A Web-Based Application for Real-Time Malaria Prediction using Environmental Variables

Malaria remains a persistent public health challenge in Zimbabwe, particularly in rural districts such as Mudzi in Mashonaland East Province, where seasonal transmission and limited healthcare access undermine conventional control measures. This study presents the development and deployment of MalariaDash, a web-based application designed for real-time forecasting of malaria case counts using satellite-derived environmental data. The system integrates remote sensing inputs such as land surface temperature, vegetation indices, elevation, and proximity to water bodies with a Random Forest regression model trained on twenty-one months of health facility data. Predictor variables were selected based on their statistical significance in earlier modelling efforts. Unlike existing studies that focus on retrospective risk mapping or static models, MalariaDash operationalises machine learning outputs into an interactive platform. The application dynamically retrieves environmental data through Sentinel Hub and weather APIs, enabling location-specific and time-specific malaria predictions. It is implemented using the Django web framework and provides a user interface where environmental conditions and predicted malaria incidence can be viewed for selected dates and areas. This research demonstrates the practical value of linking environmental intelligence with predictive analytics in a rural African context. By delivering spatially explicit and near-real-time forecasts, MalariaDash enables health authorities to adopt proactive, targeted interventions rather than reactive responses. The approach illustrates a novel integration of geospatial modelling, machine learning, and operational web deployment aimed at improving local malaria control strategies.