 597 546 212§ 154 143 Microscopy examined 14 373 14 429 13 638 22 327 14 651 11 558 9 430 6 238 Microscopy positive 1 085 720 523 321 242 297 173 468 546 178 120 122 RDT examined RDT positive 944 704 499 551 206 137 85 129 34 34 21 Imported cases 60 41 43 36 14 37 Relapse cases 0 0 0 32 Guatemala2 Suspected cases 237 075 195 080 186 645 153 731 250 964** 295 246** 333 535** 372 158** 438 833** 427 239** 319 660** 369 252 Presumed and confirmed 7 384+ 6 817 5 346 6 214 4 931 5 540ˆ 5 001+ 4 124ˆ 3 021ˆ 2 072ˆ 1 058ˆ 1 273 Microscopy examined 235 075 195 080 186 645 153 731 250 964 295 246 333 535 372 158 438 833 427 239 319 660 369 252 Microscopy positive 7 198 6 817 5 346 6 214 4 931 5 538 4 854 3 744 3 021 2 072 1 058 1 273 RDT examined 2 000 50 025 6 500 74 859 170 325 75 300 61 275 16 000 RDT positive 1 298 1 2 078 1 748 1 309 292 Imported cases 2 2 1 3 3 3 0 254 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – h. REPORTED MALARIA CASES BY METhOD OF CONFIRMATION, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 AMERICAS Bolivia (Plurinational State of)2 Suspected cases 140 857 150 662 132 904 144 049 124 900 159 167 155 407 151 697 139 938 137 473 136 795 139 279 Presumed and confirmed 13 769 7 143 7 415 7 342 7 401 6 907 5 553 4 587 5 354 9 357 12 187 9 959 Microscopy examined 133 463 143 272 121 944 133 260 124 900 159 167 155 407 151 697 139 938 110 028 Microscopy positive 12 252 6 108 6 293 6 272 7 401 6 907 5 553 4 334 5 261 8 118 8 507 7 404 RDT examined 7 394 7 390 10 960 10 789 27 445 RDT positive 1 517 1 035 1 122 1 070 253 93 1 239 3 680 2 555 Imported cases 33 11 15 12 19 7 0 Brazil2,5 Suspected cases 2 711 433 2 477 821 2 349 341 1 893 018 1 756 460 1 590 403 1 364 912 1 696 063 1 800 465 1 591 308 1 232 321 1 234 266 Presumed and confirmed 334 668 267 146 242 758 206 206+ 162 929+ 169 390+ 152 160+ 238 517+ 244 042+ 190 740+ 172 401 163 585+ Microscopy examined 2 711 432 2 476 335 2 325 775 1 873 518 1 744 640 1 573 538 1 341 639 1 656 685 1 754 244 1 539 938 1 163 048 1 138 847 Microscopy positive 334 667 266 713 237 978 174 048 142 744 139 844 124 210 184 876 181 967 146 868 127 403 119 735 RDT examined 1 1 486 23 566 19 500 11 820 16 865 23 273 39 378 46 221 51 370 69 273 95 419 RDT positive 1 433 4 780 3 719 1 384 3 318 5 034 9 549 12 606 10 586 17 785 20 651 Imported cases 8 923 4 856 4 932 5 068 4 867 6 816 4 165 1 811 1 365 Relapse cases 27 660 20 185 23 229 22 916 44 092 49 469 33 286 27 213 23 199 Colombia2 Suspected cases 521 342 418 032 416 767 327 055 403 532 328 434 296 091 254 380 208 538 295 406 212 399 239 060 Presumed and confirmed 117 650 64436+ 60 179 51 722+ 40 768 55 866+ 83 227+ 54 102+ 63 143+ 80 415+ 76 236+ 73 979+ Microscopy examined 521 342 396 861 346 599 284 332 325 713 316 451 242 973 244 732 195 286 283 471 202 736 222 155 Microscopy positive 117 637 60 121 50 938 44 293 36 166 48 059 57 515 38 349 42 810 47 806 40 155 34 114 RDT examined 21 171 70 168 42 723 77 819 11 983 53 118 9 648 13 252 11 935 9 663 16 905 RDT positive 13 4 188 9 241 7 403 4 602 3 535 5 655 5 056 3 407 3 703 5 284 5 396 Imported cases 7 785 618 1 297 1 948 2 306 457 758 Costa Rica2 Suspected cases 15 599 10 690 7 485 16 774 4 420 7 373** 5 160** 9 680** 9 000** 10 631 7 754** 12 383** Presumed and confirmed 137 17 8 6 6 8ˆ 13ˆ 25ˆ 152ˆ 149 141ˆ 232ˆ Microscopy examined 15 599 10 690 7 485 16 774 4 420 7 373 5 160 9 680 9 000 10 631 4 200 2 001 Microscopy positive 137 17 8 6 6 8 13 25 110 149 141 232 RDT examined 3 2 3 700 3 647 12 383 RDT positive 3 2 3 44 93 128 Imported cases 4 6 1 4 5 8 9 13 38 45 34 27 Relapse cases 0 0 0 2 1 0 0 0 2 4 4 12 Dominican Republic2 Suspected cases 469 052** 477 555 506 583 502 683 416 729 317 257** 251 245 226 988** 132 775** 143 366** 59 826** 48 139** Presumed and confirmed 2 482ˆ 1 616 952 579 496 661ˆ 755+ 398ˆ 484ˆ 1 314ˆ 829 291 Microscopy examined 469 052 421 405 415 808 431 683 362 304 317 257 228 795 226 988 132 775 143 366 59 826 48 139 Microscopy positive 2 482 1 616 952 579 496 661 487 398 322 1 314 829 17 RDT examined 26 585 56 150 90 775 71 000 54 425 7 659 22 450 87 397 42 425 55 000 7 570 26 839 RDT positive 932 129 80 74 221 1 313 274 Imported cases 106 37 30 65 57 51 23 3 7 Relapse cases 0 0 3 0 23 3 Ecuador2 Suspected cases 488 830 460 785 459 157 397 628 370 825 261 824 311 920 306 894 237 995** 177 742 163 990 184 862 Presumed and confirmed 1 888 1 233 560 378 242 686ˆ 1 424 1 380ˆ 1 806ˆ 1 909 2 089+ 2 436+ Microscopy examined 481 030 460 785 459 157 397 628 370 825 261 824 311 920 306 894 237 995 177 742 163 607 149 568 Microscopy positive 1 888 1 233 560 378 242 686 1 424 1 380 1 589 1 428 1 618 1 778 RDT examined 7 800 6 782 35 294 RDT positive 6 6 217 481 383 467 Imported cases 14 14 10 59 233 105 153 106 67 70 Relapse cases 0 2 0 65 183 El Salvador1,2 Suspected cases 115 256 100 883** 124 885 103 748 106 915 89 267 81 904 70 022 52 216** 89 992 18 868 Presumed and confirmed 26 15ˆ 20 7 8 9 14 4 2ˆ 3 0 Microscopy examined 115 256 100 883 124 885 103 748 106 915 89 267 81 904 70 022 52 216 89 992 18 868 Microscopy positive 26 15 20 7 8 9 14 4 2 3 0 RDT examined 1 1 RDT positive 1 1 Imported cases 9 8 7 1 2 4 1 3 2 3 0 French Guiana2 Suspected cases 14 373 14 429 13 638 22 327 14 651 11 558 9 430 6 238 Presumed and confirmed 1 632ˆ 1 209ˆ 900ˆ 875+ 448 434 258 597 546 212§ 154 143 Microscopy examined 14 373 14 429 13 638 22 327 14 651 11 558 9 430 6 238 Microscopy positive 1 085 720 523 321 242 297 173 468 546 178 120 122 RDT examined RDT positive 944 704 499 551 206 137 85 129 34 34 21 Imported cases 60 41 43 36 14 37 Relapse cases 0 0 0 32 Guatemala2 Suspected cases 237 075 195 080 186 645 153 731 250 964** 295 246** 333 535** 372 158** 438 833** 427 239** 319 660** 369 252 Presumed and confirmed 7 384+ 6 817 5 346 6 214 4 931 5 540ˆ 5 001+ 4 124ˆ 3 021ˆ 2 072ˆ 1 058ˆ 1 273 Microscopy examined 235 075 195 080 186 645 153 731 250 964 295 246 333 535 372 158 438 833 427 239 319 660 369 252 Microscopy positive 7 198 6 817 5 346 6 214 4 931 5 538 4 854 3 744 3 021 2 072 1 058 1 273 RDT examined 2 000 50 025 6 500 74 859 170 325 75 300 61 275 16 000 RDT positive 1 298 1 2 078 1 748 1 309 292 Imported cases 2 2 1 3 3 3 0 255 ANNEx 4 – h. REPORTED MALARIA CASES BY METhOD OF CONFIRMATION, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 AMERICAS Guyana2 Suspected cases 212 863 201 693** 196 622 205 903 142 843 132 941 117 483 100 308 101 346 103 836 72821** 89 809 Presumed and confirmed 22 935 29 471ˆ 31 601ˆ 31 479 12 354 9 984 11 108ˆ 13 936ˆ 17 038ˆ 18 826 17 230ˆ 20 850* Microscopy examined 212 863 201 693 196 622 205 903 142 843 132 941 110 891 100 105 95 986 85 736 49 496 63 165 Microscopy positive 22 935 29 471 31 601 31 479 12 354 9 984 10 906 13 734 15 607 13 840 8 932 11 785 RDT examined 35 6 592 203 5 360 18 100 27 116 26 622 RDT positive 35 55 1 724 242 3 570 4 986 10 025 9 065 Imported cases 411 Haiti Suspected cases 270 427 184 934 167 772 171 409 261 403 330 603 459 959 330 738** 287 522** 266 675 245 202 198 881 Presumed and confirmed 84 153 34 350 27 866 26 543 17 696 17 583ˆ 21 430ˆ 19 135ˆ 8 828ˆ 10 687 22 996+ 9 513ˆ Microscopy examined 270 427 184 934 167 726 165 823 134 766 69 659 61 428 62 539 59 803 35 144 7 855 18 130 Microscopy positive 84 153 34 350 27 866 26 543 10 893 5 224 4 342 2 119 1 586 765 1 446 674 RDT examined 46 5 586 126 637 260 944 398 531 301 812 253 001 231 531 226 374 180 751 RDT positive 6 803 12 702 23 325 18 309 8 232 9 922 21 541 9 333 Honduras2 Suspected cases 152 961** 152 451** 155 165** 144 436** 151 420** 152 730** 173 651** 148 160** 142 780** 142 870** 10 350 165 853** Presumed and confirmed 9 745 7 618ˆ 6 439ˆ 5 364ˆ 3 380ˆ 3 555ˆ 4 097ˆ 1 283ˆ 653ˆ 391ˆ 913ˆ 1 657ˆ Microscopy examined 152 961 152 451 155 165 144 436 151 420 150 854 167 836 148 160 142 780 142 870 151 244 Microscopy positive 9 745 7 618 6 439 5 364 3 380 3 555 4 097 1 283 653 391 913 1 657 RDT examined 1 500 4 000 4 000 237 1 427 4 928 20 745 25 870 31 556 18 754 10 350 14 609 RDT positive 49 10 64 102 79 657 263 454 193 539 968 Imported cases 2 0 3 10 21 61 98 107 Relapse cases 3 17 19 Mexico2 Suspected cases 1 192 081 1 035 424 1 025 659 1 017 508 900 578 867 853** 798 568** 644 174** 548 247 531 471** 242 200 298 496 Presumed and confirmed 1 233 1 130 842 499 666 551ˆ 596ˆ 765ˆ 803ˆ 641ˆ 369 275 Microscopy examined 1 192 081 1 035 424 1 025 659 1 017 508 900 578 867 853 798 568 644 174 548 247 531 471 242 200 298 496 Microscopy positive 1 233 1 130 842 499 666 551 596 765 836 641 369 275 RDT examined 7 6 6 161 RDT positive 7 6 6 3 Imported cases 7 6 9 4 10 34 45 29 23 22 10 31 Relapse cases 0 0 0 0 0 0 0 5 10 1 2 2 Nicaragua2 Suspected cases 554 414 536 105 552 722 539 022 605 357 604 418 554 415 663 132 831 077** 1 029 288 947 451 1 158 806 Presumed and confirmed 692 925 1 235 1 196 1 163 2 308 6 284 10 952 15 934ˆ 13 226 31 763 23 323 Microscopy examined 535 914 521 904 536 278 519 993 605 357 604 418 553 615 660 452 831 077 1 001 225 884 821 1 158 806 Microscopy positive 692 925 1 235 1 196 1 163 2 308 6 284 10 952 15 934 12 337 18 799 16 684 RDT examined 18 500 14 201 16 444 19 029 800 2 680 44 905 28 063 56 397 RDT positive 2 885 889 6 731 6 639 Imported cases 34 21 29 12 3 17 26 25 64 Panama2 Suspected cases 141 038 116 588 107 711 93 624 80 701 64 511 50 772 38 270** 23 383** 22 171 14 809 18 779** Presumed and confirmed 418 354 844 705 874 562ˆ 811ˆ 689ˆ 715ˆ 1 597++ 2 203++ 4 364ˆ Microscopy examined 141 038 116 588 107 711 93 624 80 701 64 511 50 772 38 270 23 383 18 217 7 027 6 105 Microscopy positive 418 354 844 705 874 562 811 689 715 1 209 1 358 649 RDT examined 829 1 141 3 954 7 782 17 239 RDT positive 3 5 689 424 388 2 109 4 253 Imported cases 9 10 16 42 40 31 15 10 7 Relapse cases 0 0 0 0 0 0 130 135 192 Paraguay1,2,3 Suspected cases 62 178 48 611 31 499 24 806 24 832 6 687** 3 192** 8 014** 11 415 11 221 11 073** Presumed and confirmed 29 10 15 11 8 8ˆ 10ˆ 5ˆ 0 2 1 4ˆ Microscopy examined 62 178 48 611 31 499 24 806 24 832 6 687 3 192 8 014 11 336 11 196 11 073 Microscopy positive 29 10 15 11 8 8 10 5 0 1 1 4 RDT examined 10 1 1 267 79 25 153 RDT positive 1 1 2 1 0 3 Imported cases 9 9 15 11 8 8 10 5 0 2 1 4 Peru2 Suspected cases 744 627** 702 894** 758 723** 863 790** 864 413** 865 980** 566 230 388 699** 304 785** 243 240 412 933 Presumed and confirmed 31 545ˆ 25 005ˆ 31 436 48 719 65 252 66 609ˆ 56 671 55 367ˆ 45 619ˆ 24 483 15 847 18 140 Microscopy examined 744 627 702 894 758 723 863 790 864 413 865 980 566 230 388 699 304 785 243 240 412 933 Microscopy positive 31 545 25 005 31 436 48 719 65 252 66 609 56 671 55 367 45 619 24 483 15 847 18 140 RDT examined 23 58 562 858 1 634 18 133 13 924 160 000 RDT positive 1 34 463 2 325 1 000 Imported cases 48 57 176 159 25 65 Suriname2 Suspected cases 17 902 16 160 22 134 19 736 33 097 15 236 23 444 22 302 19 836 20 743 14 057** 16 597** Presumed and confirmed 1 771+ 795+ 569+ 729 401 376 332+ 551ˆ 235 215 244 77ˆ Microscopy examined 16 533 15 135 17 464 13 693 17 608 15 083 14 946 12 536 11 799 13 702 13 798 16 592 Microscopy positive 1 574 751 306 530 98 345 315 412 218 209 238 77 RDT examined 1 369 1 025 4 670 6 043 15 489 153 8 498 9 766 8 037 7 041 14 043 16 513 RDT positive 190 20 248 199 303 31 11 160 17 6 6 76 Imported cases 204 295 251 414 198 111 88 53 Relapse cases 0 3 0 7 0 3 0 256 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – h. REPORTED MALARIA CASES BY METhOD OF CONFIRMATION, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 AMERICAS Guyana2 Suspected cases 212 863 201 693** 196 622 205 903 142 843 132 941 117 483 100 308 101 346 103 836 72821** 89 809 Presumed and confirmed 22 935 29 471ˆ 31 601ˆ 31 479 12 354 9 984 11 108ˆ 13 936ˆ 17 038ˆ 18 826 17 230ˆ 20 850* Microscopy examined 212 863 201 693 196 622 205 903 142 843 132 941 110 891 100 105 95 986 85 736 49 496 63 165 Microscopy positive 22 935 29 471 31 601 31 479 12 354 9 984 10 906 13 734 15 607 13 840 8 932 11 785 RDT examined 35 6 592 203 5 360 18 100 27 116 26 622 RDT positive 35 55 1 724 242 3 570 4 986 10 025 9 065 Imported cases 411 Haiti Suspected cases 270 427 184 934 167 772 171 409 261 403 330 603 459 959 330 738** 287 522** 266 675 245 202 198 881 Presumed and confirmed 84 153 34 350 27 866 26 543 17 696 17 583ˆ 21 430ˆ 19 135ˆ 8 828ˆ 10 687 22 996+ 9 513ˆ Microscopy examined 270 427 184 934 167 726 165 823 134 766 69 659 61 428 62 539 59 803 35 144 7 855 18 130 Microscopy positive 84 153 34 350 27 866 26 543 10 893 5 224 4 342 2 119 1 586 765 1 446 674 RDT examined 46 5 586 126 637 260 944 398 531 301 812 253 001 231 531 226 374 180 751 RDT positive 6 803 12 702 23 325 18 309 8 232 9 922 21 541 9 333 Honduras2 Suspected cases 152 961** 152 451** 155 165** 144 436** 151 420** 152 730** 173 651** 148 160** 142 780** 142 870** 10 350 165 853** Presumed and confirmed 9 745 7 618ˆ 6 439ˆ 5 364ˆ 3 380ˆ 3 555ˆ 4 097ˆ 1 283ˆ 653ˆ 391ˆ 913ˆ 1 657ˆ Microscopy examined 152 961 152 451 155 165 144 436 151 420 150 854 167 836 148 160 142 780 142 870 151 244 Microscopy positive 9 745 7 618 6 439 5 364 3 380 3 555 4 097 1 283 653 391 913 1 657 RDT examined 1 500 4 000 4 000 237 1 427 4 928 20 745 25 870 31 556 18 754 10 350 14 609 RDT positive 49 10 64 102 79 657 263 454 193 539 968 Imported cases 2 0 3 10 21 61 98 107 Relapse cases 3 17 19 Mexico2 Suspected cases 1 192 081 1 035 424 1 025 659 1 017 508 900 578 867 853** 798 568** 644 174** 548 247 531 471** 242 200 298 496 Presumed and confirmed 1 233 1 130 842 499 666 551ˆ 596ˆ 765ˆ 803ˆ 641ˆ 369 275 Microscopy examined 1 192 081 1 035 424 1 025 659 1 017 508 900 578 867 853 798 568 644 174 548 247 531 471 242 200 298 496 Microscopy positive 1 233 1 130 842 499 666 551 596 765 836 641 369 275 RDT examined 7 6 6 161 RDT positive 7 6 6 3 Imported cases 7 6 9 4 10 34 45 29 23 22 10 31 Relapse cases 0 0 0 0 0 0 0 5 10 1 2 2 Nicaragua2 Suspected cases 554 414 536 105 552 722 539 022 605 357 604 418 554 415 663 132 831 077** 1 029 288 947 451 1 158 806 Presumed and confirmed 692 925 1 235 1 196 1 163 2 308 6 284 10 952 15 934ˆ 13 226 31 763 23 323 Microscopy examined 535 914 521 904 536 278 519 993 605 357 604 418 553 615 660 452 831 077 1 001 225 884 821 1 158 806 Microscopy positive 692 925 1 235 1 196 1 163 2 308 6 284 10 952 15 934 12 337 18 799 16 684 RDT examined 18 500 14 201 16 444 19 029 800 2 680 44 905 28 063 56 397 RDT positive 2 885 889 6 731 6 639 Imported cases 34 21 29 12 3 17 26 25 64 Panama2 Suspected cases 141 038 116 588 107 711 93 624 80 701 64 511 50 772 38 270** 23 383** 22 171 14 809 18 779** Presumed and confirmed 418 354 844 705 874 562ˆ 811ˆ 689ˆ 715ˆ 1 597++ 2 203++ 4 364ˆ Microscopy examined 141 038 116 588 107 711 93 624 80 701 64 511 50 772 38 270 23 383 18 217 7 027 6 105 Microscopy positive 418 354 844 705 874 562 811 689 715 1 209 1 358 649 RDT examined 829 1 141 3 954 7 782 17 239 RDT positive 3 5 689 424 388 2 109 4 253 Imported cases 9 10 16 42 40 31 15 10 7 Relapse cases 0 0 0 0 0 0 130 135 192 Paraguay1,2,3 Suspected cases 62 178 48 611 31 499 24 806 24 832 6 687** 3 192** 8 014** 11 415 11 221 11 073** Presumed and confirmed 29 10 15 11 8 8ˆ 10ˆ 5ˆ 0 2 1 4ˆ Microscopy examined 62 178 48 611 31 499 24 806 24 832 6 687 3 192 8 014 11 336 11 196 11 073 Microscopy positive 29 10 15 11 8 8 10 5 0 1 1 4 RDT examined 10 1 1 267 79 25 153 RDT positive 1 1 2 1 0 3 Imported cases 9 9 15 11 8 8 10 5 0 2 1 4 Peru2 Suspected cases 744 627** 702 894** 758 723** 863 790** 864 413** 865 980** 566 230 388 699** 304 785** 243 240 412 933 Presumed and confirmed 31 545ˆ 25 005ˆ 31 436 48 719 65 252 66 609ˆ 56 671 55 367ˆ 45 619ˆ 24 483 15 847 18 140 Microscopy examined 744 627 702 894 758 723 863 790 864 413 865 980 566 230 388 699 304 785 243 240 412 933 Microscopy positive 31 545 25 005 31 436 48 719 65 252 66 609 56 671 55 367 45 619 24 483 15 847 18 140 RDT examined 23 58 562 858 1 634 18 133 13 924 160 000 RDT positive 1 34 463 2 325 1 000 Imported cases 48 57 176 159 25 65 Suriname2 Suspected cases 17 902 16 160 22 134 19 736 33 097 15 236 23 444 22 302 19 836 20 743 14 057** 16 597** Presumed and confirmed 1 771+ 795+ 569+ 729 401 376 332+ 551ˆ 235 215 244 77ˆ Microscopy examined 16 533 15 135 17 464 13 693 17 608 15 083 14 946 12 536 11 799 13 702 13 798 16 592 Microscopy positive 1 574 751 306 530 98 345 315 412 218 209 238 77 RDT examined 1 369 1 025 4 670 6 043 15 489 153 8 498 9 766 8 037 7 041 14 043 16 513 RDT positive 190 20 248 199 303 31 11 160 17 6 6 76 Imported cases 204 295 251 414 198 111 88 53 Relapse cases 0 3 0 7 0 3 0 257 ANNEx 4 – h. REPORTED MALARIA CASES BY METhOD OF CONFIRMATION, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 AMERICAS Venezuela (Bolivarian Republic of)2 Suspected cases 400 495 382 303 410 663 476 764 522 617 625 174 852 556 1 144 635 699 130 1 104 736 655 707 600 242** Presumed and confirmed 45 155 45 824 52 803 80 320 91 918 137 996 301 466 525 897 522 059+ 492 753+ 232 757ˆ 194 057 Microscopy examined 400 495 382 303 410 663 476 764 522 617 625 174 852 556 1 144 635 699 130 1 040 683 655 707 342 616 Microscopy positive 45 155 45 824 52 803 80 320 91 918 137 996 301 466 525 897 404 924 398 285 197 466 113 863 RDT examined 257 626 RDT positive 48 117 64 053 69 884 80 194 Imported cases 1 677 1 210 1 594 1 948 2 941 2 125 1 848 1 356 829 Relapse cases 58 390 111 360 106 886 87 029 31 308 12 806 EASTERN MEDITERRANEAN Afghanistan Suspected cases 865 181 936 252 847 933 817 606 881 515 939 964 1 055 368 1 143 511 1 240 523 1 008 487 943 267 917 108 Presumed and confirmed 392 864 482 748 391 365 326 593 317 608 383 008 436 017 413 536 299 863 174 894 105 445 86 370 Microscopy examined 524 523 531 053 511 408 507 145 514 466 538 789 598 556 611 904 665 200 561 160 449 875 483 806 Microscopy positive 69 397 77 549 54 840 46 114 83 920 103 377 151 528 194 866 104 960 71 389 38 923 35 149 RDT examined 17 592 36 833 155 919 138 026 262 028 431 157 524 149 446 293 389 994 433 195 RDT positive 401 6 851 22 558 16 482 89 705 118 220 143 729 102 471 66 372 51 114 Djibouti Suspected cases 354 1 412 7 189 39 284 10 586 19 492 75 594 104 800 214 101 268 147** 218 591 Presumed and confirmed 1 010 230 27 1 684 9 439 9 557 13 822 14 810 25 319 49 402* 73 535ˆ 58 916 Microscopy examined 124 1 410 7 189 39 284 10 502 19 492 24 504 42 250 50 157 Microscopy positive 1 010 22 1 684 9 439 1 764 2 280 1 283 11 633 13 210 RDT examined 51 090 104 800 214 101 268 147 168 434 RDT positive 3 7 709 11 542 13 527 25 319 49 402 73 535 45 706 Iran (Islamic Republic of)2,3 Suspected cases 614 817 530 470 479 655 385 172 468 513 610 337 418 125 383 397 477 914** 455 855** 388 232 275 419** Presumed and confirmed 3 031 3 239 1 629 1 374 1 243 799 705 939 625ˆ 1 190ˆ 1 051ˆ 999 Microscopy examined 614 817 530 470 479 655 385 172 468 513 610 337 418 125 383 397 477 914 454 322 334 861 275 419 Microscopy positive 3 031 3 239 1 629 1 374 1 243 799 705 939 625 1 190 1 046 994 RDT examined 64 061 101 803 