News Release

Climate models predict larger than expected decline in African malaria transmission areas

Peer-Reviewed Publication

American Association for the Advancement of Science (AAAS)

Areas at risk for malaria transmission in Africa may decline more than previously expected because of climate change in the 21st century, suggests an ensemble of environmental and hydrologic models. The combined models predicted that the total area of suitable malaria transmission will start to decline in Africa after 2025 through 2100, including in West Africa and as far east as South Sudan. The new study’s approach captures hydrologic features that are typically missed with standard predictive models of malaria transmission, offering a more nuanced view that could inform malaria control efforts in a warming world. Most of the burden of malaria falls on people living in low- and middle-income countries in Africa, where health infrastructure is incomplete and malaria control programs have stalled over recent years. Because it is spread by mosquitoes, malaria is also one of the most prominent climate-sensitive diseases. For example, changes in rainfall could expand or restrict the geographic range of mosquitoes and the availability of standing water that they need to breed, particularly in Africa where the climate is already rapidly shifting. However, most attempts to predict the impact of climate change on malaria have only represented surface water using precipitation, ignoring other important hydrologic features such as river inflow. Instead of relying on one model, Mark Smith and colleagues apply an ensemble of global hydrological and climate models to predict malaria transmission in Africa on a continental scale. They incorporated hydrologic metrics such as surface runoff and evaporation, paying special focus to densely populated areas near large-scale river networks such as the Nile. Compared to precipitation-based models, the ensemble method predicted these changes in area will be more widespread and more sensitive to differing future scenarios of greenhouse gas emissions. “As [new] data sources become increasingly available, we will benefit from their explicit incorporation in projections of hydrological processes to explain physically realistic malaria transmission risk at scales that can inform national operational malaria control strategies,” Smith et al. conclude.

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