Malaria has been falling across East Africa since 2000, but the decline began before large-scale mosquito net and drug campaigns were rolled out, and the pace of change varies sharply from one district to the next. No one has systematically untangled why. Climate, population growth, economic development, and intervention coverage have all shifted over the past 40 years, but their relative contributions to malaria’s rise and fall remain unknown. Without that understanding, planners cannot build a credible business case for where to invest limited resources—or how to sustain gains as climate and economies continue to change. This project will assemble data from 261 spatial units across East Africa between 1970 and 2010, then test competing explanations against real-world malaria data collected through 2020. The result will be a framework that links malaria risk directly to economic and climate vulnerability. If it succeeds, national health ministries and international funders will have an evidence-based tool for targeting malaria control—not just to the right places, but to the right mix of interventions. That could shift how billions of pounds in global health investment are allocated, and clarify malaria’s role in the UN’s sustainable development goals for Africa.
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The intensity of malaria transmission over the last 40 years in East Africa has been subject to significant change, involving complex combinations of short term and longer term cycles. Since 2000 there has been a general decline in malaria transmission intensity, however, declines began before scaled intervention coverage and the timing and rates of decline show demonstrable differences between areas within the sub-region. There has been no systematic analysis of the relative contributions of cl imate, population settlement, economic development and changing intervention coverage to the rise and fall of malaria across the sub-region. Planning the future of malaria control demands a carefully articulated, evidence-driven business case for sustained, targeted investment, linked to a framework that understands the relationships between malaria risk and economic and climate vulnerability. I will use new data on malaria risk and revised assemblies of environmental, social, population and int ervention defined determinants of risk at 261 spatial units between 1970 and 2010; analyzed within a plausibility framework against changing temporal counterfactual(s) of malaria risk using novel spatial and temporal epidemiological methods. Plausible hypotheses of the effect sizes of changing determinants of malaria risk will then be tested prospectively against more robust community, school and clinic based malaria data through to 2020 to explore the future epidemiological transition in the su b-region. This work will feed directly into national development and malaria-specific strategic planning and provide a broader understanding of malaria's role in proposed UN goals for a sustainable economic future for Africa.
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