53 371 25 025 RDT positive 436 1 089 516 5 Imported cases 1 184 1 529 842 853 867 632 611 868 602 1 107 878 821 Relapse cases 19 14 11 2 3 0 3 0 2 0 Pakistan Suspected cases 8 601 835 8 418 570 8 902 947 7 682 166 8 375 256 8 943 120 8 216 519 8 200 987 7 226 725 8 157 351 7 172 956 7 789 443 Presumed and confirmed 4 281 356 4 065 802 4 285 449 3 465 620 3 655 975 3 778 090 2 126 497 2 209 708 1 069 052 413 533 372 416 400 316 Microscopy examined 4 281 346 4 168 648 4 497 330 3 786 216 4 179 468 4 620 326 5 091 840 4 539 957 4 324 570 4 855 044 3 607 265 4 231 801 Microscopy positive 220 870 287 592 250 526 183 091 183 180 138 130 157 554 132 580 119 099 125 804 83 859 86 319 RDT examined 279 724 518 709 410 949 704 978 804 680 748 563 1 327 187 1 821 139 2 207 613 3 302 307 3 564 489 3 556 423 RDT positive 19 721 46 997 40 255 91 557 81 687 65 729 171 451 237 237 255 411 287 729 287 969 312 778 Saudi Arabia2,3 Suspected cases 944 723 1 062 827 1 186 179 1 309 783 1 249 752 1 306 700 1 267 933 1 073 998 1 015 953 1 118 706** 703 048** 1 164 803 Presumed and confirmed 1 941 2 788 3 406 2 513 2 305 2 620 5 382 3 151 2 711 2 152ˆ 3 658ˆ 2 616 Microscopy examined 944 723 1 062 827 1 186 179 1 309 783 1 249 752 1 306 700 1 267 933 1 073 998 1 015 953 1 118 706 403 972 429 910 Microscopy positive 1 941 2 788 3 406 2 513 2 305 2 620 5 382 3 151 2 711 2 152 3 658 1 091 RDT examined 1 118 706 399 076 212 908 RDT positive 2 152 3 205 1 525 Imported cases 1 912 2 719 3 324 2 479 2 254 2 537 5 110 2 974 2 517 2 029 3 453 2 470 Somalia Suspected cases 223 981 99 403 53 658 88 734 92 103 119 008 205 753 228 912 253 220 332 935** 337 965 376 167 Presumed and confirmed 24 833 41 167 23 202 9 135ˆ 26 419 39 169 58 021 37 156 31 030 65 375ˆ 27 333 50 648 Microscopy examined 20 593 26 351 19 542 12 450 59 494 32 175 Microscopy positive 5 629 1 627 841 245 11 615 4 463 RDT examined 203 388 35 236 37 273 67 464 64 480 100 792 183 360 226 894 253 211 332 935 337 965 298 929 RDT positive 19 204 1 724 6 817 7 407 11 001 20 953 35 628 35 138 31 021 39 687 27 333 8 504 Sudan Suspected cases 2 398 239 2 929 578 2 438 467 2 197 563 4 101 841 4 199 740 3 691 112 9 760 505 7 642 050 8 211 933 8 145 586 Presumed and confirmed 1 465 496 1 214 004 964 698 989 946 1 207 771 1 102 186 897 194 1 642 058 3 627 586 3 568 941 3 412 499 3 960 655 Microscopy examined 3 586 482 3 236 118 2 426 329 6 668 355 4 797 856 5 568 277 4 742 722 Microscopy positive 625 365 506 806 526 931 592 383 579 038 586 827 378 308 588 100 1 262 210 1 408 242 1 262 841 1 309 893 RDT examined 1 653 300 2 222 380 2 000 700 1 800 000 788 281 632 443 422 841 1 113 247 1 027 264 929 551 1 056 085 RDT positive 95 192 489 468 187 707 212 016 386 473 343 769 435 553 337 852 Yemen Suspected cases 835 018 804 401 888 952 927 821 821 618 711 680 1 217 602 1 659 798 779 312 1 283 681 1 280 190 1 573 986 Presumed and confirmed 198 963 142 152 165 687 149 451 122 812 104 831 145 627 143 333 233 143 216 763 202 671 244 857 Microscopy examined 645 463 645 093 685 406 723 691 643 994 561 644 960 860 1 070 020 419 415 841 358 791 049 772 511 Microscopy positive 78 269 60 751 71 300 63 484 51 768 42 052 45 886 28 936 64 233 104 350 97 008 83 436 RDT examined 97 289 108 110 150 218 157 457 141 519 121 464 210 815 589 778 284 654 391 459 450 541 736 957 RDT positive 28 428 30 203 41 059 39 294 34 939 34 207 53 814 114 397 93 667 61 549 67 058 96 903 258 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – h. REPORTED MALARIA CASES BY METhOD OF CONFIRMATION, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 AMERICAS Venezuela (Bolivarian Republic of)2 Suspected cases 400 495 382 303 410 663 476 764 522 617 625 174 852 556 1 144 635 699 130 1 104 736 655 707 600 242** Presumed and confirmed 45 155 45 824 52 803 80 320 91 918 137 996 301 466 525 897 522 059+ 492 753+ 232 757ˆ 194 057 Microscopy examined 400 495 382 303 410 663 476 764 522 617 625 174 852 556 1 144 635 699 130 1 040 683 655 707 342 616 Microscopy positive 45 155 45 824 52 803 80 320 91 918 137 996 301 466 525 897 404 924 398 285 197 466 113 863 RDT examined 257 626 RDT positive 48 117 64 053 69 884 80 194 Imported cases 1 677 1 210 1 594 1 948 2 941 2 125 1 848 1 356 829 Relapse cases 58 390 111 360 106 886 87 029 31 308 12 806 EASTERN MEDITERRANEAN Afghanistan Suspected cases 865 181 936 252 847 933 817 606 881 515 939 964 1 055 368 1 143 511 1 240 523 1 008 487 943 267 917 108 Presumed and confirmed 392 864 482 748 391 365 326 593 317 608 383 008 436 017 413 536 299 863 174 894 105 445 86 370 Microscopy examined 524 523 531 053 511 408 507 145 514 466 538 789 598 556 611 904 665 200 561 160 449 875 483 806 Microscopy positive 69 397 77 549 54 840 46 114 83 920 103 377 151 528 194 866 104 960 71 389 38 923 35 149 RDT examined 17 592 36 833 155 919 138 026 262 028 431 157 524 149 446 293 389 994 433 195 RDT positive 401 6 851 22 558 16 482 89 705 118 220 143 729 102 471 66 372 51 114 Djibouti Suspected cases 354 1 412 7 189 39 284 10 586 19 492 75 594 104 800 214 101 268 147** 218 591 Presumed and confirmed 1 010 230 27 1 684 9 439 9 557 13 822 14 810 25 319 49 402* 73 535ˆ 58 916 Microscopy examined 124 1 410 7 189 39 284 10 502 19 492 24 504 42 250 50 157 Microscopy positive 1 010 22 1 684 9 439 1 764 2 280 1 283 11 633 13 210 RDT examined 51 090 104 800 214 101 268 147 168 434 RDT positive 3 7 709 11 542 13 527 25 319 49 402 73 535 45 706 Iran (Islamic Republic of)2,3 Suspected cases 614 817 530 470 479 655 385 172 468 513 610 337 418 125 383 397 477 914** 455 855** 388 232 275 419** Presumed and confirmed 3 031 3 239 1 629 1 374 1 243 799 705 939 625ˆ 1 190ˆ 1 051ˆ 999 Microscopy examined 614 817 530 470 479 655 385 172 468 513 610 337 418 125 383 397 477 914 454 322 334 861 275 419 Microscopy positive 3 031 3 239 1 629 1 374 1 243 799 705 939 625 1 190 1 046 994 RDT examined 64 061 101 803 53 371 25 025 RDT positive 436 1 089 516 5 Imported cases 1 184 1 529 842 853 867 632 611 868 602 1 107 878 821 Relapse cases 19 14 11 2 3 0 3 0 2 0 Pakistan Suspected cases 8 601 835 8 418 570 8 902 947 7 682 166 8 375 256 8 943 120 8 216 519 8 200 987 7 226 725 8 157 351 7 172 956 7 789 443 Presumed and confirmed 4 281 356 4 065 802 4 285 449 3 465 620 3 655 975 3 778 090 2 126 497 2 209 708 1 069 052 413 533 372 416 400 316 Microscopy examined 4 281 346 4 168 648 4 497 330 3 786 216 4 179 468 4 620 326 5 091 840 4 539 957 4 324 570 4 855 044 3 607 265 4 231 801 Microscopy positive 220 870 287 592 250 526 183 091 183 180 138 130 157 554 132 580 119 099 125 804 83 859 86 319 RDT examined 279 724 518 709 410 949 704 978 804 680 748 563 1 327 187 1 821 139 2 207 613 3 302 307 3 564 489 3 556 423 RDT positive 19 721 46 997 40 255 91 557 81 687 65 729 171 451 237 237 255 411 287 729 287 969 312 778 Saudi Arabia2,3 Suspected cases 944 723 1 062 827 1 186 179 1 309 783 1 249 752 1 306 700 1 267 933 1 073 998 1 015 953 1 118 706** 703 048** 1 164 803 Presumed and confirmed 1 941 2 788 3 406 2 513 2 305 2 620 5 382 3 151 2 711 2 152ˆ 3 658ˆ 2 616 Microscopy examined 944 723 1 062 827 1 186 179 1 309 783 1 249 752 1 306 700 1 267 933 1 073 998 1 015 953 1 118 706 403 972 429 910 Microscopy positive 1 941 2 788 3 406 2 513 2 305 2 620 5 382 3 151 2 711 2 152 3 658 1 091 RDT examined 1 118 706 399 076 212 908 RDT positive 2 152 3 205 1 525 Imported cases 1 912 2 719 3 324 2 479 2 254 2 537 5 110 2 974 2 517 2 029 3 453 2 470 Somalia Suspected cases 223 981 99 403 53 658 88 734 92 103 119 008 205 753 228 912 253 220 332 935** 337 965 376 167 Presumed and confirmed 24 833 41 167 23 202 9 135ˆ 26 419 39 169 58 021 37 156 31 030 65 375ˆ 27 333 50 648 Microscopy examined 20 593 26 351 19 542 12 450 59 494 32 175 Microscopy positive 5 629 1 627 841 245 11 615 4 463 RDT examined 203 388 35 236 37 273 67 464 64 480 100 792 183 360 226 894 253 211 332 935 337 965 298 929 RDT positive 19 204 1 724 6 817 7 407 11 001 20 953 35 628 35 138 31 021 39 687 27 333 8 504 Sudan Suspected cases 2 398 239 2 929 578 2 438 467 2 197 563 4 101 841 4 199 740 3 691 112 9 760 505 7 642 050 8 211 933 8 145 586 Presumed and confirmed 1 465 496 1 214 004 964 698 989 946 1 207 771 1 102 186 897 194 1 642 058 3 627 586 3 568 941 3 412 499 3 960 655 Microscopy examined 3 586 482 3 236 118 2 426 329 6 668 355 4 797 856 5 568 277 4 742 722 Microscopy positive 625 365 506 806 526 931 592 383 579 038 586 827 378 308 588 100 1 262 210 1 408 242 1 262 841 1 309 893 RDT examined 1 653 300 2 222 380 2 000 700 1 800 000 788 281 632 443 422 841 1 113 247 1 027 264 929 551 1 056 085 RDT positive 95 192 489 468 187 707 212 016 386 473 343 769 435 553 337 852 Yemen Suspected cases 835 018 804 401 888 952 927 821 821 618 711 680 1 217 602 1 659 798 779 312 1 283 681 1 280 190 1 573 986 Presumed and confirmed 198 963 142 152 165 687 149 451 122 812 104 831 145 627 143 333 233 143 216 763 202 671 244 857 Microscopy examined 645 463 645 093 685 406 723 691 643 994 561 644 960 860 1 070 020 419 415 841 358 791 049 772 511 Microscopy positive 78 269 60 751 71 300 63 484 51 768 42 052 45 886 28 936 64 233 104 350 97 008 83 436 RDT examined 97 289 108 110 150 218 157 457 141 519 121 464 210 815 589 778 284 654 391 459 450 541 736 957 RDT positive 28 428 30 203 41 059 39 294 34 939 34 207 53 814 114 397 93 667 61 549 67 058 96 903 259 ANNEx 4 – h. REPORTED MALARIA CASES BY METhOD OF CONFIRMATION, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 EUROPEAN Armenia1,2 Suspected cases 31 026 1 213 465 350 320 121 Presumed and confirmed 1 4 0 1 2 2 2 6 3 Microscopy examined 31 026 1 213 465 350 320 121 Microscopy positive 1 4 1 2 2 2 6 3 RDT examined RDT positive Imported cases 1 4 0 1 2 2 2 6 3 Azerbaijan2 Suspected cases 456 652 449 168 497 040 432 810 399 925 405 416 465 860 373 562 358 009 Presumed and confirmed 52 8 4 4 2 1 1 1 2 0 Microscopy examined 456 652 449 168 497 040 432 810 399 925 405 416 465 860 373 562 358 009 Microscopy positive 52 8 4 4 2 1 1 1 2 0 RDT examined RDT positive Imported cases 2 4 1 4 2 1 1 1 2 Georgia2 Suspected cases 2 368 2 032 1 046 192 440 294 318 416 286 335 237 Presumed and confirmed 0 6 5 7 5 6 7 9 9 8 4 Microscopy examined 2 368 2 032 1 046 192 440 294 318 416 286 335 237 Microscopy positive 0 6 5 7 5 6 7 9 9 8 4 RDT examined RDT positive Imported cases 0 5 4 7 5 5 7 8 9 8 4 Relapse cases 1 Kyrgyzstan2 Suspected cases 30 190 27 850 18 268 54 249 35 600 75 688 62 537 8 459 7 709 46 384 18 717 Presumed and confirmed 6 5 3 4 0 1 6 2 0 1 0 Microscopy examined 30 190 27 850 18 268 54 249 35 600 75 688 62 537 8 459 7 709 46 384 18 717 Microscopy positive 6 5 3 4 0 1 6 2 0 1 0 RDT examined 0 0 0 RDT positive 0 0 0 Imported cases 3 5 3 4 0 1 6 2 0 1 0 Tajikistan2 Suspected cases 173 523 173 367 209 239 213 916 200 241 188 341** 198 766** 191 284** 209 830 159 124 Presumed and confirmed 116 100 54 25 7 4ˆ 1ˆ 3ˆ 3 0 Microscopy examined 173 523 173 367 209 239 213 916 200 241 188 341 198 766 191 284 207 821 159 124 Microscopy positive 116 100 54 25 7 4 1 3 3 0 RDT examined 42 056 34 570 41 218 2 009 RDT positive 4 1 3 Imported cases 4 22 11 10 5 4 1 3 3 Türkiye2 Suspected cases 507 841 421 295 337 830 255 125 189 854 211 740 144 499 115 557 95 877 59 429 54 586 Presumed and confirmed 90 132 595 285 254 224 209 214 238 279 135 Microscopy examined 507 841 421 295 337 830 255 125 189 854 211 740 144 499 115 557 95 877 59 429 54 586 Microscopy positive 90 132 595 285 254 224 209 214 238 279 135 RDT examined RDT positive Imported cases 81 128 376 251 249 221 208 214 237 277 133 Relapse cases 9 4 0 34 0 0 0 0 Turkmenistan1,2 Suspected cases 81 784 83 675 85 536 84 264 85 722 78 903 71 887 Presumed and confirmed 0 0 0 0 0 0 0 0 0 3 0 Microscopy examined 81 784 83 675 85 536 84 264 85 722 78 903 71 887 Microscopy positive 0 3 0 RDT examined RDT positive Imported cases 0 0 0 0 0 0 0 0 0 3 0 Uzbekistan1,2 Suspected cases 921 364 886 243 805 761 908 301 812 347 800 912 797 472 655 112 650 616 669 373 552 458 Presumed and confirmed 6 1 1 3 1 0 0 0 0 1 2 Microscopy examined 921 364 886 243 805 761 908 301 812 347 800 912 797 472 655 112 650 616 669 373 552 458 Microscopy positive 6 1 1 3 1 0 0 0 0 1 2 RDT examined RDT positive Imported cases 2 1 1 3 1 0 0 0 0 1 2 260 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – h. REPORTED MALARIA CASES BY METhOD OF CONFIRMATION, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 EUROPEAN Armenia1,2 Suspected cases 31 026 1 213 465 350 320 121 Presumed and confirmed 1 4 0 1 2 2 2 6 3 Microscopy examined 31 026 1 213 465 350 320 121 Microscopy positive 1 4 1 2 2 2 6 3 RDT examined RDT positive Imported cases 1 4 0 1 2 2 2 6 3 Azerbaijan2 Suspected cases 456 652 449 168 497 040 432 810 399 925 405 416 465 860 373 562 358 009 Presumed and confirmed 52 8 4 4 2 1 1 1 2 0 Microscopy examined 456 652 449 168 497 040 432 810 399 925 405 416 465 860 373 562 358 009 Microscopy positive 52 8 4 4 2 1 1 1 2 0 RDT examined RDT positive Imported cases 2 4 1 4 2 1 1 1 2 Georgia2 Suspected cases 2 368 2 032 1 046 192 440 294 318 416 286 335 237 Presumed and confirmed 0 6 5 7 5 6 7 9 9 8 4 Microscopy examined 2 368 2 032 1 046 192 440 294 318 416 286 335 237 Microscopy positive 0 6 5 7 5 6 7 9 9 8 4 RDT examined RDT positive Imported cases 0 5 4 7 5 5 7 8 9 8 4 Relapse cases 1 Kyrgyzstan2 Suspected cases 30 190 27 850 18 268 54 249 35 600 75 688 62 537 8 459 7 709 46 384 18 717 Presumed and confirmed 6 5 3 4 0 1 6 2 0 1 0 Microscopy examined 30 190 27 850 18 268 54 249 35 600 75 688 62 537 8 459 7 709 46 384 18 717 Microscopy positive 6 5 3 4 0 1 6 2 0 1 0 RDT examined 0 0 0 RDT positive 0 0 0 Imported cases 3 5 3 4 0 1 6 2 0 1 0 Tajikistan2 Suspected cases 173 523 173 367 209 239 213 916 200 241 188 341** 198 766** 191 284** 209 830 159 124 Presumed and confirmed 116 100 54 25 7 4ˆ 1ˆ 3ˆ 3 0 Microscopy examined 173 523 173 367 209 239 213 916 200 241 188 341 198 766 191 284 207 821 159 124 Microscopy positive 116 100 54 25 7 4 1 3 3 0 RDT examined 42 056 34 570 41 218 2 009 RDT positive 4 1 3 Imported cases 4 22 11 10 5 4 1 3 3 Türkiye2 Suspected cases 507 841 421 295 337 830 255 125 189 854 211 740 144 499 115 557 95 877 59 429 54 586 Presumed and confirmed 90 132 595 285 254 224 209 214 238 279 135 Microscopy examined 507 841 421 295 337 830 255 125 189 854 211 740 144 499 115 557 95 877 59 429 54 586 Microscopy positive 90 132 595 285 254 224 209 214 238 279 135 RDT examined RDT positive Imported cases 81 128 376 251 249 221 208 214 237 277 133 Relapse cases 9 4 0 34 0 0 0 0 Turkmenistan1,2 Suspected cases 81 784 83 675 85 536 84 264 85 722 78 903 71 887 Presumed and confirmed 0 0 0 0 0 0 0 0 0 3 0 Microscopy examined 81 784 83 675 85 536 84 264 85 722 78 903 71 887 Microscopy positive 0 3 0 RDT examined RDT positive Imported cases 0 0 0 0 0 0 0 0 0 3 0 Uzbekistan1,2 Suspected cases 921 364 886 243 805 761 908 301 812 347 800 912 797 472 655 112 650 616 669 373 552 458 Presumed and confirmed 6 1 1 3 1 0 0 0 0 1 2 Microscopy examined 921 364 886 243 805 761 908 301 812 347 800 912 797 472 655 112 650 616 669 373 552 458 Microscopy positive 6 1 1 3 1 0 0 0 0 1 2 RDT examined RDT positive Imported cases 2 1 1 3 1 0 0 0 0 1 2 261 ANNEx 4 – h. REPORTED MALARIA CASES BY METhOD OF CONFIRMATION, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 SOUTH‑EAST ASIA Bangladesh2 Suspected cases 467 767 390 930 372 806 418 755 630 181 786 830 993 589 986 442 1 300 691 1 507 230 1 416 473 1 470 849 Presumed and confirmed 62 378 52 601 29 518 26 891 57 480 39 719 27 737 29 247 10 523 17 225 6 130 7 294 Microscopy examined 308 326 270 253 253 887 290 496 418 519 527 659 573 540 613 304 800 251 750 657 611 307 370 589 Microscopy positive 20 519 20 232 9 901 7 303 13 628 6 621 3 217 3 325 1 135 1 311 262 458 RDT examined 152 936 119 849 118 919 128 259 211 662 259 171 420 049 373 138 500 440 756 573 805 166 1 100 260 RDT positive 35 354 31 541 19 617 19 588 43 852 33 098 24 520 25 922 9 388 15 914 5 868 6 836 Imported cases 129 109 19 41 6 2 6 Bhutan2 Suspected cases 54 760 44 494 42 512 31 632 33 586 74 087 118 841 120 667 133 498 119 975 31 522 25 510 Presumed and confirmed 487 207 82 45 48 104 74 51ˆ 54 42ˆ 54 23 Microscopy examined 54 709 44 481 42 512 31 632 33 586 26 149 23 442 22 885 19 778 18 973 6 246 8 742 Microscopy positive 436 194 82 45 48 84 59 51 49 38 46 23 RDT examined 47 938 95 399 97 782 113 720 101 002 25 276 16 768 RDT positive 20 15 11 5 37 8 0 Imported cases 0 23 34 70 56 38 34 30 9 13 Relapse cases 0 0 0 0 0 0 0 0 1 Democratic People's Republic of Korea2 Suspected cases 27 019 27 857 40 925 72 719 38 878 91 007 205 807 189 357 685 704 461 998 357 778 45 711 Presumed and confirmed 15 392 18 104 23 537 15 673 11 212 7 409 5 113 4 626 3 698 1 869 1 819 2 357 Microscopy examined 25 147 26 513 39 238 71 453 38 201 29 272 22 747 16 835 28 654 3 255 3 681 45 711 Microscopy positive 13 520 16 760 21 850 14 407 10 535 7 010 4 890 4 463 3 446 886 1 162 2 357 RDT examined 61 348 182 980 172 499 657 050 458 743 354 097 0 RDT positive 12 143 140 252 983 657 0 India2 Suspected cases 119 279 429 119 470 044 122 170 278 127 891 198 138 628 331 140 841 230 144 539 608 125 977 799 124 613 482 134 230 349 97 177 024 114 400 959 Presumed and confirmed 1 599 986 1 310 656 1 067 824 881 730 1 102 205 1 169 261 1 087 285 844 558 429 928 338 494 186 532 161 753 Microscopy examined 108 679 429 108 969 660 109 044 798 113 109 094 124 066 331 121 141 970 124 933 348 110 769 742 111 123 775 113 969 785 73 294 318 87 951 904 Microscopy positive 1 599 986 1 310 656 1 067 824 881 730 1 102 205 1 169 261 1 087 285 306 768 230 432 132 750 65 468 67 604 RDT examined 10 600 000 10 500 384 13 125 480 14 782 104 14 562 000 19 699 260 19 606 260 15 208 057 13 489 707 20 260 564 23 882 706 26 440 073 RDT positive 537 790 199 496 205 744 121 064 94 149 Indonesia2,6 Suspected cases 1 591 179 1 212 799 1 900 725 1 708 161 1 550 296 1 567 450 1 457 858 1 441 679 1 700 094 2 491 516 1 940 676 2 128 771 Presumed and confirmed 465 764 422 447 417 819 343 527 252 027 217 025 218 450 261 617 222 345 250 646 254 055 304 607 Microscopy examined 1 335 445 962 090 1 429 139 1 447 980 1 300 835 1 224 504 1 092 093 1 045 994 1 322 026 1 899 437 1 367 987 1 399 683 Microscopy positive 465 764 422 447 417 819 343 527 252 027 217 025 218 450 261 617 190 782 212 997 192 769 216 744 RDT examined 255 734 250 709 471 586 260 181 249 461 342 946 365 765 395 685 378 068 592 079 516 167 640 609 RDT positive 31 563 37 649 61 286 87 863 Imported cases 11 61 38 27 Myanmar2 Suspected cases 1 277 568 1 210 465 1 424 004 1 300 556 1 567 095 2 657 555 3 185 245 3 368 697 3 183 758 3 708 802 3 665 239 1 958 385** Presumed and confirmed 693 124 567 452 481 242 333 871 205 658 182 465ˆ 110 146 85 019 76 518 56 410ˆ 58 836ˆ 79 001ˆ Microscopy examined 275 374 312 689 265 135 138 473 151 258 98 014 122 078 107 242 58 126 50 902 57 950 43 929 Microscopy positive 103 285 91 752 75 220 26 509 12 010 6 453 6 717 4 648 2 577 1 050 2 167 2 669 