Predicting malaria dynamics in Burundi using deep Learning Models
This addresses the need for prediction tools to aid intervention design in Burundi, where malaria is a major public health concern, but it is incremental as it applies existing methods to a new dataset.
The study tackled predicting malaria cases in Burundi using deep learning models, specifically LSTM, with results showing that a univariate LSTM model provided more precise estimates at the province level, while both models captured similar trends overall.
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