期刊
TRANSACTIONS OF THE ROYAL SOCIETY OF TROPICAL MEDICINE AND HYGIENE
卷 110, 期 2, 页码 107-117出版社
OXFORD UNIV PRESS
DOI: 10.1093/trstmh/trv113
关键词
Elimination; Malaria; Modelling; Operational research; Policy; Vector control
资金
- Bill & Melinda Gates Foundation [OPP1110495, OPP1119467, OPP1053338, OPP1081737, OPP1106023, OPP1093011, OPP1106427, 1032350]
- National Institutes of Health/National Institute of Allergy and Infectious Diseases [U19AI089674, P01AI098670]
- Leverhulme Centre for Integrative Research in Agriculture and Health
- Research and Policy for Infectious Disease Dynamics (RAPIDD) program of the Science and Technology Directorate, Department of Homeland Security
- Fogarty International Center, National Institutes of Health
- Foundation for the National Institutes of Health through Vector-Based Control of Transmission: Discovery Research program of the Grand Challenges in Global Health initiative
- Wellcome Trust [095066]
- Medical Research Council [MR/K00669X/1] Funding Source: researchfish
- MRC [MR/K00669X/1] Funding Source: UKRI
- Bill and Melinda Gates Foundation [OPP1081737] Funding Source: Bill and Melinda Gates Foundation
Background: Major gains have been made in reducing malaria transmission in many parts of the world, principally by scaling-up coverage with long-lasting insecticidal nets and indoor residual spraying. Historically, choice of vector control intervention has been largely guided by a parameter sensitivity analysis of George Macdonald's theory of vectorial capacity that suggested prioritizing methods that kill adult mosquitoes. While this advice has been highly successful for transmission suppression, there is a need to revisit these arguments as policymakers in certain areas consider which combinations of interventions are required to eliminate malaria. Methods and Results: Using analytical solutions to updated equations for vectorial capacity we build on previous work to show that, while adult killing methods can be highly effective under many circumstances, other vector control methods are frequently required to fill effective coverage gaps. These can arise due to pre-existing or developing mosquito physiological and behavioral refractoriness but also due to additive changes in the relative importance of different vector species for transmission. Furthermore, the optimal combination of interventions will depend on the operational constraints and costs associated with reaching high coverage levels with each intervention. Conclusions: Reaching specific policy goals, such as elimination, in defined contexts requires increasingly non-generic advice from modelling. Our results emphasize the importance of measuring baseline epidemiology, intervention coverage, vector ecology and program operational constraints in predicting expected outcomes with different combinations of interventions.
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