RDT examined 729 878 795 618 1 158 831 1 162 083 1 415 837 2 559 541 3 063 167 3 261 455 3 125 632 3 666 973 3 627 732 1 919 075 RDT positive 317 523 373 542 405 984 307 362 193 648 176 163 103 429 80 371 73 941 55 590 57 009 76 377 Imported cases 9 1 Relapse cases 140 12 Nepal2 Suspected cases 213 353 188 702 276 752 168 687 200 631 131 654 146 705 165 640** 256 020 224 726 158 776 159 912** Presumed and confirmed 96 383 71 752 71 410 37 336 26 556 19 171ˆ 10 687 3 610ˆ 3 031 1 438 674 650ˆ Microscopy examined 102 977 95 011 152 780 100 336 127 130 63 946 84 595 163 323 160 904 92 367 31 304 24 221 Microscopy positive 3 115 1 910 1 659 1 197 1 499 1 112 1 009 1 293 1 158 102 29 18 RDT examined 17 887 25 353 55 792 32 989 48 444 49 649 52 432 97 870 93 378 131 631 127 228 159 635 RDT positive 779 1 504 1 571 777 725 449 135 608 401 373 Imported cases 1 079 1 026 667 521 502 670 539 579 357 359 Sri Lanka1,2,3 Suspected cases 1 001 107 985 060 957 155 1 249 846 1 078 884 1 157 366 1 090 743 1 104 333 1 149 897 1 164 914** 820 210 680 386 Presumed and confirmed 736 175 93 95 49 36 41 57 52 54ˆ 30 26 Microscopy examined 1 001 107 985 060 948 250 1 236 580 1 069 817 1 142 466 1 072 396 1 089 290 1 129 070 1 164 914 810 205 673 744 Microscopy positive 736 175 74 93 45 35 40 38 52 54 30 26 RDT examined 8 905 13 266 9 067 14 900 18 347 15 043 20 827 20 745 10 005 6 642 RDT positive 19 2 4 1 1 19 0 2 0 0 Imported cases 52 51 70 95 49 36 41 57 48 53 30 25 Relapse cases 0 0 0 0 0 3 0 0 0 Thailand2,6 Suspected cases 1 777 977 1 450 885 1 272 324 1 927 585 1 833 061 1 537 430 1 619 174 1 268 976 976 482 937 053 800 012 651 385 Presumed and confirmed 32 480 24 897 46 895 41 602 41 218 24 155 17 800 11 440 6 750 5 421 3 940 3 279 Microscopy examined 1 695 980 1 354 215 1 130 757 1 830 090 1 756 528 1 358 953 1 302 834 1 117 648 908 540 856 893 716 060 546 749 Microscopy positive 22 969 14 478 32 569 33 302 37 921 14 750 11 301 7 154 5 171 4 170 2 792 2 227 RDT examined 81 997 96 670 141 567 97 495 76 533 178 477 316 340 151 328 67 942 80 160 83 952 104 636 RDT positive 9 511 10 419 14 326 8 300 3 297 9 405 6 499 4 286 1 579 1 251 1 148 1 052 Imported cases 9 890 5 724 4 020 1 618 1 342 798 800 Relapse cases 0 0 0 1 262 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – h. REPORTED MALARIA CASES BY METhOD OF CONFIRMATION, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 SOUTH‑EAST ASIA Bangladesh2 Suspected cases 467 767 390 930 372 806 418 755 630 181 786 830 993 589 986 442 1 300 691 1 507 230 1 416 473 1 470 849 Presumed and confirmed 62 378 52 601 29 518 26 891 57 480 39 719 27 737 29 247 10 523 17 225 6 130 7 294 Microscopy examined 308 326 270 253 253 887 290 496 418 519 527 659 573 540 613 304 800 251 750 657 611 307 370 589 Microscopy positive 20 519 20 232 9 901 7 303 13 628 6 621 3 217 3 325 1 135 1 311 262 458 RDT examined 152 936 119 849 118 919 128 259 211 662 259 171 420 049 373 138 500 440 756 573 805 166 1 100 260 RDT positive 35 354 31 541 19 617 19 588 43 852 33 098 24 520 25 922 9 388 15 914 5 868 6 836 Imported cases 129 109 19 41 6 2 6 Bhutan2 Suspected cases 54 760 44 494 42 512 31 632 33 586 74 087 118 841 120 667 133 498 119 975 31 522 25 510 Presumed and confirmed 487 207 82 45 48 104 74 51ˆ 54 42ˆ 54 23 Microscopy examined 54 709 44 481 42 512 31 632 33 586 26 149 23 442 22 885 19 778 18 973 6 246 8 742 Microscopy positive 436 194 82 45 48 84 59 51 49 38 46 23 RDT examined 47 938 95 399 97 782 113 720 101 002 25 276 16 768 RDT positive 20 15 11 5 37 8 0 Imported cases 0 23 34 70 56 38 34 30 9 13 Relapse cases 0 0 0 0 0 0 0 0 1 Democratic People's Republic of Korea2 Suspected cases 27 019 27 857 40 925 72 719 38 878 91 007 205 807 189 357 685 704 461 998 357 778 45 711 Presumed and confirmed 15 392 18 104 23 537 15 673 11 212 7 409 5 113 4 626 3 698 1 869 1 819 2 357 Microscopy examined 25 147 26 513 39 238 71 453 38 201 29 272 22 747 16 835 28 654 3 255 3 681 45 711 Microscopy positive 13 520 16 760 21 850 14 407 10 535 7 010 4 890 4 463 3 446 886 1 162 2 357 RDT examined 61 348 182 980 172 499 657 050 458 743 354 097 0 RDT positive 12 143 140 252 983 657 0 India2 Suspected cases 119 279 429 119 470 044 122 170 278 127 891 198 138 628 331 140 841 230 144 539 608 125 977 799 124 613 482 134 230 349 97 177 024 114 400 959 Presumed and confirmed 1 599 986 1 310 656 1 067 824 881 730 1 102 205 1 169 261 1 087 285 844 558 429 928 338 494 186 532 161 753 Microscopy examined 108 679 429 108 969 660 109 044 798 113 109 094 124 066 331 121 141 970 124 933 348 110 769 742 111 123 775 113 969 785 73 294 318 87 951 904 Microscopy positive 1 599 986 1 310 656 1 067 824 881 730 1 102 205 1 169 261 1 087 285 306 768 230 432 132 750 65 468 67 604 RDT examined 10 600 000 10 500 384 13 125 480 14 782 104 14 562 000 19 699 260 19 606 260 15 208 057 13 489 707 20 260 564 23 882 706 26 440 073 RDT positive 537 790 199 496 205 744 121 064 94 149 Indonesia2,6 Suspected cases 1 591 179 1 212 799 1 900 725 1 708 161 1 550 296 1 567 450 1 457 858 1 441 679 1 700 094 2 491 516 1 940 676 2 128 771 Presumed and confirmed 465 764 422 447 417 819 343 527 252 027 217 025 218 450 261 617 222 345 250 646 254 055 304 607 Microscopy examined 1 335 445 962 090 1 429 139 1 447 980 1 300 835 1 224 504 1 092 093 1 045 994 1 322 026 1 899 437 1 367 987 1 399 683 Microscopy positive 465 764 422 447 417 819 343 527 252 027 217 025 218 450 261 617 190 782 212 997 192 769 216 744 RDT examined 255 734 250 709 471 586 260 181 249 461 342 946 365 765 395 685 378 068 592 079 516 167 640 609 RDT positive 31 563 37 649 61 286 87 863 Imported cases 11 61 38 27 Myanmar2 Suspected cases 1 277 568 1 210 465 1 424 004 1 300 556 1 567 095 2 657 555 3 185 245 3 368 697 3 183 758 3 708 802 3 665 239 1 958 385** Presumed and confirmed 693 124 567 452 481 242 333 871 205 658 182 465ˆ 110 146 85 019 76 518 56 410ˆ 58 836ˆ 79 001ˆ Microscopy examined 275 374 312 689 265 135 138 473 151 258 98 014 122 078 107 242 58 126 50 902 57 950 43 929 Microscopy positive 103 285 91 752 75 220 26 509 12 010 6 453 6 717 4 648 2 577 1 050 2 167 2 669 RDT examined 729 878 795 618 1 158 831 1 162 083 1 415 837 2 559 541 3 063 167 3 261 455 3 125 632 3 666 973 3 627 732 1 919 075 RDT positive 317 523 373 542 405 984 307 362 193 648 176 163 103 429 80 371 73 941 55 590 57 009 76 377 Imported cases 9 1 Relapse cases 140 12 Nepal2 Suspected cases 213 353 188 702 276 752 168 687 200 631 131 654 146 705 165 640** 256 020 224 726 158 776 159 912** Presumed and confirmed 96 383 71 752 71 410 37 336 26 556 19 171ˆ 10 687 3 610ˆ 3 031 1 438 674 650ˆ Microscopy examined 102 977 95 011 152 780 100 336 127 130 63 946 84 595 163 323 160 904 92 367 31 304 24 221 Microscopy positive 3 115 1 910 1 659 1 197 1 499 1 112 1 009 1 293 1 158 102 29 18 RDT examined 17 887 25 353 55 792 32 989 48 444 49 649 52 432 97 870 93 378 131 631 127 228 159 635 RDT positive 779 1 504 1 571 777 725 449 135 608 401 373 Imported cases 1 079 1 026 667 521 502 670 539 579 357 359 Sri Lanka1,2,3 Suspected cases 1 001 107 985 060 957 155 1 249 846 1 078 884 1 157 366 1 090 743 1 104 333 1 149 897 1 164 914** 820 210 680 386 Presumed and confirmed 736 175 93 95 49 36 41 57 52 54ˆ 30 26 Microscopy examined 1 001 107 985 060 948 250 1 236 580 1 069 817 1 142 466 1 072 396 1 089 290 1 129 070 1 164 914 810 205 673 744 Microscopy positive 736 175 74 93 45 35 40 38 52 54 30 26 RDT examined 8 905 13 266 9 067 14 900 18 347 15 043 20 827 20 745 10 005 6 642 RDT positive 19 2 4 1 1 19 0 2 0 0 Imported cases 52 51 70 95 49 36 41 57 48 53 30 25 Relapse cases 0 0 0 0 0 3 0 0 0 Thailand2,6 Suspected cases 1 777 977 1 450 885 1 272 324 1 927 585 1 833 061 1 537 430 1 619 174 1 268 976 976 482 937 053 800 012 651 385 Presumed and confirmed 32 480 24 897 46 895 41 602 41 218 24 155 17 800 11 440 6 750 5 421 3 940 3 279 Microscopy examined 1 695 980 1 354 215 1 130 757 1 830 090 1 756 528 1 358 953 1 302 834 1 117 648 908 540 856 893 716 060 546 749 Microscopy positive 22 969 14 478 32 569 33 302 37 921 14 750 11 301 7 154 5 171 4 170 2 792 2 227 RDT examined 81 997 96 670 141 567 97 495 76 533 178 477 316 340 151 328 67 942 80 160 83 952 104 636 RDT positive 9 511 10 419 14 326 8 300 3 297 9 405 6 499 4 286 1 579 1 251 1 148 1 052 Imported cases 9 890 5 724 4 020 1 618 1 342 798 800 Relapse cases 0 0 0 1 263 ANNEx 4 – h. REPORTED MALARIA CASES BY METhOD OF CONFIRMATION, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 SOUTH‑EAST ASIA Timor-Leste2,3 Suspected cases 143 690 118 120 122 008 86 597 90 818 114 383 115 008 144 061 217 129 103 614** 122 955** Presumed and confirmed 119 074 36 153 6 148 1 042 347 80 95 30 8 9ˆ 14ˆ 0 Microscopy examined 35 256 27 731 Microscopy positive 14 0 RDT examined 48 042 127 272 117 180 121 991 86 592 90 818 114 382 115 008 144 061 217 129 103 614 122 955 RDT positive 48 139 19 735 5 208 1 025 342 80 94 30 8 9 3 0 Imported cases 10 13 7 9 7 0 Relapse cases 1 1 0 0 0 0 WESTERN PACIFIC Cambodia5 Suspected cases 411 104 356 606 300 344 207 483 356 027 332 613 298 108 376 702 282 295 596 009 790 800 832 846 Presumed and confirmed 181 857 106 905 74 628 46 890 70 304 68 109 43 380 76 804 62 582 32 197 9 964 4 382 Microscopy examined 90 175 86 526 80 212 54 716 48 591 49 357 42 802 38 188 42 834 38 964 20 956 4 705 Microscopy positive 14 277 13 792 10 124 4 598 5 288 7 423 3 695 5 908 8 318 2 635 693 235 RDT examined 235 536 270 080 215 055 149 946 306 310 283 256 255 306 338 514 239 461 557 045 769 844 828 141 RDT positive 82 187 93 113 59 427 39 471 63 890 60 686 39 685 70 896 54 264 29 562 9 271 4 147 Imported cases 1 2 0 Relapse cases 0 0 1 978 China1,2 Suspected cases 7 118 649 9 190 401 6 918 770 5 555 001 4 403 633 4 052 616 3 194 915 2 409 286 1 904 290 1 680 801 1 274 340 Presumed and confirmed 9 973 4 498 2 716 4 246 3 080 3 279 3 151 2 672 2 511 2 487 1 051 Microscopy examined 7 115 784 9 189 270 6 918 657 5 554 960 4 403 633 4 052 588 3 194 915 2 409 280 1 904 290 1 680 796 1 274 340 Microscopy positive 7 108 3 367 2 603 4 205 3 080 3 251 3 151 2 666 2 511 2 482 1 051 RDT examined RDT positive Imported cases 2 118 2 059 2 399 4 051 3 026 3 212 3 149 2 663 2 511 2 486 1 050 Lao People's Democratic Republic2 Suspected cases 340 119 244 956 336 783 359 143 313 859 284 361 240 505 234 365 287 984 567 650 576 026 643 583 Presumed and confirmed 22 879 17 532 46 153 39 589 50 674 36 078 15 509 8 435 9 038 6 692 3 498 3 926 Microscopy examined 212 202 167 125 192 594 225 795 153 198 133 363 113 198 116 343 89 811 128 387 87 957 63 095 Microscopy positive 6 619 6 223 13 206 11 487 10 629 6 198 2 367 1 575 1 093 898 297 185 RDT examined 127 917 77 831 144 189 133 348 160 661 150 998 127 307 118 022 198 173 439 263 488 069 580 488 RDT positive 16 260 11 309 32 947 28 102 40 045 29 880 13 142 6 860 7 945 5 794 3 201 3 741 Imported cases 0 0 4 28 Relapse cases 2 0 0 0 Malaysia2,3,6 Suspected cases 1 619 074 1 600 439 1 566 872 1 576 012 1 443 958 1 066 470 1 153 108 1 046 163 1 070 356 1 072 252 901 799 519 557 Presumed and confirmed 6 650 5 306 4 725 3 850 3 923 2 311 2 302 4 114 4 630 3 941 2 839 3 688 Microscopy examined 1 619 074 1 600 439 1 566 872 1 576 012 1 443 958 1 066 470 1 153 108 1 046 163 1 070 356 1 072 252 901 799 519 557 Microscopy positive 6 650 5 306 4 725 3 850 3 923 2 311 2 302 4 114 4 630 3 941 2 839 3 688 RDT examined RDT positive Imported cases 831 1 142 924 865 766 435 428 423 485 630 177 111 Relapse cases 32 0 0 0 0 0 0 0 Papua New Guinea Suspected cases 1 505 393 1 279 140 1 113 528 1 520 167 1 015 615 996 660 1 246 456 1 432 082 1 513 776 1 279 574 1 589 675 1 473 179 Presumed and confirmed 1 379 787 1 151 343 878 371 1 176 874 707 716 620 785 785 120 892 235 940 693 646 648 932 973 755 598 Microscopy examined 198 742 184 466 156 495 139 972 83 257 112 864 146 242 139 910 121 766 72 636 50 564 48 691 Microscopy positive 75 985 70 603 67 202 70 658 68 118 64 719 80 472 70 449 59 652 39 684 26 925 20 632 RDT examined 20 820 27 391 228 857 519 446 538 678 609 442 849 913 888 815 967 566 1 206 938 1 356 392 1 320 853 RDT positive 17 971 13 457 82 993 245 467 245 918 281 712 454 347 418 429 456 597 606 964 723 329 631 331 Philippines2,6 Suspected cases 314 788 329 665 360 126 353 823 339 319 315 010 321 848 398 759 443 997 343 174 235 560 222 002 Presumed and confirmed 19 648 9 648 9 107 8 926 6 099 11 445 6 690 6 806 4 641 5 778 6 120 4 297 Microscopy examined 301 031 327 060 332 063 317 360 287 725 224 843 255 302 171 424 122 502 170 887 103 488 107 525 Microscopy positive 18 560 9 552 7 133 5 826 3 618 5 694 2 860 889 569 1 370 1 229 641 RDT examined 13 211 2 540 27 042 35 257 51 582 90 132 66 536 227 335 321 495 172 287 132 072 114 477 RDT positive 542 31 953 1 894 2 469 5 716 3 820 5 917 4 072 4 408 4 891 3 656 Imported cases 68 85 55 69 82 95 26 35 Relapse cases 0 0 0 1 3 1 Republic of Korea2 Suspected cases 673 515 576 559** 434 294** Presumed and confirmed 1 772 838 555 443 635 692ˆ 662ˆ 515ˆ 576ˆ 559ˆ 390 294ˆ Microscopy examined 673 515 576 559 386 294 Microscopy positive 1 772 838 555 443 635 692 662 515 576 559 386 294 RDT examined 94 94 RDT positive 452 454 372 429 94 94 Imported cases 56 64 47 50 78 79 71 79 75 74 29 20 Relapse cases 4 11 16 18 28 0 6 264 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – h. REPORTED MALARIA CASES BY METhOD OF CONFIRMATION, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 SOUTH‑EAST ASIA Timor-Leste2,3 Suspected cases 143 690 118 120 122 008 86 597 90 818 114 383 115 008 144 061 217 129 103 614** 122 955** Presumed and confirmed 119 074 36 153 6 148 1 042 347 80 95 30 8 9ˆ 14ˆ 0 Microscopy examined 35 256 27 731 Microscopy positive 14 0 RDT examined 48 042 127 272 117 180 121 991 86 592 90 818 114 382 115 008 144 061 217 129 103 614 122 955 RDT positive 48 139 19 735 5 208 1 025 342 80 94 30 8 9 3 0 Imported cases 10 13 7 9 7 0 Relapse cases 1 1 0 0 0 0 WESTERN PACIFIC Cambodia5 Suspected cases 411 104 356 606 300 344 207 483 356 027 332 613 298 108 376 702 282 295 596 009 790 800 832 846 Presumed and confirmed 181 857 106 905 74 628 46 890 70 304 68 109 43 380 76 804 62 582 32 197 9 964 4 382 Microscopy examined 90 175 86 526 80 212 54 716 48 591 49 357 42 802 38 188 42 834 38 964 20 956 4 705 Microscopy positive 14 277 13 792 10 124 4 598 5 288 7 423 3 695 5 908 8 318 2 635 693 235 RDT examined 235 536 270 080 215 055 149 946 306 310 283 256 255 306 338 514 239 461 557 045 769 844 828 141 RDT positive 82 187 93 113 59 427 39 471 63 890 60 686 39 685 70 896 54 264 29 562 9 271 4 147 Imported cases 1 2 0 Relapse cases 0 0 1 978 China1,2 Suspected cases 7 118 649 9 190 401 6 918 770 5 555 001 4 403 633 4 052 616 3 194 915 2 409 286 1 904 290 1 680 801 1 274 340 Presumed and confirmed 9 973 4 498 2 716 4 246 3 080 3 279 3 151 2 672 2 511 2 487 1 051 Microscopy examined 7 115 784 9 189 270 6 918 657 5 554 960 4 403 633 4 052 588 3 194 915 2 409 280 1 904 290 1 680 796 1 274 340 Microscopy positive 7 108 3 367 2 603 4 205 3 080 3 251 3 151 2 666 2 511 2 482 1 051 RDT examined RDT positive Imported cases 2 118 2 059 2 399 4 051 3 026 3 212 3 149 2 663 2 511 2 486 1 050 Lao People's Democratic Republic2 Suspected cases 340 119 244 956 336 783 359 143 313 859 284 361 240 505 234 365 287 984 567 650 576 026 643 583 Presumed and confirmed 22 879 17 532 46 153 39 589 50 674 36 078 15 509 8 435 9 038 6 692 3 498 3 926 Microscopy examined 212 202 167 125 192 594 225 795 153 198 133 363 113 198 116 343 89 811 128 387 87 957 63 095 Microscopy positive 6 619 6 223 13 206 11 487 10 629 6 198 2 367 1 575 1 093 898 297 185 RDT examined 127 917 77 831 144 189 133 348 160 661 150 998 127 307 118 022 198 173 439 263 488 069 580 488 RDT positive 16 260 11 309 32 947 28 102 40 045 29 880 13 142 6 860 7 945 5 794 3 201 3 741 Imported cases 0 0 4 28 Relapse cases 2 0 0 0 Malaysia2,3,6 Suspected cases 1 619 074 1 600 439 1 566 872 1 576 012 1 443 958 1 066 470 1 153 108 1 046 163 1 070 356 1 072 252 901 799 519 557 Presumed and confirmed 6 650 5 306 4 725 3 850 3 923 2 311 2 302 4 114 4 630 3 941 2 839 3 688 Microscopy examined 1 619 074 1 600 439 1 566 872 1 576 012 1 443 958 1 066 470 1 153 108 1 046 163 1 070 356 1 072 252 901 799 519 557 Microscopy positive 6 650 5 306 4 725 3 850 3 923 2 311 2 302 4 114 4 630 3 941 2 839 3 688 RDT examined RDT positive Imported cases 831 1 142 924 865 766 435 428 423 485 630 177 111 Relapse cases 32 0 0 0 0 0 0 0 Papua New Guinea Suspected cases 1 505 393 1 279 140 1 113 528 1 520 167 1 015 615 996 660 1 246 456 1 432 082 1 513 776 1 279 574 1 589 675 1 473 179 Presumed and confirmed 1 379 787 1 151 343 878 371 1 176 874 707 716 620 785 785 120 892 235 940 693 646 648 932 973 755 598 Microscopy examined 198 742 184 466 156 495 139 972 83 257 112 864 146 242 139 910 121 766 72 636 50 564 48 691 Microscopy positive 75 985 70 603 67 202 70 658 68 118 64 719 80 472 70 449 59 652 39 684 26 925 20 632 RDT examined 20 820 27 391 228 857 519 446 538 678 609 442 849 913 888 815 967 566 1 206 938 1 356 392 1 320 853 RDT positive 17 971 13 457 82 993 245 467 245 918 281 712 454 347 418 429 456 597 606 964 723 329 631 331 Philippines2,6 Suspected cases 314 788 329 665 360 126 353 823 339 319 315 010 321 848 398 759 443 997 343 174 235 560 222 002 Presumed and confirmed 19 648 9 648 9 107 8 926 6 099 11 445 6 690 6 806 4 641 5 778 6 120 4 297 Microscopy examined 301 031 327 060 332 063 317 360 287 725 224 843 255 302 171 424 122 502 170 887 103 488 107 525 Microscopy positive 18 560 9 552 7 133 5 826 3 618 5 694 2 860 889 569 1 370 1 229 641 RDT examined 13 211 2 540 27 042 35 257 51 582 90 132 66 536 227 335 321 495 172 287 132 072 114 477 RDT positive 542 31 953 1 894 2 469 5 716 3 820 5 917 4 072 4 408 4 891 3 656 Imported cases 68 85 55 69 82 95 26 35 Relapse cases 0 0 0 1 3 1 Republic of Korea2 Suspected cases 673 515 576 559** 434 294** Presumed and confirmed 1 772 838 555 443 635 692ˆ 662ˆ 515ˆ 576ˆ 559ˆ 390 294ˆ Microscopy examined 673 515 576 559 386 294 Microscopy positive 1 772 838 555 443 635 692 662 515 576 559 386 294 RDT examined 94 94 RDT positive 452 454 372 429 94 94 Imported cases 56 64 47 50 78 79 71 79 75 74 29 20 Relapse cases 4 11 16 18 28 0 6 265 ANNEx 4 – h. REPORTED MALARIA CASES BY METhOD OF CONFIRMATION, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 WESTERN PACIFIC Solomon Islands Suspected cases 284 931 254 506 249 520 245 014 233 803 192 044 274 881 238 814 244 523 271 754 240 199 253 419 Presumed and confirmed 95 006 80 859 57 296 53 270 51 649 50 916 84 514 68 712 72 430 86 122 90 830 129 758 Microscopy examined 212 329 182 847 202 620 191 137 173 900 124 376 152 690 89 061 89 169 79 694 66 824 114 187 Microscopy positive 35 373 23 202 21 904 21 540 13 865 14 793 26 187 15 978 17 825 18 239 19 621 45 185 RDT examined 17 300 17 457 13 987 26 216 26 658 40 750 92 109 133 560 142 115 178 705 160 182 93 613 RDT positive 4 331 3 455 2 479 4 069 4 539 9 205 28 245 36 541 41 366 54 528 58 016 38 954 Vanuatu2 