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Malaria continues to be a major public health problem on the Africancontinent, particularly in Sub-Saharan Africa. Nonetheless, efforts areongoing, and significant progress has been made. In Burundi, malaria is amongthe main public health concerns. In the literature, there are limitedprediction models for Burundi. We know that such tools are much needed forinterventions design. In our study, we built machine-learning based models toestimates malaria cases in Burundi. The forecast of malaria cases was carriedout at province level and national scale as well. Long short term memory (LSTM)model, a type of deep learning model has been used to achieve best resultsusing climate-change related factors such as temperature, rainfal, and relativehumidity, together with malaria historical data and human population. With thismodel, the results showed that at country level different tuning of parameterscan be used in order to determine the minimum and maximum expected malariacases. The univariate version of that model (LSTM) which learns from previousdynamics of malaria cases give more precise estimates at province-level, butboth models have same trends overall at provnce-level and country-level

Predicting malaria dynamics in Burundi using deep Learning Models