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 was carried out at province level, allowing us to estimate malaria cases on a 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. The results showed that at country level different tuning of parameters can be used in order to determine the minimum and maximum expected malaria

翻译：疟疾仍然是非洲大陆，特别是撒哈拉以南非洲地区的主要公共卫生问题。尽管如此，相关努力持续进行，并已取得显著进展。在布隆迪，疟疾是主要公共卫生关切之一。文献中针对布隆迪的预测模型较为有限。我们深知此类工具对于干预方案设计至关重要。本研究构建了基于机器学习的模型以估算布隆迪的疟疾病例数。预测在省级层面开展，同时使我们能够估算全国范围的疟疾病例数。采用长短期记忆（LSTM）模型这一深度学习模型，结合温度、降雨量、相对湿度等气候变化相关因素，以及疟疾历史数据和人口数据，取得了最佳预测效果。结果显示，在国家层面可通过不同参数调优来确定预期疟疾病例的最小值和最大值。