Suspected cases 55 161 38 150 39 047 32 716 40 333 16 044 24 232 34 152 26 931 23 531 29 362 24 748 Presumed and confirmed 20 982 7 263 4 812 2 883 1 314 845 2 531 1 228 644 576 507 322 Microscopy examined 29 180 19 183 16 981 15 219 18 135 4 870 6 704 9 187 5 935 4 596 2 941 1 564 Microscopy positive 4 013 2 077 733 767 190 15 225 120 53 26 22 34 RDT examined 14 816 17 883 21 786 17 497 22 198 10 900 17 249 24 965 20 996 11 318 26 421 23 184 RDT positive 5 804 4 102 3 799 2 116 1 124 556 2 027 1 108 591 550 485 288 Imported cases 0 1 12 9 14 10 Relapse cases 2 0 0 0 0 0 Viet Nam2 Suspected cases 2 803 918 3 312 266 3 436 534 3 115 804 2 786 135 2 673 662 2 497 326 2 616 257 1674897** 1 969 919** 2 053 578 2 053 889 Presumed and confirmed 54 297 45 588 43 717 35 406 27 868 19 252 10 446 8 411ˆ 6 870ˆ 5 987ˆ 1 733ˆ 467 Microscopy examined 2 760 119 2 791 917 2 897 730 2 684 996 2 357 536 2 204 409 2 082 986 2 009 233 1 674 897 1 914 379 1 521 490 852 340 Microscopy positive 17 515 16 612 19 638 17 128 15 752 9 331 4 161 4 548 4 813 4 765 1 309 448 RDT examined 7 017 491 373 514 725 412 530 416 483 459 332 408 055 603 161 492 270 504 431 532 088 218 230 RDT positive 1 594 1 848 3 243 1 168 19 Imported cases 1 681 1 565 46 90 REGIONAL SUMMARY (presumed and confirmed malaria cases) African 104 478 030 102 278 324 113 761 255 125 732 311 135 095 910 142 473 526 160 847 397 164 222 522 157 996 344 168 282 485 168 364 949 174 962 186 Americas 677 583 495 221 471 841 468 931 410 975 479 057 651 224 932 341 930 469 848 994 589 418 524 158 Eastern Mediterranean 6 369 494 5 952 130 5 835 463 4 946 316 5 343 572 5 420 260 3 683 265 4 464 691 5 289 329 4 492 250 4 198 608 4 805 377 European 271 252 666 328 270 238 226 231 255 295 144 South-East Asia 3 085 804 2 504 444 2 144 568 1 681 812 1 696 800 1 659 425 1 477 428 1 240 255 752 907 671 608 512 084 558 990 Western Pacific 1 792 851 1 429 780 1 122 080 1 372 377 923 262 813 712 954 305 1 069 932 1 104 615 790 987 1 049 905 902 732 Total 116 404 033 112 660 151 123 335 873 134 202 075 143 470 789 150 846 218 167 613 845 171 929 972 166 073 919 175 086 619 174 715 108 181 753 443 RDT: rapid diagnostic test; WHO: World Health Organization. * The country reported double counting of RDTs and microscopy but did not indicate the number of cases double counted. Confirmed cases have not been corrected. ** Suspected cases are less than presumed+microscopy examined+rdt examined due to double counting of tests by microscopy and RDT. ˆ Confirmed cases are corrected for double counting of microscopy and RDTs. + Incomplete laboratory data. Confirmed cases reported by the country exceed microscopy positive + RDT positive. ++ Unresolved data quality issues between total confirmed and total classified cases. £ Confirmed cases reported through outpatients + inpatients are used instead of laboratory confirmed cases. $ Data are available for Zanzibar only. § Imported cases are not included in total confirmed cases. Between 2010 and 2018 suspected cases were calculated based on the formula suspected = presumed+microscopy examined+rdt examined, unless reported retrospectively by the country. From 2019 onwards, suspected cases were reported by countries. If data quality issues were detected, suspected cases were recalculated applying the same formula used in 2010–2018. Data as of 23 November 2022 1 Certified malaria free countries are included in this listing for historical purposes. 2 Cases include imported and/or introduced cases. 3 There were no indigenous cases in 2021. 4 In May 2013, South Sudan was reassigned to the WHO African Region (WHA resolution 66.21, https://apps.who.int/gb/ ebwha/pdf_files/WHA66/A66_R21-en.pdf). 5 In 2021, more than one third of cases were due to relapses. 6 Figures include non-human malaria cases (Plasmodium knowlesi). 266 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – h. REPORTED MALARIA CASES BY METhOD OF CONFIRMATION, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 WESTERN PACIFIC Solomon Islands Suspected cases 284 931 254 506 249 520 245 014 233 803 192 044 274 881 238 814 244 523 271 754 240 199 253 419 Presumed and confirmed 95 006 80 859 57 296 53 270 51 649 50 916 84 514 68 712 72 430 86 122 90 830 129 758 Microscopy examined 212 329 182 847 202 620 191 137 173 900 124 376 152 690 89 061 89 169 79 694 66 824 114 187 Microscopy positive 35 373 23 202 21 904 21 540 13 865 14 793 26 187 15 978 17 825 18 239 19 621 45 185 RDT examined 17 300 17 457 13 987 26 216 26 658 40 750 92 109 133 560 142 115 178 705 160 182 93 613 RDT positive 4 331 3 455 2 479 4 069 4 539 9 205 28 245 36 541 41 366 54 528 58 016 38 954 Vanuatu2 Suspected cases 55 161 38 150 39 047 32 716 40 333 16 044 24 232 34 152 26 931 23 531 29 362 24 748 Presumed and confirmed 20 982 7 263 4 812 2 883 1 314 845 2 531 1 228 644 576 507 322 Microscopy examined 29 180 19 183 16 981 15 219 18 135 4 870 6 704 9 187 5 935 4 596 2 941 1 564 Microscopy positive 4 013 2 077 733 767 190 15 225 120 53 26 22 34 RDT examined 14 816 17 883 21 786 17 497 22 198 10 900 17 249 24 965 20 996 11 318 26 421 23 184 RDT positive 5 804 4 102 3 799 2 116 1 124 556 2 027 1 108 591 550 485 288 Imported cases 0 1 12 9 14 10 Relapse cases 2 0 0 0 0 0 Viet Nam2 Suspected cases 2 803 918 3 312 266 3 436 534 3 115 804 2 786 135 2 673 662 2 497 326 2 616 257 1674897** 1 969 919** 2 053 578 2 053 889 Presumed and confirmed 54 297 45 588 43 717 35 406 27 868 19 252 10 446 8 411ˆ 6 870ˆ 5 987ˆ 1 733ˆ 467 Microscopy examined 2 760 119 2 791 917 2 897 730 2 684 996 2 357 536 2 204 409 2 082 986 2 009 233 1 674 897 1 914 379 1 521 490 852 340 Microscopy positive 17 515 16 612 19 638 17 128 15 752 9 331 4 161 4 548 4 813 4 765 1 309 448 RDT examined 7 017 491 373 514 725 412 530 416 483 459 332 408 055 603 161 492 270 504 431 532 088 218 230 RDT positive 1 594 1 848 3 243 1 168 19 Imported cases 1 681 1 565 46 90 REGIONAL SUMMARY (presumed and confirmed malaria cases) African 104 478 030 102 278 324 113 761 255 125 732 311 135 095 910 142 473 526 160 847 397 164 222 522 157 996 344 168 282 485 168 364 949 174 962 186 Americas 677 583 495 221 471 841 468 931 410 975 479 057 651 224 932 341 930 469 848 994 589 418 524 158 Eastern Mediterranean 6 369 494 5 952 130 5 835 463 4 946 316 5 343 572 5 420 260 3 683 265 4 464 691 5 289 329 4 492 250 4 198 608 4 805 377 European 271 252 666 328 270 238 226 231 255 295 144 South-East Asia 3 085 804 2 504 444 2 144 568 1 681 812 1 696 800 1 659 425 1 477 428 1 240 255 752 907 671 608 512 084 558 990 Western Pacific 1 792 851 1 429 780 1 122 080 1 372 377 923 262 813 712 954 305 1 069 932 1 104 615 790 987 1 049 905 902 732 Total 116 404 033 112 660 151 123 335 873 134 202 075 143 470 789 150 846 218 167 613 845 171 929 972 166 073 919 175 086 619 174 715 108 181 753 443 RDT: rapid diagnostic test; WHO: World Health Organization. * The country reported double counting of RDTs and microscopy but did not indicate the number of cases double counted. Confirmed cases have not been corrected. ** Suspected cases are less than presumed+microscopy examined+rdt examined due to double counting of tests by microscopy and RDT. ˆ Confirmed cases are corrected for double counting of microscopy and RDTs. + Incomplete laboratory data. Confirmed cases reported by the country exceed microscopy positive + RDT positive. ++ Unresolved data quality issues between total confirmed and total classified cases. £ Confirmed cases reported through outpatients + inpatients are used instead of laboratory confirmed cases. $ Data are available for Zanzibar only. § Imported cases are not included in total confirmed cases. Between 2010 and 2018 suspected cases were calculated based on the formula suspected = presumed+microscopy examined+rdt examined, unless reported retrospectively by the country. From 2019 onwards, suspected cases were reported by countries. If data quality issues were detected, suspected cases were recalculated applying the same formula used in 2010–2018. Data as of 23 November 2022 1 Certified malaria free countries are included in this listing for historical purposes. 2 Cases include imported and/or introduced cases. 3 There were no indigenous cases in 2021. 4 In May 2013, South Sudan was reassigned to the WHO African Region (WHA resolution 66.21, https://apps.who.int/gb/ ebwha/pdf_files/WHA66/A66_R21-en.pdf). 5 In 2021, more than one third of cases were due to relapses. 6 Figures include non-human malaria cases (Plasmodium knowlesi). 267 ANNEx 4 – I. REPORTED MALARIA CASES BY SPECIES, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 AFRICAN Algeria1 Indigenous cases 1* 1* 55* 8* 0* 0* 0* 0* 0* 0* 0* 0* Total P. falciparum Total P. vivax Total mixed cases Total other cases Imported cases 396 187 828 587 260 727 420 446 1 241 1 014 2 725 1 164 Angola Indigenous cases 1 682 870 1 632 282 1 496 834 1 999 868 2 298 979 2 769 305 3 794 253 3 874 892 5 150 575 7 054 978 6 599 327 8 325 921 Total P. falciparum Total P. vivax Total mixed cases Total other cases Benin Indigenous cases 422 968 705 839 1 090 602 1 309 238 1 721 626 1 610 790 1 933 912 1 975 812 2 895 878 2 516 465 2 876 368 Total P. falciparum 68 745 1 044 235 1 268 347 1 324 576 1 696 777 1 768 450 2 895 878 2 516 465 2 876 368 Total P. vivax Total mixed cases Total other cases Botswana Indigenous cases 1 046 432 193 456 1 346 284** 659** 1 847** 534* 169* 884* 703* Total P. falciparum 1 046 432 193 456 1 346 278 640 1 831 534 169 884 703 Total P. vivax 4 Total mixed cases 12 3 Total other cases Imported cases 30 30 48 64 62 51 103 69 26 Burkina Faso Indigenous cases 804 539 428 113 3 858 046 3 769 051 5 428 655 7 015 446 9 779 411 10 557 260 10 278 970 5 877 426 10 600 340 11 791 638 Total P. falciparum 5 877 426 10 248 510 11 791 638 Total P. vivax Total mixed cases Total other cases Burundi Indigenous cases 1 763 447 1 575 237 2 166 690 4 178 338 4 726 299 5 428 710 8 793 176 8 795 952 4 966 511 9 959 533 4 720 103 6 615 714 Total P. falciparum 3 963 662 5 424 395 Total P. vivax Total mixed cases Total other cases Cabo Verde Indigenous cases 47 7* 1* 22* 26* 7* 49* 423* 2* 0* 0* 0* Total P. falciparum 47 7 1 22 26 7 49 423 2 0 0 0 Total P. vivax Total mixed cases Total other cases Imported cases 29 35 24 20 20 28 23 18 39 10 20 Total introduced cases 0 0 0 0 1 1 0 1 Cameroon Indigenous cases 17 874 66 656 42 581 1 193 281 2 476 153 2 244 788 2 257 633 2 819 803 2 890 193 3 335 174 Total P. falciparum 592 351 1 675 264 1 191 257 1 249 705 2 318 830 2 890 193 3 335 174 Total P. vivax Total mixed cases Total other cases Central African Republic Indigenous cases 87 566 163 701 295 088 598 833 1 239 317 411 913 972 119 2 416 960 1 740 970 2 002 149 Total P. falciparum 295 088 598 833 1 032 764 383 309 972 119 2 416 960 1 740 970 2 002 149 Total P. vivax Total mixed cases Total other cases Chad Indigenous cases 200 448 181 126 7 710 754 565 914 032 787 046 1 294 768 1 962 372 1 364 706 1 632 529 1 544 194 1 418 539 Total P. falciparum 1 480 402 1 418 539 Total P. vivax Total mixed cases Total other cases Comoros Indigenous cases 36 538 24 856 49 840 53 156 2 203 1 884 1 467 3 896 15 613 17 599** 4 546* 10 537* Total P. falciparum 33 791 21 387 43 681 45 687 2 203 1 300 1 066 2 274 15 613 17 599 4 546 10 537 Total P. vivax 528 334 637 72 Total mixed cases Total other cases 880 557 363 Imported cases 98 0 10 268 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – I. REPORTED MALARIA CASES BY SPECIES, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 AFRICAN Algeria1 Indigenous cases 1* 1* 55* 8* 0* 0* 0* 0* 0* 0* 0* 0* Total P. falciparum Total P. vivax Total mixed cases Total other cases Imported cases 396 187 828 587 260 727 420 446 1 241 1 014 2 725 1 164 Angola Indigenous cases 1 682 870 1 632 282 1 496 834 1 999 868 2 298 979 2 769 305 3 794 253 3 874 892 5 150 575 7 054 978 6 599 327 8 325 921 Total P. falciparum Total P. vivax Total mixed cases Total other cases Benin Indigenous cases 422 968 705 839 1 090 602 1 309 238 1 721 626 1 610 790 1 933 912 1 975 812 2 895 878 2 516 465 2 876 368 Total P. falciparum 68 745 1 044 235 1 268 347 1 324 576 1 696 777 1 768 450 2 895 878 2 516 465 2 876 368 Total P. vivax Total mixed cases Total other cases Botswana Indigenous cases 1 046 432 193 456 1 346 284** 659** 1 847** 534* 169* 884* 703* Total P. falciparum 1 046 432 193 456 1 346 278 640 1 831 534 169 884 703 Total P. vivax 4 Total mixed cases 12 3 Total other cases Imported cases 30 30 48 64 62 51 103 69 26 Burkina Faso Indigenous cases 804 539 428 113 3 858 046 3 769 051 5 428 655 7 015 446 9 779 411 10 557 260 10 278 970 5 877 426 10 600 340 11 791 638 Total P. falciparum 5 877 426 10 248 510 11 791 638 Total P. vivax Total mixed cases Total other cases Burundi Indigenous cases 1 763 447 1 575 237 2 166 690 4 178 338 4 726 299 5 428 710 8 793 176 8 795 952 4 966 511 9 959 533 4 720 103 6 615 714 Total P. falciparum 3 963 662 5 424 395 Total P. vivax Total mixed cases Total other cases Cabo Verde Indigenous cases 47 7* 1* 22* 26* 7* 49* 423* 2* 0* 0* 0* Total P. falciparum 47 7 1 22 26 7 49 423 2 0 0 0 Total P. vivax Total mixed cases Total other cases Imported cases 29 35 24 20 20 28 23 18 39 10 20 Total introduced cases 0 0 0 0 1 1 0 1 Cameroon Indigenous cases 17 874 66 656 42 581 1 193 281 2 476 153 2 244 788 2 257 633 2 819 803 2 890 193 3 335 174 Total P. falciparum 592 351 1 675 264 1 191 257 1 249 705 2 318 830 2 890 193 3 335 174 Total P. vivax Total mixed cases Total other cases Central African Republic Indigenous cases 87 566 163 701 295 088 598 833 1 239 317 411 913 972 119 2 416 960 1 740 970 2 002 149 Total P. falciparum 295 088 598 833 1 032 764 383 309 972 119 2 416 960 1 740 970 2 002 149 Total P. vivax Total mixed cases Total other cases Chad Indigenous cases 200 448 181 126 7 710 754 565 914 032 787 046 1 294 768 1 962 372 1 364 706 1 632 529 1 544 194 1 418 539 Total P. falciparum 1 480 402 1 418 539 Total P. vivax Total mixed cases Total other cases Comoros Indigenous cases 36 538 24 856 49 840 53 156 2 203 1 884 1 467 3 896 15 613 17 599** 4 546* 10 537* Total P. falciparum 33 791 21 387 43 681 45 687 2 203 1 300 1 066 2 274 15 613 17 599 4 546 10 537 Total P. vivax 528 334 637 72 Total mixed cases Total other cases 880 557 363 Imported cases 98 0 10 269 ANNEx 4 – I. REPORTED MALARIA CASES BY SPECIES, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 AFRICAN Congo Indigenous cases 37 744 120 319 43 232 66 323 51 529 171 847 127 939 116 903 117 837 91 538 189 616 Total P. falciparum 37 744 120 319 43 232 66 323 51 529 171 847 127 939 116 903 117 837 91 538 189 616 Total P. vivax Total mixed cases Total other cases Côte d’Ivoire Indigenous cases 62 726 29 976 1 140 627 2 524 326 3 712 831 3 375 904 3 754 504 4 034 781 4 766 477 5 935 178 4 980 640 7 295 068 Total P. falciparum 2 506 953 3 712 831 3 375 904 3 471 024 3 274 683 4 766 477 5 935 178 4 980 640 7 295 068 Total P. vivax Total mixed cases Total other cases Democratic Republic of the Congo Indigenous cases 2 417 780 4 561 981 4 791 598 6 719 887 10 288 519 12 538 805 16 821 130 16 793 002 16 972 207 21 608 681 22 590 647 23 249 165 Total P. falciparum 16 793 002 16 972 207 21 608 681 22 590 647 23 249 165 Total P. vivax Total mixed cases Total other cases Equatorial Guinea Indigenous cases 53 813 22 466 15 169 16 405 20 417 15 142 147 714 15 725 8 962 25 904 Total P. falciparum 53 813 22 466 15 169 16 405 17 452 Total P. vivax Total mixed cases Total other cases Eritrea Indigenous cases 35 982 34 848 21 815 21 317 50 534 28 036 24 251 54 005 46 440 93 878 74 041 43 463 Total P. falciparum 9 785 10 263 12 121 12 482 23 787 14 510 20 704 21 849 16 553 75 568 66 600 37 696 Total P. vivax 3 989 4 932 9 204 7 361 6 780 4 780 2 999 9 185 6 108 15 790 7 119 5 613 Total mixed cases 63 94 346 1 391 166 70 543 429 268 2 340 300 120 Total other cases 57 19 0 83 35 12 5 23 26 180 22 34 Eswatini Indigenous cases 268 379** 409** 728** 389** 318* 250* 440* 686* 235* 233* 505** Total P. falciparum 87 189 192 268 389 318 250 440 686 235 233 505 Total P. vivax Total mixed cases Total other cases 1 Imported cases 170 153 234 322 157 67 687 271 337 82 76 Ethiopia Indigenous cases 1 196 829 1 480 360 1 692 578 2 645 454 2 118 815 1 867 059 1 718 504 1 530 739 962 087 904 495 1 743 755 1 396 315** Total P. falciparum 732 776 814 547 946 595 1 687 163 1 250 110 1 188 627 1 142 235 1 059 847 859 675 738 155 1 340 869 912 075 Total P. vivax 390 252 665 813 745 983 958 291 868 705 678 432 576 269 470 892 102 412 166 340 263 877 252 589 Total mixed cases 73 801 30 051 14 380 Total other cases 108 958 90 607 Imported cases 836 Gabon Indigenous cases 13 936 19 753 28 982 31 900 23 867 23 915 35 244 111 719 53 182 53 659 64 957 Total P. falciparum 2 157 26 432 26 117 23 915 35 244 111 719 52 811 53 659 64 957 Total P. vivax 720 Total mixed cases 55 371 Total other cases 2 015 1 570 Gambia Indigenous cases 116 353 268 020 313 469 242 513 168 256 246 348 162 739 78 040 87 448 53 386 75 801 77 287 Total P. falciparum 64 108 190 379 271 038 240 792 99 976 240 382 153 685 69 931 87 448 53 386 75 801 77 287 Total P. vivax Total mixed cases Total other cases Ghana Indigenous cases 1 071 637 1 041 260 3 755 166 1 643 642 3 415 912 5 657 096 5 428 979 7 003 155 4 931 454 6 115 267 5 172 803 5 747 585 Total P. falciparum 926 447 593 518 3 755 166 1 629 198 3 415 912 4 319 919 4 421 788 4 266 541 4 808 163 6 075 297 5 137 777 5 719 704 Total P. vivax Total mixed cases 83 654 82 153 28 952 Total other cases 102 937 31 238 29 725 27 245 27 635 11 018 35 026 27 881 Guinea Indigenous cases 20 936 95 574 340 258 211 257 660 207 810 979 992 146 1 335 323 1 214 996 2 143 225 2 008 976 2 422 374 Total P. falciparum 20 936 5 450 191 421 63 353 660 207 810 979 992 146 1 335 323 1 214 996 2 143 225 2 008 976 2 422 374 Total P. vivax Total mixed cases Total other cases Guinea-Bissau Indigenous cases 50 391 71 982 50 381 54 584 97 424 150 085 156 471 152 619 171 075 160 907 Total P. falciparum 96 520 97 889 89 784 125 511 115 232 Total P. vivax Total mixed cases Total other cases 270 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – I. REPORTED MALARIA CASES BY SPECIES, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 AFRICAN Congo Indigenous cases 37 744 120 319 43 232 66 323 51 529 171 847 127 939 116 903 117 837 91 538 189 616 Total P. falciparum 37 744 120 319 43 232 66 323 51 529 171 847 127 939 116 903 117 837 91 538 189 616 Total P. vivax Total mixed cases Total other cases Côte d’Ivoire Indigenous cases 62 726 29 976 1 140 627 2 524 326 3 712 831 3 375 904 3 754 504 4 034 781 4 766 477 5 935 178 4 980 640 7 295 068 Total P. falciparum 2 506 953 3 712 831 3 375 904 3 471 024 3 274 683 4 766 477 5 935 178 4 980 640 7 295 068 Total P. vivax Total mixed cases Total other cases Democratic Republic of the Congo Indigenous cases 2 417 780 4 561 981 4 791 598 6 719 887 10 288 519 12 538 805 16 821 130 16 793 002 