Malaria is a vector-borne disease, subject to climate change. The true impact of climate change on malaria dynamics is, however, still debated. Between 2008-2019, we studied patterns of malaria dynamics in a lowland area of western Kenya. We used wavelet analysis to assess the seasonality of monthly malaria incidence and related climatic factors, including air temperature, land surface temperature, rainfall and Nino 3.4 sea surface temperature. We performed a maximal overlap discrete wavelet transform to decompose incidence and climatic factors and fitted bivariate linear regressions to analyse their relationships across time scales. We observed a strong semestrial seasonality of malaria with the emergence of an annual cycle with variation strongly associated with rainfall dynamics. Rainfall emerged as a significant short-term predictor, while temperature contributed more at higher time scales. We found a recent increase in the time lag between climatic factors and their related effects on malaria incidence. This augmentation is related to bed net coverage and El Nino events. Our study underlines the importance of considering long-term time scales when assessing malaria dynamics. The presented wavelet approach could be applicable to other infectious diseases.

The authors have declared no competing interest.

Our work was conducted within the framework of the Research Unit project "Climate Change and Health in Sub-Saharan Africa" funded by the German Research Foundation (DFG/FOR 2936) and the Swiss National Science Foundation under the Weave Lead Agency scheme (SNSF 310030E_186574), with additional funding for disease surveillance studies from the United States Centers for Disease Control and Prevention.

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Ethics committee/IRB of the Kenya Medical Research Institute and the United States Centers for Disease Control and Prevention gave ethical approval for this work

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