Francis-11-xiya/climacare-ai-africa

AI-powered early warning system that predicts malaria risk using satellite data, climate variables, and machine learning to support public health decisions in Africa. ClimaCare AI — Malaria Early Warning System ClimaCare AI is a satellite-driven early warning system that uses machine learning to predict malaria risk in Africa using environmental and climate data. Problem Malaria remains a major public health issue in Africa, strongly influenced by environmental conditions such as rainfall, temperature, and vegetation. Existing systems are mostly reactive rather than predictive. Solution ClimaCare AI integrates: - Satellite data (CHIRPS, MODIS, ERA5) - Machine learning models - Climate-based feature engineering to generate malaria risk predictions and visual risk maps. Model - Algorithm: Random Forest Regressor - Features: Rainfall, NDVI, temperature - Output: Continuous malaria risk score - Performance: R² ≈ 0.63 (prototype) Output - Interactive risk map (Folium) - Climate risk classification (low / medium / high) - Early warning visualization system Data Sources All data used is publicly available: - CHIRPS (precipitation) - MODIS (vegetation index NDVI) - ERA5 (temperature proxies) Limitations - No real-time clinical hospital data - Prototype-level machine learning model - Requires further validation with epidemiological datasets Future Work - Integration with IoT sensors (ESP32) - Real-time alert system - Expansion to continental coverage - Health ministry decision support dashboard Author Francis Xiya(Francisco Muondo) Independent Youth AI Research Initiative