Predicting malaria prevalence with machine learning models using satellite-based climate information: technical report
The current report presents a machine learning model developed to predict malaria prevalence based on rainfall patterns, specifically tailored to different regions within Senegal. The developed model takes into account the varying climate conditions across regions to provide a more localized and accurate prediction. The primary input parameters used for prediction include rainfall, month, and year, allowing the model to capture each region's seasonal variations and trends.