Climate variability and extremes impact on seasonal occurrence patterns of malaria cases in Senegal [Abstract only]

Jampani, Mahesh; Panjwani, Shweta; Ghosh, Surajit; Sambou, Mame Henriette Astou; Amarnath, Giriraj. 2023. Climate variability and extremes impact on seasonal occurrence patterns of malaria cases in Senegal [Abstract only]. Paper presented at the American Geophysical Union (AGU) Chapman Conference on Climate and Health for Africa, Washington, D. C., USA, 12-15 June 2023. 2p.

The increasing frequency of floods and droughts has compounding impacts on Malaria prevalence in West Africa, especially in Senegal. Malaria is a mosquito-borne viral disease and has detrimental impacts on health systems in the global south. Over the last decade, it was continuously reported a rising number of malaria cases year by year in Senegal. Many studies reported a strong correlation between climate variability and extremes and Malaria prevalence, but it is often tricky to evaluate the underlying causing factors. In this context, we analyzed and evaluated the monthly malaria cases with respect to climate variability and extremes over the last 12 years for all the provinces of Senegal. We emphasized our study to elucidate the seasonality of the occurrence of malaria cases and possible and probable underlying socio-economic factors combined with biophysical factors. We used satellite remote sensing datasets to extract various indicators related to rainfall, temperature, drought and flood. We performed integrated statistical analysis in combination with machine learning models (random forest, neural network, and bayesian hierarchical models) to evaluate and predict the probability of occurrence of malaria cases with respect to regional climate variability and extremes. Our initial results suggest that seasonality and accumulated rainfall play a critical role in Senegal for Malaria prevalence. The parabolic curve of malaria cases occurs between May to January, where September to November is the recorded high number of cases depending on the provinces that are located in different climate zones. Overall, our fine-tuned predictive modelling results aim to feed into an early warning platform to provide informed decisions to local policymakers, which can bestow insights into the seasonal occurrence of malaria prevalence for control and prevention measures.