16 972 207 21 608 681 22 590 647 23 249 165 Total P. falciparum 16 793 002 16 972 207 21 608 681 22 590 647 23 249 165 Total P. vivax Total mixed cases Total other cases Equatorial Guinea Indigenous cases 53 813 22 466 15 169 16 405 20 417 15 142 147 714 15 725 8 962 25 904 Total P. falciparum 53 813 22 466 15 169 16 405 17 452 Total P. vivax Total mixed cases Total other cases Eritrea Indigenous cases 35 982 34 848 21 815 21 317 50 534 28 036 24 251 54 005 46 440 93 878 74 041 43 463 Total P. falciparum 9 785 10 263 12 121 12 482 23 787 14 510 20 704 21 849 16 553 75 568 66 600 37 696 Total P. vivax 3 989 4 932 9 204 7 361 6 780 4 780 2 999 9 185 6 108 15 790 7 119 5 613 Total mixed cases 63 94 346 1 391 166 70 543 429 268 2 340 300 120 Total other cases 57 19 0 83 35 12 5 23 26 180 22 34 Eswatini Indigenous cases 268 379** 409** 728** 389** 318* 250* 440* 686* 235* 233* 505** Total P. falciparum 87 189 192 268 389 318 250 440 686 235 233 505 Total P. vivax Total mixed cases Total other cases 1 Imported cases 170 153 234 322 157 67 687 271 337 82 76 Ethiopia Indigenous cases 1 196 829 1 480 360 1 692 578 2 645 454 2 118 815 1 867 059 1 718 504 1 530 739 962 087 904 495 1 743 755 1 396 315** Total P. falciparum 732 776 814 547 946 595 1 687 163 1 250 110 1 188 627 1 142 235 1 059 847 859 675 738 155 1 340 869 912 075 Total P. vivax 390 252 665 813 745 983 958 291 868 705 678 432 576 269 470 892 102 412 166 340 263 877 252 589 Total mixed cases 73 801 30 051 14 380 Total other cases 108 958 90 607 Imported cases 836 Gabon Indigenous cases 13 936 19 753 28 982 31 900 23 867 23 915 35 244 111 719 53 182 53 659 64 957 Total P. falciparum 2 157 26 432 26 117 23 915 35 244 111 719 52 811 53 659 64 957 Total P. vivax 720 Total mixed cases 55 371 Total other cases 2 015 1 570 Gambia Indigenous cases 116 353 268 020 313 469 242 513 168 256 246 348 162 739 78 040 87 448 53 386 75 801 77 287 Total P. falciparum 64 108 190 379 271 038 240 792 99 976 240 382 153 685 69 931 87 448 53 386 75 801 77 287 Total P. vivax Total mixed cases Total other cases Ghana Indigenous cases 1 071 637 1 041 260 3 755 166 1 643 642 3 415 912 5 657 096 5 428 979 7 003 155 4 931 454 6 115 267 5 172 803 5 747 585 Total P. falciparum 926 447 593 518 3 755 166 1 629 198 3 415 912 4 319 919 4 421 788 4 266 541 4 808 163 6 075 297 5 137 777 5 719 704 Total P. vivax Total mixed cases 83 654 82 153 28 952 Total other cases 102 937 31 238 29 725 27 245 27 635 11 018 35 026 27 881 Guinea Indigenous cases 20 936 95 574 340 258 211 257 660 207 810 979 992 146 1 335 323 1 214 996 2 143 225 2 008 976 2 422 374 Total P. falciparum 20 936 5 450 191 421 63 353 660 207 810 979 992 146 1 335 323 1 214 996 2 143 225 2 008 976 2 422 374 Total P. vivax Total mixed cases Total other cases Guinea-Bissau Indigenous cases 50 391 71 982 50 381 54 584 97 424 150 085 156 471 152 619 171 075 160 907 Total P. falciparum 96 520 97 889 89 784 125 511 115 232 Total P. vivax Total mixed cases Total other cases 271 ANNEx 4 – I. REPORTED MALARIA CASES BY SPECIES, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 AFRICAN Kenya Indigenous cases 898 531 1 002 805 1 453 471 2 375 129 2 851 555 2 041 277 3 064 796 3 607 026 2 318 090 5 019 389 3 659 170 3 828 757 Total P. falciparum 898 531 1 002 805 1 453 471 2 335 286 2 808 931 1 499 027 2 783 846 3 215 116 1 521 566 5 019 389 3 659 170 3 828 757 Total P. vivax Total mixed cases Total other cases Liberia Indigenous cases 922 173 1 921 159 1 412 629 1 244 220 881 224 941 711 1 191 137 1 093 115 915 845 912 436 Total P. falciparum 212 927 577 641 1 407 455 1 244 220 864 204 809 356 915 845 912 436 Total P. vivax Total mixed cases Total other cases Madagascar Indigenous cases 202 450 224 498 402 900 433 450 470 212** 938 490** 686 024 985 852 972 790 963 712** 969 096** 970 625** Total P. falciparum 2 020 2 224 1 949 964 Total P. vivax 24 5 253 Total mixed cases 15 Total other cases 254 Imported cases 712 1 167 7 116 1 732 203 Malawi Indigenous cases 304 499 1 564 984 1 280 892 2 905 310 3 661 238 4 827 373 4 947 443 5 865 476 5 184 107 7 139 065 6 948 500 Total P. falciparum 1 564 984 1 280 892 2 905 310 3 585 315 4 730 835 4 901 344 5 830 741 4 287 578 7 057 864 6 880 537 Total P. vivax Total mixed cases Total other cases Mali Indigenous cases 239 787 307 035 886 482 1 506 940 2 039 853 2 454 508 2 311 098 2 277 218 2 345 475 3 221 535 2 666 266 3 204 130 Total P. falciparum 3 165 483 2 666 266 3 204 130 Total P. vivax Total mixed cases Total other cases Mauritania Indigenous cases 1 994 2 926 1 888 1 587 15 835 22 631 29 156 20 105 30 609 14 869 12 425 18 660 Total P. falciparum Total P. vivax Total mixed cases Total other cases Mayotte Indigenous cases 237*** 50*** 29*** 11** 1* 1* 18* 9* 3* Total P. falciparum 138 38 21 9 1 12 Total P. vivax 3 2 2 Total mixed cases 4 Total other cases Imported cases 236 51 47 71 14 10 10 10 44 Total introduced cases 5 Mozambique Indigenous cases 1 522 577 1 756 874 1 853 276 3 282 172 7 407 175 8 222 814 9 690 873 9 892 601 10 304 472 11 734 926 11 318 685 10 095 807 Total P. falciparum 878 009 663 132 927 841 2 998 874 7 117 648 7 718 782 8 520 376 8 921 081 9 292 928 11 734 926 11 318 685 10 095 807 Total P. vivax Total mixed cases Total other cases Namibia Indigenous cases 556 1 525 194 4 775 15 692 9 162* 19 510* 54 268* 30 567* 2 376* 12 331** 12 979* Total P. falciparum 556 335 194 136 15 692 9 162 19 510 54 268 30 567 2 340 12 331 12 979 Total P. vivax 6 Total mixed cases Total other cases Imported cases 2 888 3 980 11 874 4 021 1 064 1 305 759 Niger Indigenous cases 642 774 838 585 2 329 260 2 373 591 3 963 768 2 392 108 4 258 110 2 761 268 3 046 450 3 771 451 4 377 938 4 044 707 Total P. falciparum 601 455 757 449 2 185 060 2 306 354 3 828 486 2 267 867 3 961 178 2 638 580 3 046 450 3 748 155 4 154 337 4 044 707 Total P. vivax Total mixed cases 17 123 21 370 22 399 46 068 78 102 Total other cases 4 133 186 989 23 296 Nigeria Indigenous cases 551 187 8 572 322 8 068 583 13 598 282 13 087 878 14 548 024 19 806 915 18 325 240 21 325 186 Total P. falciparum 523 513 1 515 920 13 524 751 920 398 15 251 460 18 765 444 Total P. vivax Total mixed cases Total other cases 272 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – I. REPORTED MALARIA CASES BY SPECIES, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 AFRICAN Kenya Indigenous cases 898 531 1 002 805 1 453 471 2 375 129 2 851 555 2 041 277 3 064 796 3 607 026 2 318 090 5 019 389 3 659 170 3 828 757 Total P. falciparum 898 531 1 002 805 1 453 471 2 335 286 2 808 931 1 499 027 2 783 846 3 215 116 1 521 566 5 019 389 3 659 170 3 828 757 Total P. vivax Total mixed cases Total other cases Liberia Indigenous cases 922 173 1 921 159 1 412 629 1 244 220 881 224 941 711 1 191 137 1 093 115 915 845 912 436 Total P. falciparum 212 927 577 641 1 407 455 1 244 220 864 204 809 356 915 845 912 436 Total P. vivax Total mixed cases Total other cases Madagascar Indigenous cases 202 450 224 498 402 900 433 450 470 212** 938 490** 686 024 985 852 972 790 963 712** 969 096** 970 625** Total P. falciparum 2 020 2 224 1 949 964 Total P. vivax 24 5 253 Total mixed cases 15 Total other cases 254 Imported cases 712 1 167 7 116 1 732 203 Malawi Indigenous cases 304 499 1 564 984 1 280 892 2 905 310 3 661 238 4 827 373 4 947 443 5 865 476 5 184 107 7 139 065 6 948 500 Total P. falciparum 1 564 984 1 280 892 2 905 310 3 585 315 4 730 835 4 901 344 5 830 741 4 287 578 7 057 864 6 880 537 Total P. vivax Total mixed cases Total other cases Mali Indigenous cases 239 787 307 035 886 482 1 506 940 2 039 853 2 454 508 2 311 098 2 277 218 2 345 475 3 221 535 2 666 266 3 204 130 Total P. falciparum 3 165 483 2 666 266 3 204 130 Total P. vivax Total mixed cases Total other cases Mauritania Indigenous cases 1 994 2 926 1 888 1 587 15 835 22 631 29 156 20 105 30 609 14 869 12 425 18 660 Total P. falciparum Total P. vivax Total mixed cases Total other cases Mayotte Indigenous cases 237*** 50*** 29*** 11** 1* 1* 18* 9* 3* Total P. falciparum 138 38 21 9 1 12 Total P. vivax 3 2 2 Total mixed cases 4 Total other cases Imported cases 236 51 47 71 14 10 10 10 44 Total introduced cases 5 Mozambique Indigenous cases 1 522 577 1 756 874 1 853 276 3 282 172 7 407 175 8 222 814 9 690 873 9 892 601 10 304 472 11 734 926 11 318 685 10 095 807 Total P. falciparum 878 009 663 132 927 841 2 998 874 7 117 648 7 718 782 8 520 376 8 921 081 9 292 928 11 734 926 11 318 685 10 095 807 Total P. vivax Total mixed cases Total other cases Namibia Indigenous cases 556 1 525 194 4 775 15 692 9 162* 19 510* 54 268* 30 567* 2 376* 12 331** 12 979* Total P. falciparum 556 335 194 136 15 692 9 162 19 510 54 268 30 567 2 340 12 331 12 979 Total P. vivax 6 Total mixed cases Total other cases Imported cases 2 888 3 980 11 874 4 021 1 064 1 305 759 Niger Indigenous cases 642 774 838 585 2 329 260 2 373 591 3 963 768 2 392 108 4 258 110 2 761 268 3 046 450 3 771 451 4 377 938 4 044 707 Total P. falciparum 601 455 757 449 2 185 060 2 306 354 3 828 486 2 267 867 3 961 178 2 638 580 3 046 450 3 748 155 4 154 337 4 044 707 Total P. vivax Total mixed cases 17 123 21 370 22 399 46 068 78 102 Total other cases 4 133 186 989 23 296 Nigeria Indigenous cases 551 187 8 572 322 8 068 583 13 598 282 13 087 878 14 548 024 19 806 915 18 325 240 21 325 186 Total P. falciparum 523 513 1 515 920 13 524 751 920 398 15 251 460 18 765 444 Total P. vivax Total mixed cases Total other cases 273 ANNEx 4 – I. REPORTED MALARIA CASES BY SPECIES, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 AFRICAN Rwanda Indigenous cases 669 322 273 293 483 470 962 618 1 623 176 2 505 794 3 380 568 5 940 533 4 231 883 3 612 822 2 043 392 1 163 670 Total P. falciparum 638 669 208 858 483 470 962 618 1 623 176 2 927 780 1 657 793 3 612 822 2 043 392 1 163 670 Total P. vivax Total mixed cases Total other cases Sao Tome and Principe Indigenous cases 3 146 8 442 12 550 9 243 1 754 2 056** 2 238 2 239** 2 937** 2 732** 1 933** 2 719** Total P. falciparum 2 219 6 363 10 700 9 242 1 754 2 055 2 238 2 239 2 937 2 447 1 933 2 719 Total P. vivax 14 4 1 1 Total mixed cases Total other cases 6 1 0 Imported cases 2 0 2 3 10 11 11 Senegal Indigenous cases 330 331 274 119 294 385 366 687 265 624 492 253 349 540 395 706 530 652** 354 663** 445 313 536 850 Total P. falciparum 330 331 274 119 294 385 366 687 265 624 492 253 349 540 395 706 530 652 354 663 445 313 536 850 Total P. vivax Total mixed cases Total other cases Imported cases 0 0 0 292 45 0 0 Sierra Leone Indigenous cases 934 028 644 018 1 537 322 1 701 958 1 374 476 1 483 376 1 775 306 1 651 236 1 733 831 2 407 505 725 006 1 953 902 Total P. falciparum 218 473 30 670 1 537 322 1 701 958 1 374 476 1 483 376 1 775 306 1 651 236 1 733 831 2 407 505 725 006 1 953 902 Total P. vivax Total mixed cases Total other cases South Africa Indigenous cases 8 060 9 866 6 621 8 645 11 705 4 959 4 323 23 381* 9 562*** 4 821*** 4 463*** 2 958*** Total P. falciparum 2 181 4 206 4 565 8 645 11 563 4 344 4 323 23 381 9 540 3 096 3 173 1 032 Total P. vivax 14 5 5 Total mixed cases 12 3 Total other cases 5 15 5 Imported cases 3 568 3 075 6 234 5 742 8 890 3 663 2 854 South Sudan2 Indigenous cases 900 283 112 024 225 371 262 520 71 377 24 371 7 619 1 488 005 98 843 1 903 742 661 922 2 017 227 Total P. falciparum 112 024 225 371 262 520 71 377 24 371 7 619 1 488 005 3 242 1 902 505 145 954 1 943 519 Total P. vivax 2 205 43 197 Total mixed cases Total other cases 3 851 30 511 Togo Indigenous cases 1 019 029 506 764 909 129 965 832 1 524 339 1 610 711 1 746 334 1 756 582 2 002 877 2 406 091 996 364 Total P. falciparum 1 018 801 506 741 909 120 965 824 1 524 322 1 610 568 1 746 101 1 756 331 2 002 712 2 402 967 995 655 Total P. vivax Total mixed cases Total other cases 228 23 9 8 17 143 233 251 165 3 124 709 Uganda Indigenous cases 1 666 582 316 241 2 662 258 1 502 362 3 631 939 7 412 747 9 735 849 11 667 831 5 759 174 13 982 362 16 329 136 14 336 387 Total P. falciparum 1 565 348 316 241 2 662 258 1 502 362 3 631 939 7 137 662 9 385 132 11 667 831 5 759 174 13 982 362 16 329 136 14 336 387 Total P. vivax 15 812 Total mixed cases 47 435 Total other cases United Republic of Tanzania Indigenous cases 1 279 362 2 151 236 1 987 629 1 554 117** 680 442** 4 900 278** 5 762 462 5 744 907 6 051 844** 5 908 168** 5 969 953** 4 452 499** Total P. falciparum 1 279 362 1 817 668 1 774 792 71 132 108 844 413 633 5 015$ 1 733$ 1 462$ 1 338$ 5 674 847 4 452 135 Total P. vivax 12$ Total mixed cases 201$ 89$ 1 606$ 132$ 2 000$ 363$ Total other cases 10$ 65$ 208$ Imported cases 719$ 1583$ 2 550$ 1 754$ 3 286$ 4 314$ 4 319$ Total introduced cases 0$ 0$ 0$ Mainland Indigenous cases 1 277 024 2 146 747 1 984 698 1 552 444 678 207 4 898 211 5 755 669 5 739 863 6 050 382 5 906 621 5 963 725 4 450 468 Total P. falciparum 1 277 024 1 813 179 1 772 062 69 459 106 609 411 741 5 670 676 4 450 468 Total P. vivax Total mixed cases Total other cases Zanzibar Indigenous cases 2 338 4 489 2 931 1 673* 2 235* 2 067** 6 793 5 044 1 462* 1 547* 6 228*ˆ 2 031* Total P. falciparum 2 338 4 489 2 730 1 673 2 235 1 892 5 015 1 733 1 462 1 338 4 171ˆ 1 667 Total P. vivax 12 Total mixed cases 201 89 1 606 0 132 2 000 363 Total other cases 10 0 65 208 Imported cases 719 1 583 2 550 1 754 3 286 4 314 4 319 274 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – I. REPORTED MALARIA CASES BY SPECIES, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 AFRICAN Rwanda Indigenous cases 669 322 273 293 483 470 962 618 1 623 176 2 505 794 3 380 568 5 940 533 4 231 883 3 612 822 2 043 392 1 163 670 Total P. falciparum 638 669 208 858 483 470 962 618 1 623 176 2 927 780 1 657 793 3 612 822 2 043 392 1 163 670 Total P. vivax Total mixed cases Total other cases Sao Tome and Principe Indigenous cases 3 146 8 442 12 550 9 243 1 754 2 056** 2 238 2 239** 2 937** 2 732** 1 933** 2 719** Total P. falciparum 2 219 6 363 10 700 9 242 1 754 2 055 2 238 2 239 2 937 2 447 1 933 2 719 Total P. vivax 14 4 1 1 Total mixed cases Total other cases 6 1 0 Imported cases 2 0 2 3 10 11 11 Senegal Indigenous cases 330 331 274 119 294 385 366 687 265 624 492 253 349 540 395 706 530 652** 354 663** 445 313 536 850 Total P. falciparum 330 331 274 119 294 385 366 687 265 624 492 253 349 540 395 706 530 652 354 663 445 313 536 850 Total P. vivax Total mixed cases Total other cases Imported cases 0 0 0 292 45 0 0 Sierra Leone Indigenous cases 934 028 644 018 1 537 322 1 701 958 1 374 476 1 483 376 1 775 306 1 651 236 1 733 831 2 407 505 725 006 1 953 902 Total P. falciparum 218 473 30 670 1 537 322 1 701 958 1 374 476 1 483 376 1 775 306 1 651 236 1 733 831 2 407 505 725 006 1 953 902 Total P. vivax Total mixed cases Total other cases South Africa Indigenous cases 8 060 9 866 6 621 8 645 11 705 4 959 4 323 23 381* 9 562*** 4 821*** 4 463*** 2 958*** Total P. falciparum 2 181 4 206 4 565 8 645 11 563 4 344 4 323 23 381 9 540 3 096 3 173 1 032 Total P. vivax 14 5 5 Total mixed cases 12 3 Total other cases 5 15 5 Imported cases 3 568 3 075 6 234 5 742 8 890 3 663 2 854 South Sudan2 Indigenous cases 900 283 112 024 225 371 262 520 71 377 24 371 7 619 1 488 005 98 843 1 903 742 661 922 2 017 227 Total P. falciparum 112 024 225 371 262 520 71 377 24 371 7 619 1 488 005 3 242 1 902 505 145 954 1 943 519 Total P. vivax 2 205 43 197 Total mixed cases Total other cases 3 851 30 511 Togo Indigenous cases 1 019 029 506 764 909 129 965 832 1 524 339 1 610 711 1 746 334 1 756 582 2 002 877 2 406 091 996 364 Total P. falciparum 1 018 801 506 741 909 120 965 824 1 524 322 1 610 568 1 746 101 1 756 331 2 002 712 2 402 967 995 655 Total P. vivax Total mixed cases Total other cases 228 23 9 8 17 143 233 251 165 3 124 709 Uganda Indigenous cases 1 666 582 316 241 2 662 258 1 502 362 3 631 939 7 412 747 9 735 849 11 667 831 5 759 174 13 982 362 16 329 136 14 336 387 Total P. falciparum 1 565 348 316 241 2 662 258 1 502 362 3 631 939 7 137 662 9 385 132 11 667 831 5 759 174 13 982 362 16 329 136 14 336 387 Total P. vivax 15 812 Total mixed cases 47 435 Total other cases United Republic of Tanzania Indigenous cases 1 279 362 2 151 236 1 987 629 1 554 117** 680 442** 4 900 278** 5 762 462 5 744 907 6 051 844** 5 908 168** 5 969 953** 4 452 499** Total P. falciparum 1 279 362 1 817 668 1 774 792 71 132 108 844 413 633 5 015$ 1 733$ 1 462$ 1 338$ 5 674 847 4 452 135 Total P. vivax 12$ Total mixed cases 201$ 89$ 1 606$ 132$ 2 000$ 363$ Total other cases 10$ 65$ 208$ Imported cases 719$ 1583$ 2 550$ 1 754$ 3 286$ 4 314$ 4 319$ Total introduced cases 0$ 0$ 0$ Mainland Indigenous cases 1 277 024 2 146 747 1 984 698 1 552 444 678 207 4 898 211 5 755 669 5 739 863 6 050 382 5 906 621 5 963 725 4 450 468 Total P. falciparum 1 277 024 1 813 179 1 772 062 69 459 106 609 411 741 5 670 676 4 450 468 Total P. vivax Total mixed cases Total other cases Zanzibar Indigenous cases 2 338 4 489 2 931 1 673* 2 235* 2 067** 6 793 5 044 1 462* 1 547* 6 228*ˆ 2 031* Total P. falciparum 2 338 4 489 2 730 1 673 2 235 1 892 5 015 1 733 1 462 1 338 4 171ˆ 1 667 Total P. vivax 12 Total mixed cases 201 89 1 606 0 132 2 000 363 Total other cases 10 0 65 208 Imported cases 719 1 583 2 550 1 754 3 286 4 314 4 319 275 ANNEx 4 – I. REPORTED MALARIA CASES BY SPECIES, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 AFRICAN Zambia Indigenous cases 4 077 547 4 184 661 4 851 319 5 505 639 5 039 679 5 147 350 8 121 215 6 769 142 Total P. falciparum 4 077 547 4 184 661 4 851 319 5 505 639 5 039 679 5 147 350 8 121 215 6 769 142 Total P. vivax Total mixed cases Total other cases Zimbabwe Indigenous cases 249 379 321 901 277 736 423 702 550 696 484 614** 316 631** 471 030** 263 346** 308 173 447 381 133 137 Total P. falciparum 249 379 321 901 277 736 423 702 538 351 393 886 282 616 319 146 183 755 308 173 447 381 133 137 Total P. vivax Total mixed cases Total other cases Imported cases 180 358 768 672 AMERICAS Argentina1 Indigenous cases 54* 0* 0* 0* 0* 0* 0* 0* 0* 0* 0* Total P. falciparum 0 0 0 0 0 0 0 0 0 0 0 Total P. vivax 14 Total mixed cases Total other cases Imported cases 55 28 16 11 15 11 9 18 23 Total introduced cases 11 0 0 0 0 0 0 0 0 Belize Indigenous cases 150 72* 33* 20* 19* 9* 4* 7* 3* 0* 0* 0* Total P. falciparum 1 0 0 0 Total P. vivax 149 72 33 20 19 9 4 5 2 0 0 0 Total mixed cases 1 2 0 Total other cases Imported cases 7 4 4 0 4 1 2 4 2 0 0 Bolivia (Plurinational State of) Indigenous cases 13 769 7 143 7 415 7 342 7 401 6 874* 5 542* 4 572** 5 342* 9 338* 12 180** 9 959* Total P. falciparum 1 165 370 337 959 325 77 4 31 66 165 Total P. vivax 12 569 6 756 7 067 6 346 7 060 6 785 5 535 4 572 5 342 9 299 12 107 9 729 Total mixed cases 35 17 11 37 16 12 3 5 7 65 Total other cases Imported cases 33 11 15 12 19 7 0 Brazil Indigenous cases 334 668 267 146 242 758 169 623** 137 888**ˆ 141 229** 124 177* 189 559* 187 757* 153 294* 143 395* 139 021* Total P. falciparum 47 407 32 029 31 913 25 928 21 156ˆ 14 762 13 160 18 614 17 861 15 137 21 437 21 612 Total P. vivax 283 