Malaria-carrying mosquitoes across east Africa are becoming resistant to the chemical insecticides used in bednets and indoor spraying, and no one has a clear enough map of where and why this is happening. Insecticide-treated bednets and indoor spraying have cut malaria deaths dramatically this century—608,000 people still died in 2022, most in Sub-Saharan Africa—but widespread resistance now threatens those gains. New insecticides are being added to nets and sprays, but programme managers cannot deploy them strategically because they lack two things: a continent-wide picture of the genetic mechanisms driving resistance in different mosquito species, and the ability to predict how that resistance will affect malaria control in practice. This project will build statistical models that link genetic resistance data from across a large east African region to produce predictive maps of which resistance mechanisms are active where. The researcher will then feed those maps into computer models of malaria transmission in humans, simulating how different resistance profiles, mosquito species, and local conditions alter the effectiveness of control measures. The goal is to give district-level planners in east Africa customised recommendations for where to deploy which insecticide—turning scattered surveillance data into actionable strategy.
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Anopheles mosquitoes are the primary vectors of malaria, which caused 608,000 deaths in 2022, the majority occurring in Sub-Saharan Africa. Interventions involving chemical insecticides, including insecticide-treated bednets (ITNs) and indoor residual spraying (IRS), have achieved large reductions in malaria burden this century, and remain pivotal to malaria control. Unfortunately, insecticide resistance has become widespread in African vector populations and threatens the continued efficacy of insecticidal interventions. To mitigate the impacts of resistance, new insecticides are being incorporated into ITNs and IRS to target multiple resistance mechanisms. However, outstanding questions regarding the types and mechanisms of resistance present in field vector populations prevent strategic deployment of these interventions. Firstly, we lack comprehensive Africa-wide geospatial knowledge about the underlying genetic mechanisms of resistance present and how these vary regionally across individual vector species. Secondly, our capacity for predicting the impacts of resistance on malaria control efficacy is underdeveloped. I will develop novel geostatistical methods to link spatiotemporal data describing multiple genetic resistance mechanisms in Anopheles species to produce predictive maps of key genetic mechanisms across a large east African region. I will integrate these new results to develop computational models of malaria transmission dynamics in humans, to deliver geospatial predictions of malaria control efficacy accounting for differing resistance genetics, phenotypes and vector species composition. I will perform customised analyses representing local epidemiological settings across administrative planning districts in east Africa to identify optimal strategies for geographically-targeted interventions. This will support pan-African vector control policy objectives to incorporate local surveillance data into decision-making.
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