frankraDIUM/Uganda-Multi-Disease-GeoAI-Early-Warning-System

A Climate-Intelligent Early Warning System for Vector-Borne and Waterborne Diseases in Uganda # 🦠 Uganda Multi-Disease GeoAI Early Warning System Research title: Climate-Intelligent GeoAI for Multi-Disease Early Warning: Integrating Climate Data, Spatiotemporal Machine Learning, and Outbreak Detection in Uganda - A Climate-Intelligent Early Warning System for Vector-Borne and Waterborne Diseases in Uganda **Summary** This project is a complete GeoAI-powered early warning system that predicts vector-borne (e.g., malaria) and waterborne (e.g., cholera) disease risk at the district level in Uganda. It integrates high-resolution climate data from Copernicus ERA5-Land, administrative boundaries, engineered spatiotemporal features, and machine learning models to generate risk predictions and outbreak alerts. The system is delivered through an interactive Streamlit dashboard with real-time forecasting and mapping capabilities. --- Dashboard Preview --- --- **Key Objectives Achieved** - Fuse climate (ERA5-Land) and spatial data (OSM + HDX boundaries) - Build separate predictive models for vector-borne and waterborne diseases - Develop an epidemiological outbreak detection engine - Create a production-grade interactive dashboard with AI recommendations - Simulate realistic early warning workflows (risk scoring, alerts, mapping) **Technical Architecture** Data Sources: - Climate: ERA5-Land Monthly Means (temperature, precipitation) - Spatial: Uganda ADM2 districts (135 districts) - Health: Advanced synthetic epidemiological simulation (population-adjusted, spatial spillover, rolling outbreak detection) Core Technologies: - Python, GeoPandas, xarray, rioxarray - Scikit-learn (RandomForestRegressor + RandomForestClassifier) - Streamlit + Plotly + Folium - Joblib model persistence - Modeling Performance (Test Set) - Vector-Borne Model: R² = 0.861, MAE = 9.12 - Waterborne Model: R² = 0.738, MAE = 5.06 - Outbreak Classifier: AUC = 0.936 **Key Features Engineered** - Temporal lags, rolling statistics, anomalies - Climate-disease interaction te …