435 231 368 203 018 137 887 115 809ˆ 122 746 110 340 169 887 168 552 136 947 119 911 115 258 Total mixed cases 3 642 3 606 7 722 5 015 894 701 669 1 032 1 333 1 189 2 040 2 135 Total other cases 183 143 104 51 38 37 8 26 11 21 7 16 Imported cases 8 923 4 856 4 932 5 068 4 867 6 816 4 165 1 811 1 365 Colombia Indigenous cases 117 650 64 436 60 179 51 722 40 768 55 334* 82 609* 52 805* 61 195* 78 109* 75 779** 73 221** Total P. falciparum 32 900 14 650 15 215 17 650 20 067 27 875 47 232 29 558 29 953 40 074 21 935 19 847 Total P. vivax 83 255 44 701 44 283 33 345 20 129 19 002 32 635 22 132 30 063 37 197 22 260 18 424 Total mixed cases 1 434 754 672 690 567 739 2 742 1 115 1 179 838 787 468 Total other cases 48 16 9 11 5 0 0 0 0 0 0 9 Imported cases 7 785 618 1 297 1 948 2 306 457 758 Costa Rica Indigenous cases 110* 10* 6* 0* 0* 0* 4* 12* 70* 95* 90* 189*** Total P. falciparum 9 8 2 15 Total P. vivax 110 10 5 4 12 61 87 88 171 Total mixed cases Total other cases Imported cases 4 6 1 4 5 8 9 13 38 45 34 27 Total introduced cases 23 1 0 0 0 0 0 0 0 5 17 4 Dominican Republic Indigenous cases 2 482 1 616 952 473* 459* 631* 690* 341* 433* 1 291* 826* 284* Total P. falciparum 2 480 1 614 950 473 459 631 690 341 433 1 291 826 284 Total P. vivax 2 2 2 Total mixed cases Total other cases Imported cases 106 37 30 65 57 51 23 3 7 276 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – I. REPORTED MALARIA CASES BY SPECIES, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 AFRICAN Zambia Indigenous cases 4 077 547 4 184 661 4 851 319 5 505 639 5 039 679 5 147 350 8 121 215 6 769 142 Total P. falciparum 4 077 547 4 184 661 4 851 319 5 505 639 5 039 679 5 147 350 8 121 215 6 769 142 Total P. vivax Total mixed cases Total other cases Zimbabwe Indigenous cases 249 379 321 901 277 736 423 702 550 696 484 614** 316 631** 471 030** 263 346** 308 173 447 381 133 137 Total P. falciparum 249 379 321 901 277 736 423 702 538 351 393 886 282 616 319 146 183 755 308 173 447 381 133 137 Total P. vivax Total mixed cases Total other cases Imported cases 180 358 768 672 AMERICAS Argentina1 Indigenous cases 54* 0* 0* 0* 0* 0* 0* 0* 0* 0* 0* Total P. falciparum 0 0 0 0 0 0 0 0 0 0 0 Total P. vivax 14 Total mixed cases Total other cases Imported cases 55 28 16 11 15 11 9 18 23 Total introduced cases 11 0 0 0 0 0 0 0 0 Belize Indigenous cases 150 72* 33* 20* 19* 9* 4* 7* 3* 0* 0* 0* Total P. falciparum 1 0 0 0 Total P. vivax 149 72 33 20 19 9 4 5 2 0 0 0 Total mixed cases 1 2 0 Total other cases Imported cases 7 4 4 0 4 1 2 4 2 0 0 Bolivia (Plurinational State of) Indigenous cases 13 769 7 143 7 415 7 342 7 401 6 874* 5 542* 4 572** 5 342* 9 338* 12 180** 9 959* Total P. falciparum 1 165 370 337 959 325 77 4 31 66 165 Total P. vivax 12 569 6 756 7 067 6 346 7 060 6 785 5 535 4 572 5 342 9 299 12 107 9 729 Total mixed cases 35 17 11 37 16 12 3 5 7 65 Total other cases Imported cases 33 11 15 12 19 7 0 Brazil Indigenous cases 334 668 267 146 242 758 169 623** 137 888**ˆ 141 229** 124 177* 189 559* 187 757* 153 294* 143 395* 139 021* Total P. falciparum 47 407 32 029 31 913 25 928 21 156ˆ 14 762 13 160 18 614 17 861 15 137 21 437 21 612 Total P. vivax 283 435 231 368 203 018 137 887 115 809ˆ 122 746 110 340 169 887 168 552 136 947 119 911 115 258 Total mixed cases 3 642 3 606 7 722 5 015 894 701 669 1 032 1 333 1 189 2 040 2 135 Total other cases 183 143 104 51 38 37 8 26 11 21 7 16 Imported cases 8 923 4 856 4 932 5 068 4 867 6 816 4 165 1 811 1 365 Colombia Indigenous cases 117 650 64 436 60 179 51 722 40 768 55 334* 82 609* 52 805* 61 195* 78 109* 75 779** 73 221** Total P. falciparum 32 900 14 650 15 215 17 650 20 067 27 875 47 232 29 558 29 953 40 074 21 935 19 847 Total P. vivax 83 255 44 701 44 283 33 345 20 129 19 002 32 635 22 132 30 063 37 197 22 260 18 424 Total mixed cases 1 434 754 672 690 567 739 2 742 1 115 1 179 838 787 468 Total other cases 48 16 9 11 5 0 0 0 0 0 0 9 Imported cases 7 785 618 1 297 1 948 2 306 457 758 Costa Rica Indigenous cases 110* 10* 6* 0* 0* 0* 4* 12* 70* 95* 90* 189*** Total P. falciparum 9 8 2 15 Total P. vivax 110 10 5 4 12 61 87 88 171 Total mixed cases Total other cases Imported cases 4 6 1 4 5 8 9 13 38 45 34 27 Total introduced cases 23 1 0 0 0 0 0 0 0 5 17 4 Dominican Republic Indigenous cases 2 482 1 616 952 473* 459* 631* 690* 341* 433* 1 291* 826* 284* Total P. falciparum 2 480 1 614 950 473 459 631 690 341 433 1 291 826 284 Total P. vivax 2 2 2 Total mixed cases Total other cases Imported cases 106 37 30 65 57 51 23 3 7 277 ANNEx 4 – I. REPORTED MALARIA CASES BY SPECIES, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 AMERICAS Ecuador Indigenous cases 1 888 1 219* 544* 368* 242 618* 1 191* 1 275* 1 653* 1 803* 1 934* 2 175* Total P. falciparum 258 290 78 160 40 184 403 309 149 211 214 445 Total P. vivax 1 630 928 466 208 202 434 788 963 1 504 1 592 1 715 1 728 Total mixed cases 3 5 2 Total other cases Imported cases 14 14 10 59 233 105 153 106 67 70 El Salvador1 Indigenous cases 17* 7* 13* 6* 6* 5* 12* 0* 0* 0* 0* Total P. falciparum Total P. vivax 17 7 13 6 6 5 12 0 0 0 0 Total mixed cases Total other cases Imported cases 9 8 7 1 2 4 1 3 2 3 0 French Guiana Indigenous cases 1 632ˆ 1 209ˆ 900ˆ 875 448 374** 217** 554** 546 212* 140** 74* Total P. falciparum 987ˆ 584ˆ 382ˆ 304 136 61 55 62 49 17 3 1 Total P. vivax 476ˆ 339ˆ 257ˆ 220 129 194 95 368 496 193 137 72 Total mixed cases 561 496 381 348 182 119 67 124 0 0 0 Total other cases 5 5 2 0 1 1 2 0 1 Imported cases 60 41 43 36 14 37 Guatemala Indigenous cases 7 384 6 817 5 346 6 214* 4 929* 5 538* 5 000** 4 121** 3 018* 2 069* 1 058* 1 273* Total P. falciparum 30 64 54 101 24 43 4 0 0 0 0 0 Total P. vivax 7 163 6 707 5 278 6 062 4 838 5 487 4 849 4 121 3 018 2 069 1 058 1 273 Total mixed cases 5 3 14 51 67 8 0 0 0 0 Total other cases Imported cases 2 2 1 3 3 3 0 Guyana Indigenous cases 22 935 29 471 31 601 31 479 12 354 9 984 10 697** 13 936 17 038 18 826 17 230 20 850 Total P. falciparum 11 244 15 945 16 695 13 655 3 943 3 219 3 576 5 141 6 033 5 737 5 544 5 994 Total P. vivax 8 402 9 066 11 225 13 953 7 173 6 002 6 081 7 645 9 854 11 940 10 901 13 963 Total mixed cases 3 157 4 364 3 598 3 770 1 197 731 781 1 078 1 086 1 110 757 846 Total other cases 132 96 83 101 41 32 57 72 65 39 28 47 Imported cases 411 Haiti Indigenous cases 84 153 34 350 27 866 26 543 17 696 17 583 21 430 19 135 8 828 10 687 22 996 9 513 Total P. falciparum 84 153 32 969 25 423 20 957 17 696 17 583 21 430 19 135 8 828 10 687 22 996 9 513 Total P. vivax 0 0 0 0 0 0 0 0 0 0 0 0 Total mixed cases 0 0 0 0 0 0 0 0 0 0 0 0 Total other cases 0 0 0 0 0 0 0 0 0 0 0 0 Honduras Indigenous cases 9 745 7 618 6 439 5 364 3 378** 3 555 4 094** 1 273** 632** 330** 815* 1 550** Total P. falciparum 866 581 559 1 073 528 902 1 310 128 55 11 223 657 Total P. vivax 8 759 7 013 5 856 4 245 2 813 2 626 2 744 1 145 576 319 576 879 Total mixed cases 120 24 24 46 37 27 40 0 1 0 7 8 Total other cases Imported cases 2 0 3 10 21 61 98 107 Total introduced cases 0 0 0 0 0 5 10 Mexico Indigenous cases 1 226* 1 124* 833* 495* 656* 517* 551* 736* 803* 618* 356* 242* Total P. falciparum Total P. vivax 1 226 1 124 833 495 656 517 551 736 803 618 356 242 Total mixed cases Total other cases Imported cases 7 6 9 4 10 34 45 29 23 22 10 31 Total introduced cases 0 0 0 0 0 0 0 0 0 1 0 Nicaragua Indigenous cases 692 925 1 235 1 162* 1 142* 2 279* 6 272* 10 949* 15 917* 13 200* 25 505* 23 259* Total P. falciparum 154 150 236 208 155 338 1 285 1 836 1 319 2 398 11 250 10 454 Total P. vivax 538 775 999 954 985 1 937 4 965 9 080 14 553 10 679 13 421 10 364 Total mixed cases 2 4 22 33 45 123 834 441 Total other cases Imported cases 34 21 29 12 3 17 26 25 64 Panama Indigenous cases 418 354 844 696* 864* 546* 769* 649* 684* 1 756* 1 946* 4 354****ˆ Total P. falciparum 20 1 1 21 1 0 12 9 10ˆ Total P. vivax 398 353 843 696 864 546 748 648 684 1 744 1 937 4 883ˆ Total mixed cases 0 23 0 2 Total other cases 0 0 0 Imported cases 9 10 16 42 40 31 15 10 7 Total introduced cases 0 0 0 0 0 0 68 111 3 278 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – I. REPORTED MALARIA CASES BY SPECIES, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 AMERICAS Ecuador Indigenous cases 1 888 1 219* 544* 368* 242 618* 1 191* 1 275* 1 653* 1 803* 1 934* 2 175* Total P. falciparum 258 290 78 160 40 184 403 309 149 211 214 445 Total P. vivax 1 630 928 466 208 202 434 788 963 1 504 1 592 1 715 1 728 Total mixed cases 3 5 2 Total other cases Imported cases 14 14 10 59 233 105 153 106 67 70 El Salvador1 Indigenous cases 17* 7* 13* 6* 6* 5* 12* 0* 0* 0* 0* Total P. falciparum Total P. vivax 17 7 13 6 6 5 12 0 0 0 0 Total mixed cases Total other cases Imported cases 9 8 7 1 2 4 1 3 2 3 0 French Guiana Indigenous cases 1 632ˆ 1 209ˆ 900ˆ 875 448 374** 217** 554** 546 212* 140** 74* Total P. falciparum 987ˆ 584ˆ 382ˆ 304 136 61 55 62 49 17 3 1 Total P. vivax 476ˆ 339ˆ 257ˆ 220 129 194 95 368 496 193 137 72 Total mixed cases 561 496 381 348 182 119 67 124 0 0 0 Total other cases 5 5 2 0 1 1 2 0 1 Imported cases 60 41 43 36 14 37 Guatemala Indigenous cases 7 384 6 817 5 346 6 214* 4 929* 5 538* 5 000** 4 121** 3 018* 2 069* 1 058* 1 273* Total P. falciparum 30 64 54 101 24 43 4 0 0 0 0 0 Total P. vivax 7 163 6 707 5 278 6 062 4 838 5 487 4 849 4 121 3 018 2 069 1 058 1 273 Total mixed cases 5 3 14 51 67 8 0 0 0 0 Total other cases Imported cases 2 2 1 3 3 3 0 Guyana Indigenous cases 22 935 29 471 31 601 31 479 12 354 9 984 10 697** 13 936 17 038 18 826 17 230 20 850 Total P. falciparum 11 244 15 945 16 695 13 655 3 943 3 219 3 576 5 141 6 033 5 737 5 544 5 994 Total P. vivax 8 402 9 066 11 225 13 953 7 173 6 002 6 081 7 645 9 854 11 940 10 901 13 963 Total mixed cases 3 157 4 364 3 598 3 770 1 197 731 781 1 078 1 086 1 110 757 846 Total other cases 132 96 83 101 41 32 57 72 65 39 28 47 Imported cases 411 Haiti Indigenous cases 84 153 34 350 27 866 26 543 17 696 17 583 21 430 19 135 8 828 10 687 22 996 9 513 Total P. falciparum 84 153 32 969 25 423 20 957 17 696 17 583 21 430 19 135 8 828 10 687 22 996 9 513 Total P. vivax 0 0 0 0 0 0 0 0 0 0 0 0 Total mixed cases 0 0 0 0 0 0 0 0 0 0 0 0 Total other cases 0 0 0 0 0 0 0 0 0 0 0 0 Honduras Indigenous cases 9 745 7 618 6 439 5 364 3 378** 3 555 4 094** 1 273** 632** 330** 815* 1 550** Total P. falciparum 866 581 559 1 073 528 902 1 310 128 55 11 223 657 Total P. vivax 8 759 7 013 5 856 4 245 2 813 2 626 2 744 1 145 576 319 576 879 Total mixed cases 120 24 24 46 37 27 40 0 1 0 7 8 Total other cases Imported cases 2 0 3 10 21 61 98 107 Total introduced cases 0 0 0 0 0 5 10 Mexico Indigenous cases 1 226* 1 124* 833* 495* 656* 517* 551* 736* 803* 618* 356* 242* Total P. falciparum Total P. vivax 1 226 1 124 833 495 656 517 551 736 803 618 356 242 Total mixed cases Total other cases Imported cases 7 6 9 4 10 34 45 29 23 22 10 31 Total introduced cases 0 0 0 0 0 0 0 0 0 1 0 Nicaragua Indigenous cases 692 925 1 235 1 162* 1 142* 2 279* 6 272* 10 949* 15 917* 13 200* 25 505* 23 259* Total P. falciparum 154 150 236 208 155 338 1 285 1 836 1 319 2 398 11 250 10 454 Total P. vivax 538 775 999 954 985 1 937 4 965 9 080 14 553 10 679 13 421 10 364 Total mixed cases 2 4 22 33 45 123 834 441 Total other cases Imported cases 34 21 29 12 3 17 26 25 64 Panama Indigenous cases 418 354 844 696* 864* 546* 769* 649* 684* 1 756* 1 946* 4 354****ˆ Total P. falciparum 20 1 1 21 1 0 12 9 10ˆ Total P. vivax 398 353 843 696 864 546 748 648 684 1 744 1 937 4 883ˆ Total mixed cases 0 23 0 2 Total other cases 0 0 0 Imported cases 9 10 16 42 40 31 15 10 7 Total introduced cases 0 0 0 0 0 0 68 111 3 279 ANNEx 4 – I. REPORTED MALARIA CASES BY SPECIES, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 AMERICAS Paraguay1 Indigenous cases 20* 1* 0* 0* 0* 0* 0* 0* 0* 0* 0* 0* Total P. falciparum Total P. vivax 20 1 Total mixed cases Total other cases Imported cases 9 9 15 11 8 8 10 5 0 2 1 4 Total introduced cases 9 0 0 0 0 0 0 0 0 0 0 Peru Indigenous cases 31 545 25 005 31 436ˆ 48 719ˆ 65 252* 61 865* 56 623* 55 367* 45 443* 24 324* 15 822* 18 075* Total P. falciparum 2 291 2 929 3 399ˆ 7 890ˆ 10 416 12 569 15 319 13 173 9 438 4 724 3 198 3 584 Total P. vivax 29 168 21 984 28 030ˆ 40 829ˆ 54 819 49 287 41 287 42 044 36 005 19 600 12 624 14 490 Total mixed cases 83 89 102 213 0 Total other cases 3 3 7 11 17 9 17 2 1 Imported cases 48 57 176 159 25 65 Suriname Indigenous cases 1 771 795 569 525**§ 401 81* 78* 137** 37** 104** 156** 22* Total P. falciparum 638 331 161 322 165 17 6 33 5 0 0 0 Total P. vivax 817 382 180 322 78 61 69 99 12 104 156 22 Total mixed cases 83 21 13 85 158 3 1 5 1 0 0 0 Total other cases 36 17 2 Imported cases 204 295 251 414 198 111 88 53 Total introduced cases 0 0 0 1 0 0 2 Venezuela (Bolivarian Republic of) Indigenous cases 45 155 45 824 52 803 78 643* 90 708* 136 402* 240 613* 411 586* 404 924* 398 285* 190 314* 174 409* Total P. falciparum 10 629 9 724 10 978 22 777 21 074 24 018 46 503 69 076 71 504 64 307 33 887# 23 428 Total P. vivax 32 710 34 651 39 478 50 938 62 850 100 880 179 554 316 401 307 622 308 132 151 783# 143 287 Total mixed cases 286 909 2 324 4 882 6 769 11 491 14 531 26 080 25 789 25 846 11 795# 7 693 Total other cases 60 6 23 46 15 13 25 29 9 1# 1 Imported cases 1 677 1 210 1 594 1 948 2 941 2 125 1 848 1 356 829 Total introduced cases 513 1 1 1 7 152 4 556 EASTERN MEDITERRANEAN Afghanistan Indigenous cases 69 798 77 549 54 840 52 965 106 478 119 859 241 233 313 086 248 689 173 860 105 295 86 263 Total P. falciparum 6 142 5 581 1 231 1 877 3 000 4 004 6 369 6 907 6 437 2 701 2 691 3 921 Total P. vivax 63 255 71 968 53 609 43 369 58 362 82 891 132 407 154 468 166 583 170 747 102 316 82 094 Total mixed cases 311 403 473 412 288 248 Total other cases Djibouti Indigenous cases 1 010 25 1 684 9 439 9 473 13 822 14 810 25 319 49 402 73 535 58 916 Total P. falciparum 1 010 20 11 781 9 290 16 130 36 025 46 537 44 385 Total P. vivax 2 041 5 381 9 189 13 377 26 998 14 531 Total mixed cases Total other cases Iran (Islamic Republic of) Indigenous cases 1 847* 1 632*ˆ 756*ˆ 480*ˆ 358*ˆ 167* 81* 57* 0* 0* 0* 0* Total P. falciparum 166 152ˆ 44ˆ 72ˆ 21ˆ 8 0 2 0 0 0 0 Total P. vivax 1 656 1 502ˆ 711ˆ 426ˆ 351ˆ 157 79 55 0 0 0 0 Total mixed cases 25 56 32 22 4 1 2 0 0 0 0 Total other cases Imported cases 1 184 1 529 842 853 867 632 611 868 602 1 107 878 821 Total introduced cases 78 12 26 7 24 8 13 20 85 171 178 Pakistan Indigenous cases 240 591 334 589 290 781ˆ 274 648 264 867 203 859 329 005 369 817 374 510 413 533 371 828 399 097 Total P. falciparum 73 857 73 925 95 095ˆ 46 974 33 340 28 311 43 821 54 407 55 639 87 169 74 899 94 543 Total P. vivax 143 136 205 879 228 215ˆ 215 655 221 816 167 460 260 477 300 623 314 572 323 355 293 077 301 169 Total mixed cases 2 901 12 019 9 711 8 088 24 707 14 787 4 299 3 009 3 852 3 385 Total other cases Saudi Arabia Indigenous cases 29* 69* 82* 34* 30* 83* 272* 177* 61* 38* 83* 0* Total P. falciparum 29 69 82 34 30 83 270 172 57 38 83 0 Total P. vivax 2 5 4 0 Total mixed cases Total other cases Imported cases 1 912 2 719 3 324 2 479 2 254 2 537 5 110 2 974 2 517 2 029 3 453 2 470 Total introduced cases 0 0 0 0 21 0 0 0 133 85 122 146 280 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – I. REPORTED MALARIA CASES BY SPECIES, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 AMERICAS Paraguay1 Indigenous cases 20* 1* 0* 0* 0* 0* 0* 0* 0* 0* 0* 0* Total P. falciparum Total P. vivax 20 1 Total mixed cases Total other cases Imported cases 9 9 15 11 8 8 10 5 0 2 1 4 Total introduced cases 9 0 0 0 0 0 0 0 0 0 0 Peru Indigenous cases 31 545 25 005 31 436ˆ 48 719ˆ 65 252* 61 865* 56 623* 55 367* 45 443* 24 324* 15 822* 18 075* Total P. falciparum 2 291 2 929 3 399ˆ 7 890ˆ 10 416 12 569 15 319 13 173 9 438 4 724 3 198 3 584 Total P. vivax 29 168 21 984 28 030ˆ 40 829ˆ 54 819 49 287 41 287 42 044 36 005 19 600 12 624 14 490 Total mixed cases 83 89 102 213 0 Total other cases 3 3 7 11 17 9 17 2 1 Imported cases 48 57 176 159 25 65 Suriname Indigenous cases 1 771 795 569 525**§ 401 81* 78* 137** 37** 104** 156** 22* Total P. falciparum 638 331 161 322 165 17 6 33 5 0 0 0 Total P. vivax 817 382 180 322 78 61 69 99 12 104 156 22 Total mixed cases 83 21 13 85 158 3 1 5 1 0 0 0 Total other cases 36 17 2 Imported cases 204 295 251 414 198 111 88 53 Total introduced cases 0 0 0 1 0 0 2 Venezuela (Bolivarian Republic of) Indigenous cases 45 155 45 824 52 803 78 643* 90 708* 136 402* 240 613* 411 586* 404 924* 398 285* 190 314* 174 409* Total P. falciparum 10 629 9 724 10 978 22 777 21 074 24 018 46 503 69 076 71 504 64 307 33 887# 23 428 Total P. vivax 32 710 34 651 39 478 50 938 62 850 100 880 179 554 316 401 307 622 308 132 151 783# 143 287 Total mixed cases 286 909 2 324 4 882 6 769 11 491 14 531 26 080 25 789 25 846 11 795# 7 693 Total other cases 60 6 23 46 15 13 25 29 9 1# 1 Imported cases 1 677 1 210 1 594 1 948 2 941 2 125 1 848 1 356 829 Total introduced cases 513 1 1 1 7 152 4 556 EASTERN MEDITERRANEAN Afghanistan Indigenous cases 69 798 77 549 54 840 52 965 106 478 119 859 241 233 313 086 248 689 173 860 105 295 86 263 Total P. falciparum 6 142 5 581 1 231 1 877 3 000 4 004 6 369 6 907 6 437 2 701 2 691 3 921 Total P. vivax 63 255 71 968 53 609 43 369 58 362 82 891 132 407 154 468 166 583 170 747 102 316 82 094 Total mixed cases 311 403 473 412 288 248 Total other cases Djibouti Indigenous cases 1 010 25 1 684 9 439 9 473 13 822 14 810 25 319 49 402 73 535 58 916 Total P. falciparum 1 010 20 11 781 9 290 16 130 36 025 46 537 44 385 Total P. vivax 2 041 5 381 9 189 13 377 26 998 14 531 Total mixed cases Total other cases Iran (Islamic Republic of) Indigenous cases 1 847* 1 632*ˆ 756*ˆ 480*ˆ 358*ˆ 167* 81* 57* 0* 0* 0* 0* Total P. falciparum 166 152ˆ 44ˆ 72ˆ 21ˆ 8 0 2 0 0 0 0 Total P. vivax 1 656 1 502ˆ 711ˆ 426ˆ 351ˆ 157 79 55 0 0 0 0 Total mixed cases 25 56 32 22 4 1 2 0 0 0 0 Total other cases Imported cases 1 184 1 529 842 853 867 632 611 868 602 1 107 878 821 Total introduced cases 78 12 26 7 24 8 13 20 85 171 178 Pakistan Indigenous cases 240 591 334 589 290 781ˆ 274 648 264 867 203 859 329 005 369 817 374 510 413 533 371 828 399 097 Total P. falciparum 73 857 73 925 95 095ˆ 46 974 33 340 28 311 43 821 54 407 55 639 87 169 74 899 94 543 Total P. vivax 143 136 205 879 228 215ˆ 215 655 221 816 167 460 260 477 300 623 314 572 323 355 293 077 301 169 Total mixed cases 2 901 12 019 9 711 8 088 24 707 14 787 4 299 3 009 3 852 3 385 Total other cases Saudi Arabia Indigenous cases 29* 69* 82* 34* 30* 83* 272* 177* 61* 38* 83* 0* Total P. falciparum 29 69 82 34 30 83 270 172 57 38 83 0 Total P. vivax 2 5 4 0 Total mixed cases Total other cases Imported cases 1 912 2 719 3 324 2 479 2 254 2 537 5 110 2 974 2 517 2 029 3 453 2 470 Total introduced cases 0 0 0 0 21 0 0 0 133 85 122 146 281 ANNEx 4 – I. REPORTED MALARIA CASES BY SPECIES, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 EASTERN MEDITERRANEAN Somalia Indigenous cases 24 833 3 351 6 817 7 407 11 246 20 953 35 628 35 138 