In many parts of East Africa, small pools of water that form after heavy rainfall are ideal breeding sites for the Anopheles mosquitoes that transmit malaria. Researchers at the Karlsruhe Institute of Technology (KIT) have analyzed how such environmental conditions affect the effectiveness of mosquito nets. They combined high-resolution climate and hydrology models with malaria data from Kenya to enable better assessments of when and where the nets are especially effective at preventing infections. Their results have been published in Scientific Reports. (DOI: 10.1038/s41598-025-33539-w )
More than 600,000 people die of malaria in sub-Saharan Africa every year. How widespread the disease becomes depends not only on medical care and preventive measures but also on environmental factors such as rainfall, temperature, and especially the formation of temporary bodies of water. "Such pools of water determine where Anopheles mosquitoes breed and increase the risk of infections," said Professor Harald Kunstmann of the Institute of Meteorology and Climate Research Atmospheric Environmental Research (IMKIFU) at KIT's Campus Alpin in Garmisch-Partenkirchen. "Thanks to today's high-resolution environmental models, we know exactly when and where that occurs." With his team, Kunstmann has investigated whether and how such data can be used to maximize the effectiveness of countermeasures. "One of the simplest tools for fighting malaria is mosquito nets that protect people from mosquito bites at night," said Dr. Diarra Dieng of the IMKIFU, who was a major contributor to the project. "We wanted to find out how much they actually reduce transmission, and where their use has the greatest impact."
From Rain to Infection: a Modeling Chain
The researchers combined various model types for their study, with climate models providing temperature and precipitation data and hydrological simulations showing where water can accumulate to form potential breeding sites. Based on this data, an epidemiological model predicts the resulting spread of malaria. The analysis was based in part on malaria data from Kenya. "Our approach is the first to consider the entire chain, from atmospheric processes to the formation of breeding sites to disease transmission, enabling us to make the first experimental determination of how effective mosquito nets really are at reducing infections," Dieng said.
The researchers quantified the extent of changes in malaria transmission and incidence under different environmental conditions with and without mosquito nets. They were able to show that systematic use of mosquito nets significantly reduced the number of infectious insect bites, causing malaria incidence to decrease by around 40 percent on average, and in some regions by over 50 percent. They also showed the extent to which trends were influenced by local environmental factors. Temperature, precipitation, and the availability of temporary breeding sites determine when and where mosquitoes can breed most successfully, which in turn determines the effectiveness of preventive measures.
Planning Targeted Preventive Measures
The study shows how climate data can be used for practical healthcare decisions. High-resolution environmental data make it possible to assess malaria risk with much greater geographical precision and to estimate the expected benefits of preventive measures. Health programs could use this information to identify regions where targeted intervention would be especially effective and where additional measures might be needed. "For the first time, we have data that show what really helps," Dieng said. "If we understand the relationships between environmental conditions and preventive measures, we can put limited resources to better use."
Original publication
Mame Diarra Bousso Dieng, Stephan Munga, Adrian M. Tompkins, Miguel Garrido Zornoza, Cyril Caminade, Benjamin Fersch, Joël Arnault, Sammy Khagayi, Maximilian Schwarz, Simon Kariuki, Godfrey Bigogo, Harald Kunstmann: High resolution physically based modelling reveals malaria incidence reduction by vector control measures. Scientific Reports, 2025. DOI: 10.1038/s41598-025-33539-w
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