  Malaria continues to be a major public health problem on the African
continent, particularly in Sub-Saharan Africa. Nonetheless, efforts are
ongoing, and significant progress has been made. In Burundi, malaria is among
the main public health concerns. In the literature, there are limited
prediction models for Burundi. We know that such tools are much needed for
interventions design. In our study, we built machine-learning based models to
estimates malaria cases in Burundi. The forecast of malaria cases was carried
out 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 results
using climate-change related factors such as temperature, rainfal, and relative
humidity, together with malaria historical data and human population. With this
model, the results showed that at country level different tuning of parameters
can be used in order to determine the minimum and maximum expected malaria
cases. The univariate version of that model (LSTM) which learns from previous
dynamics of malaria cases give more precise estimates at province-level, but
both models have same trends overall at provnce-level and country-level