31 021 39 687 27 333 12 967 Total P. falciparum 5 629 830 462 2 657 36 766 10 919 Total P. vivax 0 11 28 825 2 921 2 048 Total mixed cases Total other cases Sudan Indigenous cases 720 557 506 806 526 931 592 383 1 068 506 586 827 566 015 800 116 1 648 683 1 752 011 1 698 394 1 647 745 Total P. falciparum 333 009 580 145 1 286 915 1 363 507 1 272 738 1 182 363 Total P. vivax 82 175 58 335 143 314 194 904 170 202 175 590 Total mixed cases 32 557 82 399 187 270 193 600 143 066 160 222 Total other cases Yemen Indigenous cases 106 697 90 954 112 359 102 778 86 707 76 259 99 700 143 333 157 900 165 899 164 066 180 339 Total P. falciparum 77 271 59 689 109 504 102 369 86 428 75 898 45 469 109 849 112 823 163 941 162 318 173 922 Total P. vivax 966 478 398 408 267 334 347 1 833 970 1 802 1 684 4 343 Total mixed cases 30 7 2 12 27 70 2 322 63 114 3 46 Total other cases 2 33 4 69 42 61 2 028 EUROPEAN Armenia1 Indigenous cases 0* 0* 0* 0* 0* 0* 0* 0* 0 0* Total P. falciparum 0 0 0 0 0 0 0 0 0 0 0 Total P. vivax 0 0 0 0 0 0 0 0 0 0 0 Total mixed cases 0 0 0 0 0 0 0 0 0 0 0 Total other cases 0 0 0 0 0 0 0 0 0 0 0 Imported cases 1 4 0 1 2 2 2 6 3 Azerbaijan Indigenous cases 50* 4* 3* 0* 0* 0* 0* 0* 0* 0* Total P. falciparum Total P. vivax 50 4 3 0 0 0 0 0 0 0 Total mixed cases 0 0 0 0 0 0 0 0 0 0 Total other cases 0 0 0 0 0 0 0 0 0 0 0 Imported cases 2 4 1 4 2 1 1 1 2 Georgia Indigenous cases 0* 0* 0* 0* 0* 0* 0* 0* 0* 0* 0* Total P. falciparum 0 0 0 0 0 0 0 0 0 0 0 Total P. vivax 0 0 0 0 0 0 0 0 0 0 0 Total mixed cases 0 0 0 0 0 0 0 0 0 0 0 Total other cases 0 0 0 0 0 0 0 0 0 0 0 Imported cases 0 5 4 7 5 5 7 8 9 8 4 Total introduced cases 0 1 1 0 0 0 0 0 Kyrgyzstan1 Indigenous cases 3* 0* 0* 0* 0* 0* 0* 0* 0* 0* 0* Total P. falciparum 0 0 0 0 0 0 0 0 0 0 Total P. vivax 3 0 0 0 0 0 0 0 0 0 0 Total mixed cases 0 0 0 0 0 0 0 0 0 0 0 Total other cases 0 0 0 0 0 0 0 0 0 0 0 Imported cases 3 5 3 4 0 1 6 2 0 1 0 Tajikistan Indigenous cases 112* 78* 28* 4* 2* 0* 0* 0* 0* 0* 0* Total P. falciparum 1 5 0 0 0 0 0 0 Total P. vivax 111 73 0 0 0 0 0 0 Total mixed cases 0 0 0 0 0 0 Total other cases 0 0 0 0 0 0 Imported cases 4 22 11 10 5 4 1 3 0 3 Total introduced cases 0 0 15 11 0 0 0 0 Türkiye Indigenous cases 0* 0* 0* 0* 0* 0* 0* 0* 0* 0* 0* Total P. falciparum 0 0 0 0 0 0 0 0 0 0 0 Total P. vivax 0 0 0 0 0 0 0 0 0 0 0 Total mixed cases 0 0 0 0 0 0 0 0 0 0 0 Total other cases 0 0 0 0 0 0 0 0 0 0 0 Imported cases 81 128 376 251 249 221 208 214 237 277 133 Total introduced cases 0 0 219 0 5 3 1 0 1 2 2 Turkmenistan1 Indigenous cases 0* 0* 0* 0* 0* 0* 0* 0* 0* Total P. falciparum 0 0 0 0 0 0 0 0 0 Total P. vivax 0 0 0 0 0 0 0 0 0 Total mixed cases 0 0 0 0 0 0 0 0 0 Total other cases 0 0 0 0 0 0 0 0 0 Imported cases 0 0 0 0 0 0 0 0 0 3 0 282 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – I. REPORTED MALARIA CASES BY SPECIES, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 EASTERN MEDITERRANEAN Somalia Indigenous cases 24 833 3 351 6 817 7 407 11 246 20 953 35 628 35 138 31 021 39 687 27 333 12 967 Total P. falciparum 5 629 830 462 2 657 36 766 10 919 Total P. vivax 0 11 28 825 2 921 2 048 Total mixed cases Total other cases Sudan Indigenous cases 720 557 506 806 526 931 592 383 1 068 506 586 827 566 015 800 116 1 648 683 1 752 011 1 698 394 1 647 745 Total P. falciparum 333 009 580 145 1 286 915 1 363 507 1 272 738 1 182 363 Total P. vivax 82 175 58 335 143 314 194 904 170 202 175 590 Total mixed cases 32 557 82 399 187 270 193 600 143 066 160 222 Total other cases Yemen Indigenous cases 106 697 90 954 112 359 102 778 86 707 76 259 99 700 143 333 157 900 165 899 164 066 180 339 Total P. falciparum 77 271 59 689 109 504 102 369 86 428 75 898 45 469 109 849 112 823 163 941 162 318 173 922 Total P. vivax 966 478 398 408 267 334 347 1 833 970 1 802 1 684 4 343 Total mixed cases 30 7 2 12 27 70 2 322 63 114 3 46 Total other cases 2 33 4 69 42 61 2 028 EUROPEAN Armenia1 Indigenous cases 0* 0* 0* 0* 0* 0* 0* 0* 0 0* Total P. falciparum 0 0 0 0 0 0 0 0 0 0 0 Total P. vivax 0 0 0 0 0 0 0 0 0 0 0 Total mixed cases 0 0 0 0 0 0 0 0 0 0 0 Total other cases 0 0 0 0 0 0 0 0 0 0 0 Imported cases 1 4 0 1 2 2 2 6 3 Azerbaijan Indigenous cases 50* 4* 3* 0* 0* 0* 0* 0* 0* 0* Total P. falciparum Total P. vivax 50 4 3 0 0 0 0 0 0 0 Total mixed cases 0 0 0 0 0 0 0 0 0 0 Total other cases 0 0 0 0 0 0 0 0 0 0 0 Imported cases 2 4 1 4 2 1 1 1 2 Georgia Indigenous cases 0* 0* 0* 0* 0* 0* 0* 0* 0* 0* 0* Total P. falciparum 0 0 0 0 0 0 0 0 0 0 0 Total P. vivax 0 0 0 0 0 0 0 0 0 0 0 Total mixed cases 0 0 0 0 0 0 0 0 0 0 0 Total other cases 0 0 0 0 0 0 0 0 0 0 0 Imported cases 0 5 4 7 5 5 7 8 9 8 4 Total introduced cases 0 1 1 0 0 0 0 0 Kyrgyzstan1 Indigenous cases 3* 0* 0* 0* 0* 0* 0* 0* 0* 0* 0* Total P. falciparum 0 0 0 0 0 0 0 0 0 0 Total P. vivax 3 0 0 0 0 0 0 0 0 0 0 Total mixed cases 0 0 0 0 0 0 0 0 0 0 0 Total other cases 0 0 0 0 0 0 0 0 0 0 0 Imported cases 3 5 3 4 0 1 6 2 0 1 0 Tajikistan Indigenous cases 112* 78* 28* 4* 2* 0* 0* 0* 0* 0* 0* Total P. falciparum 1 5 0 0 0 0 0 0 Total P. vivax 111 73 0 0 0 0 0 0 Total mixed cases 0 0 0 0 0 0 Total other cases 0 0 0 0 0 0 Imported cases 4 22 11 10 5 4 1 3 0 3 Total introduced cases 0 0 15 11 0 0 0 0 Türkiye Indigenous cases 0* 0* 0* 0* 0* 0* 0* 0* 0* 0* 0* Total P. falciparum 0 0 0 0 0 0 0 0 0 0 0 Total P. vivax 0 0 0 0 0 0 0 0 0 0 0 Total mixed cases 0 0 0 0 0 0 0 0 0 0 0 Total other cases 0 0 0 0 0 0 0 0 0 0 0 Imported cases 81 128 376 251 249 221 208 214 237 277 133 Total introduced cases 0 0 219 0 5 3 1 0 1 2 2 Turkmenistan1 Indigenous cases 0* 0* 0* 0* 0* 0* 0* 0* 0* Total P. falciparum 0 0 0 0 0 0 0 0 0 Total P. vivax 0 0 0 0 0 0 0 0 0 Total mixed cases 0 0 0 0 0 0 0 0 0 Total other cases 0 0 0 0 0 0 0 0 0 Imported cases 0 0 0 0 0 0 0 0 0 3 0 283 ANNEx 4 – I. REPORTED MALARIA CASES BY SPECIES, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 EUROPEAN Uzbekistan1 Indigenous cases 3* 0* 0* 0* 0* 0* 0* 0* 0* 0* 0* Total P. falciparum 0 0 0 0 0 0 0 0 0 0 0 Total P. vivax 3 0 0 0 0 0 0 0 0 0 0 Total mixed cases 0 0 0 0 0 0 0 0 0 0 0 Total other cases 0 0 0 0 0 0 0 0 0 0 0 Imported cases 2 1 1 3 1 0 0 0 0 1 2 Total introduced cases 1 0 0 0 0 0 0 0 SOUTH‑EAST ASIA Bangladesh Indigenous cases 55 873 51 773 29 518 26 891 57 480 39 590** 27 628** 29 228** 10 482** 17 219** 6 128** 7 288** Total P. falciparum 52 012 49 084 27 651 25 815 41 261 26 453 17 269 23 315 8 470 14 752 4 744 4 992 Total P. vivax 3 824 2 579 1 699 983 3 348 4 000 3 297 4 442 1 672 2 126 1 245 1 953 Total mixed cases 37 110 168 93 12 871 9 137 7 062 1 471 340 341 139 343 Total other cases Imported cases 129 109 19 41 6 2 6 Bhutan Indigenous cases 436 194 82* 15* 19* 34* 15* 11* 6* 2* 22* 9* Total P. falciparum 140 87 33 6 11 13 1 0 1 0 0 1 Total P. vivax 261 92 47 9 8 21 13 11 5 2 22 7 Total mixed cases 35 15 0 1 0 0 0 0 1 Total other cases Imported cases 0 23 34 70 56 38 34 30 9 13 Total introduced cases 0 0 0 3 13 14 10 23 0 Democratic People's Republic of Korea Indigenous cases 13 520 16 760 21 850* 14 407* 10 535* 7 022 5 033 4 603 3 698 1 869 1 819 2 357 Total P. falciparum 0 0 0 0 0 0 0 0 0 0 0 0 Total P. vivax 13 520 16 760 21 850 14 407 10 535 7 022 5 033 4 603 3 598 1 869 1 819 2 357 Total mixed cases Total other cases India Indigenous cases 1 599 986 1 310 656 1 067 824 881 730 1 102 205 1 169 261 1 087 285 844 558 429 928 338 494 186 532 161 753* Total P. falciparum 830 779 662 748 524 370 462 079 720 795 774 627 706 257 525 637 204 733 154 645 117 567 100 442 Total P. vivax 765 622 645 652 534 129 417 884 379 659 390 440 375 783 315 028 222 730 181 514 67 444 60 187 Total mixed cases 3 585 2 256 9 325 1 767 1 751 4 194 5 245 3 893 2 465 2 295 1 520 1 124 Total other cases 1 Indonesia Indigenous cases 465 764 422 447 417 819 343 527 252 027 217 017** 218 449** 261 557** 222 125** 250 553** 254 001** 304 579** Total P. falciparum 220 077 200 662 199 977 170 848 126 397 103 315 118 844 143 926 116 035 142 036 141 807 152 724 Total P. vivax 187 583 187 989 187 583 150 985 107 260 94 267 81 748 95 694 84 862 86 742 83 743 114 301 Total mixed cases 21 964 31 535 29 278 20 352 16 410 13 105 16 751 18 899 18 383 18 707 25 148 31 893 Total other cases 2 547 2 261 981 1 342 1 960 1 387 1 106 1 818 2 585 3 057 3 288 5 658 Imported cases 11 61 38 27 Myanmar Indigenous cases 420 808 465 294 481 204 333 871 205 658 182 465ˆ 110 146 85 019 76 518 56 410ˆ 58 827**§ 79 000** Total P. falciparum 388 464 433 146 314 676 222 770 138 311 110 449ˆ 62 917 50 730 38 483 23 017ˆ 15 191 14 614 Total P. vivax 29 944 28 966 135 386 98 860 61 830 65 536ˆ 43 748 32 070 36 502 32 788ˆ 43 578 63 909 Total mixed cases 2 054 3 020 31 039 12 216 5 511 6 624 3 476 2 214 1 530 606 407 523 Total other cases 346 162 103 25 6 7 5 5 3 4 0 0 Imported cases 9 1 Nepal Indigenous cases 3 894 2 335** 2 204** 1 974 832* 591* 507* 623* 493* 127* 73* 32* Total P. falciparum 550 240 184 273 81 67 61 25 5 9 5 3 Total P. vivax 2 349 1 208 888 1 659 693 504 433 587 488 118 68 29 Total mixed cases 216 30 22 58 20 13 11 0 0 0 Total other cases Imported cases 1 079 1 026 667 521 502 670 539 579 357 359 Sri Lanka1 Indigenous cases 684* 124* 23* 0* 0* 0* 0* 0* 0* 0* 0* 0* Total P. falciparum 6 3 4 0 0 0 0 0 0 0 0 0 Total P. vivax 668 119 19 0 0 0 0 0 0 0 0 0 Total mixed cases 0 0 0 0 0 0 0 Total other cases Imported cases 52 51 70 95 49 36 41 57 48 53 30 25 Total introduced cases 0 0 0 0 0 1 0 0 0 284 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – I. REPORTED MALARIA CASES BY SPECIES, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 EUROPEAN Uzbekistan1 Indigenous cases 3* 0* 0* 0* 0* 0* 0* 0* 0* 0* 0* Total P. falciparum 0 0 0 0 0 0 0 0 0 0 0 Total P. vivax 3 0 0 0 0 0 0 0 0 0 0 Total mixed cases 0 0 0 0 0 0 0 0 0 0 0 Total other cases 0 0 0 0 0 0 0 0 0 0 0 Imported cases 2 1 1 3 1 0 0 0 0 1 2 Total introduced cases 1 0 0 0 0 0 0 0 SOUTH‑EAST ASIA Bangladesh Indigenous cases 55 873 51 773 29 518 26 891 57 480 39 590** 27 628** 29 228** 10 482** 17 219** 6 128** 7 288** Total P. falciparum 52 012 49 084 27 651 25 815 41 261 26 453 17 269 23 315 8 470 14 752 4 744 4 992 Total P. vivax 3 824 2 579 1 699 983 3 348 4 000 3 297 4 442 1 672 2 126 1 245 1 953 Total mixed cases 37 110 168 93 12 871 9 137 7 062 1 471 340 341 139 343 Total other cases Imported cases 129 109 19 41 6 2 6 Bhutan Indigenous cases 436 194 82* 15* 19* 34* 15* 11* 6* 2* 22* 9* Total P. falciparum 140 87 33 6 11 13 1 0 1 0 0 1 Total P. vivax 261 92 47 9 8 21 13 11 5 2 22 7 Total mixed cases 35 15 0 1 0 0 0 0 1 Total other cases Imported cases 0 23 34 70 56 38 34 30 9 13 Total introduced cases 0 0 0 3 13 14 10 23 0 Democratic People's Republic of Korea Indigenous cases 13 520 16 760 21 850* 14 407* 10 535* 7 022 5 033 4 603 3 698 1 869 1 819 2 357 Total P. falciparum 0 0 0 0 0 0 0 0 0 0 0 0 Total P. vivax 13 520 16 760 21 850 14 407 10 535 7 022 5 033 4 603 3 598 1 869 1 819 2 357 Total mixed cases Total other cases India Indigenous cases 1 599 986 1 310 656 1 067 824 881 730 1 102 205 1 169 261 1 087 285 844 558 429 928 338 494 186 532 161 753* Total P. falciparum 830 779 662 748 524 370 462 079 720 795 774 627 706 257 525 637 204 733 154 645 117 567 100 442 Total P. vivax 765 622 645 652 534 129 417 884 379 659 390 440 375 783 315 028 222 730 181 514 67 444 60 187 Total mixed cases 3 585 2 256 9 325 1 767 1 751 4 194 5 245 3 893 2 465 2 295 1 520 1 124 Total other cases 1 Indonesia Indigenous cases 465 764 422 447 417 819 343 527 252 027 217 017** 218 449** 261 557** 222 125** 250 553** 254 001** 304 579** Total P. falciparum 220 077 200 662 199 977 170 848 126 397 103 315 118 844 143 926 116 035 142 036 141 807 152 724 Total P. vivax 187 583 187 989 187 583 150 985 107 260 94 267 81 748 95 694 84 862 86 742 83 743 114 301 Total mixed cases 21 964 31 535 29 278 20 352 16 410 13 105 16 751 18 899 18 383 18 707 25 148 31 893 Total other cases 2 547 2 261 981 1 342 1 960 1 387 1 106 1 818 2 585 3 057 3 288 5 658 Imported cases 11 61 38 27 Myanmar Indigenous cases 420 808 465 294 481 204 333 871 205 658 182 465ˆ 110 146 85 019 76 518 56 410ˆ 58 827**§ 79 000** Total P. falciparum 388 464 433 146 314 676 222 770 138 311 110 449ˆ 62 917 50 730 38 483 23 017ˆ 15 191 14 614 Total P. vivax 29 944 28 966 135 386 98 860 61 830 65 536ˆ 43 748 32 070 36 502 32 788ˆ 43 578 63 909 Total mixed cases 2 054 3 020 31 039 12 216 5 511 6 624 3 476 2 214 1 530 606 407 523 Total other cases 346 162 103 25 6 7 5 5 3 4 0 0 Imported cases 9 1 Nepal Indigenous cases 3 894 2 335** 2 204** 1 974 832* 591* 507* 623* 493* 127* 73* 32* Total P. falciparum 550 240 184 273 81 67 61 25 5 9 5 3 Total P. vivax 2 349 1 208 888 1 659 693 504 433 587 488 118 68 29 Total mixed cases 216 30 22 58 20 13 11 0 0 0 Total other cases Imported cases 1 079 1 026 667 521 502 670 539 579 357 359 Sri Lanka1 Indigenous cases 684* 124* 23* 0* 0* 0* 0* 0* 0* 0* 0* 0* Total P. falciparum 6 3 4 0 0 0 0 0 0 0 0 0 Total P. vivax 668 119 19 0 0 0 0 0 0 0 0 0 Total mixed cases 0 0 0 0 0 0 0 Total other cases Imported cases 52 51 70 95 49 36 41 57 48 53 30 25 Total introduced cases 0 0 0 0 0 1 0 0 0 285 ANNEx 4 – I. REPORTED MALARIA CASES BY SPECIES, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 SOUTH‑EAST ASIA Thailand Indigenous cases 32 480 24 897 46 895 41 602 41 218 14 265** 12 076** 7 416** 5 110** 4 065** 3 123** 2 426** Total P. falciparum 9 401 5 710 11 553 14 449 13 743 3 301 1 774 846 447 391 155 53 Total P. vivax 13 401 8 608 17 506 15 573 20 513 4 655 5 765 4 802 3 575 2 752 2 892 2 313 Total mixed cases 147 147 196 588 122 109 36 34 25 16 9 Total other cases 20 13 3 084 3 077 12 1 244 10 21 30 58 42 Imported cases 9 890 5 724 4 020 1 618 1 342 798 800 Timor-Leste Indigenous cases 48 139 19 735 5 208 1 025 342 80 81* 16* 0* 0* 3* 0* Total P. falciparum 36 239 14 261 1 962 373 118 33 46 4 0 0 2 0 Total P. vivax 11 432 3 754 2 288 512 139 24 7 3 0 0 0 0 Total mixed cases 468 1 720 958 140 85 23 28 9 0 0 1 0 Total other cases 0 0 0 0 0 0 0 Imported cases 10 13 7 9 7 0 Total introduced cases 2 0 1 0 4 0 WESTERN PACIFIC Cambodia Indigenous cases 96 464 106 905 69 551 44 069 69 178 68 109 43 380 76 804 62 582 32 196** 9 962** 4 382 Total P. falciparum 8 213 7 054 14 896 7 092 8 332 17 830 12 156 20 328 10 525 4 833 1 073 342 Total P. vivax 4 794 5 155 19 575 11 267 10 356 13 146 9 816 15 207 30 680 26 871 8 722 4 018 Total mixed cases 1 270 1 583 4 971 2 418 6 464 2 954 1 520 1 397 1 080 492 167 17 Total other cases 0 0 0 0 0 0 0 0 0 0 0 5 Imported cases 1 2 0 China1 Indigenous cases 4 990* 1 308* 244* 83* 53* 39* 1* 0* 0* 0* 0* Total P. falciparum 1 269 57 16 11 6 1 0 0 0 0 0 Total P. vivax 3 675 677 179 67 45 26 1 0 0 0 0 Total mixed cases 26 1 5 1 0 0 0 0 0 0 Total other cases 0 0 0 0 0 6 0 0 0 0 0 Imported cases 2 118 2 059 2 399 4 051 3 026 3 212 3 149 2 663 2 511 2 486 1 050 Lao People's Democratic Republic Indigenous cases 22 879 17 532 46 153 39 589 50 674 36 078 15 509 8 435 9 022****§ 6 691**** 3 489**** 3 897**** Total P. falciparum 22 452 16 556 37 685 25 441 25 317 14 439 5 737 4 169 4 828 2 168 1 573 1 339 Total P. vivax 403 962 7 594 13 067 23 763 20 815 9 441 4 104 4 099 4 444 1 879 2 536 Total mixed cases 24 0 873 1 079 1 593 823 329 162 111 79 37 22 Total other cases 0 10 0 2 0 0 0 0 2 0 0 0 Imported cases 0 0 4 28 Total introduced cases 16 1 5 1 Malaysia Indigenous cases 5 194* 3 954* 3 662* 1 028* 596* 242** 266* 85* 0* 0* 0* 0* Total P. falciparum 1 344 973 894 422 177 110 67 18 0 0 0 0 Total P. vivax 3 387 1 750 915 385 241 84 178 59 0 0 0 0 Total mixed cases 145 120 48 42 33 22 9 1 0 0 0 0 Total other cases 0 0 0 0 0 26 12 7 0 0 0 0 Imported cases 831 1 142 924 865 766 435 428 423 485 630 177 111 Total introduced cases 107 124 35 26 8 16 0 21 96 54 2 Papua New Guinea Indigenous cases 93 956 84 060 150 195 316 125 314 036 346 431 534 819 488 878 516 249 646 648 750 254 651 963 Total P. falciparum 56 735 59 153 58 747 119 469 120 641 118 452 183 686 163 160 174 818 181 463 189 397 181 927 Total P. vivax 13 171 9 654 7 108 7 579 78 846 62 228 95 328 113 561 138 006 163 237 186 981 172 964 Total mixed cases 4 089 1 164 1 279 79 574 115 157 197 711 200 186 201 658 299 869 372 257 296 710 Total other cases 1 990 632 0 0 2 125 1 950 1 772 1 433 1 767 2 079 1 619 362 Philippines Indigenous cases 19 102 9 583 8 086 7 720 6 019** 11 325** 6 625** 6 737** 4 559** 5 681**ˆ 6 094**ˆ 4 262** Total P. falciparum 11 824 6 877 4 774 4 968 3 760 4 769 5 282 3 258 1 310 5 034ˆ 5 234ˆ 3 347 Total P. vivax 2 885 2 380 2 189 1 357 834 755 816 551 116 537ˆ 736ˆ 703 Total mixed cases 214 166 83 235 195 388 83 22 80 88 185 Total other cases 175 127 0 0 74 87 139 40 47 48 37 27 Imported cases 68 85 55 69 82 95 26 35 Republic of Korea Indigenous cases 1 267* 505* 394* 383* 557* 627* 602* 436* 501* 485* 356* 274* Total P. falciparum 0 0 0 0 0 0 0 Total P. vivax 383 557 627 602 436 501 485 356 274 Total mixed cases 0 0 0 0 0 0 0 Total other cases 0 0 0 0 0 0 0 0 0 0 0 0 Imported cases 56 64 47 50 78 79 71 79 75 74 29 20 Total introduced cases 1 0 0 0 0 0 0 0 286 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – I. REPORTED MALARIA CASES BY SPECIES, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 SOUTH‑EAST ASIA Thailand Indigenous cases 32 480 24 897 46 895 41 602 41 218 14 265** 12 076** 7 416** 5 110** 4 065** 3 123** 2 426** Total P. falciparum 9 401 5 710 11 553 14 449 13 743 3 301 1 774 846 447 391 155 53 Total P. vivax 13 401 8 608 17 506 15 573 20 513 4 655 5 765 4 802 3 575 2 752 2 892 2 313 Total mixed cases 147 147 196 588 122 109 36 34 25 16 9 Total other cases 20 13 3 084 3 077 12 1 244 10 21 30 58 42 Imported cases 9 890 5 724 4 020 1 618 1 342 798 800 Timor-Leste Indigenous cases 48 139 19 735 5 208 1 025 342 80 81* 16* 0* 0* 3* 0* Total P. falciparum 36 239 14 261 1 962 373 118 33 46 4 0 0 2 0 Total P. vivax 11 432 3 754 2 288 512 139 24 7 3 0 0 0 0 Total mixed cases 468 1 720 958 140 85 23 28 9 0 0 1 0 Total other cases 0 0 0 0 0 0 0 Imported cases 10 13 7 9 7 0 Total introduced cases 2 0 1 0 4 0 WESTERN PACIFIC Cambodia Indigenous cases 96 464 106 905 69 551 44 069 69 178 68 109 43 380 76 804 62 582 32 196** 9 962** 4 382 Total P. falciparum 8 213 7 054 14 896 7 092 8 332 17 830 12 156 20 328 10 525 4 833 1 073 342 Total P. vivax 4 794 5 155 19 575 11 267 10 356 13 146 9 816 15 207 30 680 26 871 8 722 4 018 Total mixed cases 1 270 1 583 4 971 2 418 6 464 2 954 1 520 1 397 1 080 492 167 17 Total other cases 0 0 0 0 0 0 0 0 0 0 0 5 Imported cases 1 2 0 China1 Indigenous cases 4 990* 1 308* 244* 83* 53* 39* 1* 0* 0* 0* 0* Total P. falciparum 1 269 57 16 11 6 1 0 0 0 0 0 Total P. vivax 3 675 677 179 67 45 26 1 0 0 0 0 Total mixed cases 26 1 5 1 0 0 0 0 0 0 Total other cases 0 0 0 0 0 6 0 0 0 0 0 Imported cases 2 118 2 059 2 399 4 051 3 026 3 212 3 149 2 663 2 511 2 486 1 050 Lao People's Democratic Republic Indigenous cases 22 879 17 532 46 153 39 589 50 674 36 078 15 509 8 435 9 022****§ 6 691**** 3 489**** 3 897**** Total P. falciparum 22 452 16 556 37 685 25 441 25 317 14 439 5 737 4 169 4 828 2 168 1 573 1 339 Total P. vivax 403 962 7 594 13 067 23 763 20 815 9 441 4 104 4 099 4 444 1 879 2 536 Total mixed cases 24 0 873 1 079 1 593 823 329 162 111 79 37 22 Total other cases 0 10 0 2 0 0 0 0 2 0 0 0 Imported cases 0 0 4 28 Total introduced cases 16 1 5 1 Malaysia Indigenous cases 5 194* 3 954* 3 662* 1 028* 596* 242** 266* 85* 0* 0* 0* 0* Total P. falciparum 1 344 973 894 422 177 110 67 18 0 0 0 0 Total P. vivax 3 387 1 750 915 385 241 84 178 59 0 0 0 0 Total mixed cases 145 120 48 42 33 22 9 1 0 0 0 0 Total other cases 0 0 0 0 0 26 12 7 0 0 0 0 Imported cases 831 1 142 924 865 766 435 428 423 485 630 177 111 Total introduced cases 107 124 35 26 8 16 0 21 96 54 2 Papua New Guinea Indigenous cases 93 956 84 060 150 195 316 125 314 036 346 431 534 819 488 878 516 249 646 648 750 254 651 963 Total P. falciparum 56 735 59 153 58 747 119 469 120 641 118 452 183 686 163 160 174 818 181 463 189 397 181 927 Total P. vivax 13 171 9 654 7 108 7 579 78 846 62 228 95 328 113 561 138 006 163 237 186 981 172 964 Total mixed cases 4 089 1 164 1 279 79 574 115 157 197 711 200 186 201 658 299 869 372 257 296 710 Total other cases 1 990 632 0 0 2 125 1 950 1 772 1 433 1 767 2 079 1 619 362 Philippines Indigenous cases 19 102 9 583 8 086 7 720 6 019** 11 325** 6 625** 6 737** 4 559** 5 681**ˆ 6 094**ˆ 4 262** Total P. falciparum 11 824 6 877 4 774 4 968 3 760 4 769 5 282 3 258 1 310 5 034ˆ 5 234ˆ 3 347 Total P. vivax 2 885 2 380 2 189 1 357 834 755 816 551 116 537ˆ 736ˆ 703 Total mixed cases 214 166 83 235 195 388 83 22 80 88 185 Total other cases 175 127 0 0 74 87 139 40 47 48 37 27 Imported cases 68 85 55 69 82 95 26 35 Republic of Korea Indigenous cases 1 267* 505* 394* 383* 557* 627* 602* 436* 501* 485* 356* 274* Total P. falciparum 0 0 0 0 0 0 0 Total P. vivax 383 557 627 602 436 501 485 356 274 Total mixed cases 0 0 0 0 0 0 0 Total other cases 0 0 0 0 0 0 0 0 0 0 0 0 Imported cases 56 64 47 50 78 79 71 79 75 74 29 20 Total introduced cases 1 0 0 0 0 0 0 0 287 ANNEx 4 – I. REPORTED MALARIA CASES BY SPECIES, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 WESTERN PACIFIC Solomon Islands Indigenous cases 39 704 26 657 24 383 25 609 18 404 23 998 54 432 52 519 59 191 72 767 77 637 84 139 Total P. falciparum 22 892 14 454 14 748 13 194 9 835 10 478 16 607 15 400 15 771 15 595 14 753 22 057 Total P. vivax 12 281 8 665 9 339 11 628 7 845 12 150 33 060 30 169 35 072 47 164 52 039 57 095 Total mixed cases 200 83 232 446 724 1 370 4 719 6 917 8 341 9 979 10 813 4 943 Total other cases 0 0 0 0 0 0 46 33 7 27 32 44 Vanuatu Indigenous cases 9 817 6 179 4 532 2 883 1 314 571 2 243**** 1 227** 632** 567** 493**§ 312** Total P. falciparum 1 545 770 1 257 1 039 279 150 186 273 42 36 38 0 Total P. vivax 2 265 1 224 1 680 1 342 703 273 1 682 798 590 531 469 312 Total mixed cases 193 81 470 0 0 0 0 0 0 0 0 0 Total other cases 10 2 0 0 0 0 0 0 0 0 0 0 Imported cases 0 1 12 9 14 10 Total introduced cases 9 0 0 0 0 0 Viet Nam Indigenous cases 17 515 16 612 19 638 17 128 15 752 9 331 4 161 4 548 3 132*ˆ 3 200**§ 1 376* 377* Total P. falciparum 12 763 10 101 11 448 9 532 8 245 4 327 2 323 2 858 2 966ˆ 3 110 792 142 Total P. vivax 4 466 5 602 7 220 6 901 7 220 4 756 1 750 1 608 1 751ˆ 1 514 573 233 Total mixed cases 286 909 970 695 287 234 73 70 83 33 11 0 Total other cases 0 0 0 0 0 14 15 12 0 10 0 2 Imported cases 1 681 1 565 46 90 P.: Plasmodium; RDT: rapid diagnostic test; WHO: World Health Organization. * Reported indigenous cases. ** Indigenous cases = confirmed cases – imported cases. *** Unclassified cases are reclassified as indigenous cases. **** Indigenous cases = confirmed cases – imported and introduced cases. $ Zanzibar only. ˆ Data discrepancies between total indigenous cases and sum of species are due to the use of different data sources, differences in classification of mixed infections, failure to update species data following data audit or inability to adjust species for double counting of RDTs and microscopy. § No adjustment for imported and/or introduced cases due to incomplete information, data quality issues or use of different data sources for imported and introduced cases. # Species include introduced cases. Indigenous cases do not include non-human malaria cases or introduced cases. 1 Certified malaria free countries are included in this listing for historical purposes. 2 In May 2013, South Sudan was reassigned to the WHO African Region (WHA resolution 66.21, https://apps.who.int/gb/ebwha/pdf_files/ WHA66/A66_R21-en.pdf). Data as of 22 November 2022 288 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – I. REPORTED MALARIA CASES BY SPECIES, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 WESTERN PACIFIC Solomon Islands Indigenous cases 39 704 26 657 24 383 25 609 18 404 23 998 54 432 52 519 59 191 72 767 77 637 84 139 Total P. falciparum 22 892 14 454 14 748 13 194 9 835 10 478 16 607 15 400 15 771 15 595 14 753 22 057 Total P. vivax 12 281 8 665 9 339 11 628 7 845 12 150 33 060 30 169 35 072 47 164 52 039 57 095 Total mixed cases 200 83 232 446 724 1 370 4 719 6 917 8 341 9 979 10 813 4 943 Total other cases 0 0 0 0 0 0 46 33 7 27 32 44 Vanuatu Indigenous cases 9 817 6 179 4 532 2 883 1 314 571 2 243**** 1 227** 632** 567** 493**§ 312** Total P. falciparum 1 545 770 1 257 1 039 279 150 186 273 42 36 38 0 Total P. vivax 2 265 1 224 1 680 1 342 703 273 1 682 798 590 531 469 312 Total mixed cases 193 81 470 0 0 0 0 0 0 0 0 0 Total other cases 10 2 0 0 0 0 0 0 0 0 0 0 Imported cases 0 1 12 9 14 10 Total introduced cases 9 0 0 0 0 0 Viet Nam Indigenous cases 17 515 16 612 19 638 17 128 15 752 9 331 4 161 4 548 3 132*ˆ 3 200**§ 1 376* 377* Total P. falciparum 12 763 10 101 11 448 9 532 8 245 4 327 2 323 2 858 2 966ˆ 3 110 792 142 Total P. vivax 4 466 5 602 7 220 6 901 7 220 4 756 1 750 1 608 1 751ˆ 1 514 573 233 Total mixed cases 286 909 970 695 287 234 73 70 83 33 11 0 Total other cases 0 0 0 0 0 14 15 12 0 10 0 2 Imported cases 1 681 1 565 46 90 P.: Plasmodium; RDT: rapid diagnostic test; WHO: World Health Organization. * Reported indigenous cases. ** Indigenous cases = confirmed cases – imported cases. *** Unclassified cases are reclassified as indigenous cases. **** Indigenous cases = confirmed cases – imported and introduced cases. $ Zanzibar only. ˆ Data discrepancies between total indigenous cases and sum of species are due to the use of different data sources, differences in classification of mixed infections, failure to update species data following data audit or inability to adjust species for double counting of RDTs and microscopy. § No adjustment for imported and/or introduced cases due to incomplete information, data quality issues or use of different data sources for imported and introduced cases. # Species include introduced cases. Indigenous cases do not include non-human malaria cases or introduced cases. 1 Certified malaria free countries are included in this listing for historical purposes. 2 In May 2013, South Sudan was reassigned to the WHO African Region (WHA resolution 66.21, https://apps.who.int/gb/ebwha/pdf_files/ WHA66/A66_R21-en.pdf). Data as of 22 November 2022 289 ANNEx 4 – J. REPORTED MALARIA DEAThS, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 AFRICAN Algeria1 1 0 0 0 0 0 0 0 0 0 0 0 Angola 8114 6909 5736 7300 5714 7832 15997 13967 11814 18691 11757 13676 Benin 964 1753 2261 2288 1869 1416 1646 2182 2138 2589 2336 2956 Botswana 8 8 3 7 22 5 3 17 9 7 11 5 Burkina Faso 9024 7001 7963 6294 5632 5379 3974 4144 4294 1060 3983 4355 Burundi 2677 2233 2263 3411 2974 3799 5853 4414 2481 3316 2276 2292 Cabo Verde2 1 1 0 0 1 0 1 2 0 0 0 0 Cameroon 4536 3808 3209 4349 4398 3440 2639 3195 3256 4510 4121 3782 Central African Republic 526 858 1442 1026 635 1763 2668 3689 1292 2017 1779 2412 Chad 886 1220 1359 1881 1720 1572 1686 2088 1948 3374 2955 3065 Comoros 53 19 17 15 0 1 0 3 8 0 7 3 Congo 892 623 2870 271 435 733 229 131 107 99 252 Côte d'Ivoire 1023 1389 1534 3261 4069 2604 3340 3222 3133 1693 1316 1276 Democratic Republic of the Congo 23476 23748 21601 30918 25502 39054 33997 27458 18030 13072 18636 22729 Equatorial Guinea 30 52 77 66 28 109 15 Eritrea 27 12 30 6 15 12 21 8 5 3 3 6 Eswatini 8 1 3 4 4 5 3 20 2 3 2 5 Ethiopia 1581 936 1621 358 213 662 510 356 158 213 173 175 Gabon 182 74 134 273 159 309 101 218 591 314 224 244 Gambia 151 440 289 262 170 167 79 54 60 41 73 42 Ghana 3859 3259 2855 2506 2200 2137 1264 599 428 336 308 277 Guinea 735 743 979 108 1067 846 867 1174 1267 1881 1119 1117 Guinea-Bissau 296 472 370 418 357 477 191 296 244 288 Kenya 26017 713 785 360 472 15061 603 858 742 753 Liberia 1422 1725 1191 2288 1379 1259 758 602 248 Madagascar 427 398 552 641 551 841 443 370 927 657 674 547 Malawi 8206 6674 5516 3723 4490 3799 4000 3613 2967 2341 2517 2368 Mali 3006 2128 1894 1680 2309 1544 1344 1050 1001 1454 1698 1480 Mauritania 60 66 106 46 19 39 315 67 Mayotte Mozambique 3354 3086 2818 2941 3245 2467 1685 1114 968 734 563 408 Namibia 63 36 4 21 61 32 65 57 58 6 35 14 Niger 3929 2802 2825 2209 2691 2778 2226 2316 3576 4449 5849 4430 Nigeria 4238 3353 7734 7878 6082 9330 7397 8720 14936 26540 1811 7828 Rwanda 670 380 459 409 496 516 715 376 341 224 149 60 Sao Tome and Principe 14 19 7 11 0 0 1 1 0 0 0 1 Senegal 553 472 649 815 500 526 325 284 555 260 373 399 Sierra Leone 8188 3573 3611 4326 2848 1107 1345 1298 1949 2771 1648 2107 South Africa 83 54 72 105 174 110 34 301 69 79 38 56 South Sudan3 1053 406 1321 1311 3483 1191 4877 244 4220 Togo 1507 1314 1197 1361 1205 1127 847 995 905 1275 929 809 Uganda 8431 5958 6585 7277 5921 6100 5635 5111 3302 5027 4252 3158 United Republic of Tanzania 15915 11806 7828 8528 5373 6315 5046 3685 2753 1171 2569 1920 Mainland 15867 11799 7820 8526 5368 6313 5045 3684 2747 1163 2549 1916 Zanzibar 48 7 8 2 5 2 1 1 6 8 20 4 Zambia 4834 4540 3705 3548 3257 2389 1827 1425 1209 1339 1972 1503 Zimbabwe 255 451 351 352 406 200 351 527 192 266 400 131 290 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – J. REPORTED MALARIA DEAThS, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 AMERICAS Argentina1 0 0 0 0 0 0 0 0 0 Belize2 0 0 0 0 0 0 0 0 0 0 0 0 Bolivia (Plurinational State of)2 0 0 0 0 1 0 0 0 0 0 0 Brazil2 76 70 60 40 36 35 35 34 56 37 51 58 Colombia 42 23 24 10 17 18 36 19 9 3 5 11 Costa Rica2 0 0 0 0 0 0 0 0 0 0 0 0 Dominican Republic 15 10 8 5 4 3 1 1 1 4 2 1 Ecuador2 0 0 0 0 0 0 0 0 0 0 3 0 El Salvador1 0 0 0 0 0 0 0 0 0 0 0 French Guiana 1 2 2 3 0 0 0 0 0 0 0 Guatemala2 0 0 0 1 1 1 0 0 0 0 0 Guyana 24 36 35 14 11 12 13 11 6 15 13 16 Haiti 8 5 6 10 9 15 13 12 19 7 11 16 Honduras2 3 2 1 1 2 0 0 1 1 0 0 0 Mexico2 0 0 0 0 0 0 0 0 0 0 0 0 Nicaragua2 1 1 2 0 0 1 2 1 3 1 0 0 Panama2 1 0 1 0 0 0 0 0 0 0 0 0 Paraguay1 0 0 0 0 0 0 0 0 0 0 0 0 Peru 0 1 7 4 4 5 7 10 4 5 1 6 Suriname2 1 1 0 1 1 0 0 1 0 0 0 0 Venezuela (Bolivarian Republic of) 18 16 10 38 44 79 140 340 257 118 31 18 EASTERN MEDITERRANEAN Afghanistan2 22 40 36 24 32 49 47 10 1 0 0 0 Djibouti 0 0 0 17 28 23 5 0 19 Iran (Islamic Republic of)2 0 0 0 0 0 0 0 0 0 0 0 0 Pakistan2 4 260 244 56 34 33 113 102 0 80 0 Saudi Arabia2 0 0 0 0 0 0 0 0 0 0 0 0 Somalia2 6 5 10 23 14 27 13 20 31 20 5 0 Sudan 1023 612 618 685 823 868 698 1534 3129 1663 701 1679 Yemen 92 75 72 55 23 14 65 37 57 5 6 25 SOUTH‑EAST ASIA Bangladesh 37 36 11 15 45 9 17 13 7 9 9 9 Bhutan2 2 1 1 0 0 0 0 0 0 0 0 0 Democratic People's Republic of Korea2 0 0 0 0 0 0 0 0 0 0 0 0 India 1018 754 519 440 562 384 331 194 96 77 93 90 Indonesia 432 388 252 385 217 157 161 47 34 49 32 48 Myanmar 788 581 403 236 92 37 21 30 19 14 10 11 Nepal 6 2 0 0 0 0 3 0 0 0 0 1 Sri Lanka1 0 0 0 0 0 0 0 0 0 0 0 0 Thailand2 80 43 37 47 38 33 27 15 15 13 3 0 Timor-Leste2 58 16 6 3 1 0 0 0 0 0 0 0 291 ANNEx 4 – J. REPORTED MALARIA DEAThS, 2010–2021 WHO region Country/area 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 WESTERN PACIFIC Cambodia2 151 94 45 12 18 10 3 1 0 0 0 0 China1 19 33 0 0 0 0 0 0 0 0 0 Lao People's Democratic Republic2 24 17 44 28 4 2 1 2 6 0 0 1 Malaysia4 13 11 11 10 4 4 2 12 12 6 5 13 Papua New Guinea 616 523 381 307 203 163 306 273 216 180 188 201 Philippines 30 12 16 12 10 20 7 4 2 9 3 3 Republic of Korea2 1 2 0 0 0 0 0 0 0 0 0 0 Solomon Islands 34 19 18 18 23 13 20 27 7 14 3 9 Vanuatu2 1 1 0 0 0 0 0 0 0 0 0 0 Viet Nam2 21 14 8 6 6 3 3 6 1 0 0 0 REGIONAL SUMMARY African 150 383 104 057 104 113 116 354 99 380 127 603 111 145 102 886 88 188 108 460 77 641 91 109 Americas 190 167 156 127 130 169 247 430 356 190 117 126 Eastern Mediterranean 1 143 736 996 1 048 976 1 015 861 1 714 3 320 1 688 792 1 723 South-East Asia 2 421 1 821 1 229 1 126 955 620 560 299 171 162 147 159 Western Pacific 910 726 523 393 268 215 342 325 244 209 199 227 Total 155 047 107 507 107 017 119 048 101 709 129 622 113 155 105 654 92 279 110 709 78 896 93 344 Data as of 22 November 2022 E-2025: eliminating countries for 2025; P.: Plasmodium; WHO: World Health Organization. 1 Certified malaria free countries are included in this listing for historical purposes. 2 There were no indigenous malaria deaths in 2021. 3 In May 2013, South Sudan was reassigned to the WHO African Region (WHA resolution 66.21, https://apps.who.int/gb/ebwha/pdf_files/WHA66/ A66_R21-en.pdf). 4 All malaria deaths reported between 2018 and 2021 and 11 deaths in 2017 were due to infection with P. knowlesi. Notes: There have been no indigenous deaths reported in the WHO European Region since 2010. Reported indigenous deaths are shown for countries where 100% of cases have been investigated and classified, which may vary from year to year. The majority of these are E-2025 countries. For countries where case investigations are not carried out, all deaths are assumed to be indigenous. 292 W O RL D M AL AR IA R EP O RT 2 02 2 ANNEx 4 – K. METhODS FOR TABLES A-D-G-h-I-J Annex 4 – A. Policy adoption, 2021 Information on existing policies and whether they were implemented in 2021 was reported by national malaria programmes (NMPs). Policy implementation in 2021 was adjusted for the following variables, based on whether supporting data were available and reported by NMPs to the world malaria report database: distribution of insecticide-treated mosquito nets (ITNs) or long-lasting insecticidal nets (LLINs) through antenatal care (ANC), the Expanded Programme on Immunization or mass campaigns, indoor residual spraying (IRS), intermittent preventive treatment of malaria in pregnancy (IPTp), seasonal malaria chemoprevention (SMC), rapid diagnostic tests (RDTs) used at community level and artemisinin-based combination therapies (ACTs) used for the treatment of Plasmodium falciparum. IPTp is applicable in only 43 countries and SMC in only 34 countries; a setting of “not applicable” was automatically applied to countries where these interventions are not applicable. Annex 4 – D. Commodities distribution and coverage, 2019–2021 See notes for Fig. 7.1, Fig. 7.2, Fig. 7.3 and Fig. 7.6. Data sources for the number of malaria cases treated with ACTs were from NMP reports captured in the world malaria report database or, where such data were unavailable, from reports submitted to the Global Fund to Fight AIDS, Tuberculosis and Malaria (Global Fund). Missing data for ACT distributions or any first-line treatment courses delivered (including ACTs) were calculated based on the ratio of distributions to the number of patients treated from the previous year, multiplied by the number of patients treated in the current year. Where these data were not available, the number of patients treated was used as a proxy for distributions. In some countries, numbers of ACT distributions were used to replace missing information on any first-line treatment courses delivered (including ACTs). Annex 4 – G. Population denominator for case incidence and mortality rate, and reported malaria cases by place of care, 2021 Presumed and confirmed cases were reported by health sector (public, private and community). Where data could not be separated into health sectors they are shown for the public sector, indicating which data have been combined. Presumed cases were reported through outpatient registers. Confirmed cases were reported through a laboratory, unless indicated by the country that confirmed cases from the outpatient register should be used, owing to incomplete or inaccurate laboratory data. Confirmed cases were corrected for double counting of microscopy and RDT where the exact number of double counted cases was known. If the health sector where double counting occurred was not indicated, then data were combined, adjusted and displayed in the public sector. Annex 4 – H. Reported malaria cases by method of confirmation, 2010–2021 Presumed and confirmed cases were calculated based on the sum of confirmed cases from the laboratory (adjusted for double counting) or the outpatient register (see notes on Table G) and the presumed cases reported from the outpatient register. Confirmed cases include indigenous, imported, introduced, relapsing and recrudescent cases, as well as all species, including non-human malaria P. knowlesi. Between 2010 and 2018, suspected cases were calculated based on the formula “suspected = presumed + microscopy examined + RDT examined”, unless reported retrospectively by the country. From 2019 onwards, suspected cases were reported by countries. If data quality issues were detected, suspected cases were recalculated applying the formula used in 2010–2018. Suspected cases were adjusted for double counting of RDT and microscopy examined, if indicated by the country (e.g. if 100% of patients were examined by microscopy and RDT then suspected = presumed+RDT examined). Annex 4 – I. Reported malaria cases by species, 2010–2021 Indigenous cases and species were reported based on the following: total confirmed cases, where no case investigations and case classifications had been carried out; total confirmed cases minus imported and introduced cases, where data were available; and reported indigenous cases for countries that were part of the eliminating countries for 2025 (E-2025) initiative and where the number of cases classified equalled the total number of confirmed cases. P. knowlesi cases were excluded in all three approaches. For Costa Rica (2021) and South Africa (2018–2021) unclassified cases were reclassified as indigenous and added to the reported indigenous cases. Annex 4 – J. Reported malaria deaths, 2010–2021 All malaria deaths were reported except those in E-2025 countries and other countries where 100% of cases are investigated and classified – in which case, indigenous deaths are shown. The proportion of cases that are investigated and classified can vary from year to year. 293 Notes

For further information please contact: Global Malaria Programme World Health Organization 20, avenue Appia CH-1211 Geneva 27 Web: www.who.int/teams/global-malaria-programme Email: GMPinfo@who.int m a l a r i a a t l a s p r o j e c t 9789240064898 9 789240 064898