noriegaking08-creator/Malawi-outbreak-predictor

AI and ML # Malawi Outbreak Predictor A Streamlit-based application to predict disease outbreaks (Malaria, Cholera, Monkeypox) in Malawi using LSTM and Prophet models, with visualizations, PDF reports, and email alerts and advisory reports. ## Table of Contents - Overview - Features - Installation - Project Structure - Dependencies - Configuration - Usage - Security - Development - License - Acknowledgments ## Overview The Malawi Outbreak Predictor is a comprehensive disease surveillance system that leverages machine learning to forecast potential disease outbreaks in Malawi. The application combines historical health data with climate metrics to generate accurate predictions, enabling proactive public health responses. ## Features - **User Authentication System**: Secure login and account creation with credential management - **Interactive Dashboard**: Real-time visualization of historical disease cases and climate data with overlay image - **Geospatial Mapping**: Interactive choropleth maps displaying predicted outbreak risk by district - **Advanced Predictions**: 4-week forecasts using both LSTM and Prophet models with configurable risk levels - **Export Functionality**: Generate and download detailed PDF reports of predictions - **Email Alert System**: Automated notifications for high-risk predictions - **Feedback Integration**: User feedback logging for continuous improvement - **Responsive Design**: Mobile-friendly interface optimized for field use ## Installation 1. **Clone the Repository**: ```bash git clone github.com cd malawi-outbreak-predictor ``` 2. **Create Virtual Environment**: ```bash python -m venv venv # On Windows: venv\Scripts\activate # On macOS/Linux: source venv/bin/activate ``` 3. **Install Dependencies** (ensure requirements.txt exists or create one): ```bash pip install streamlit pandas numpy scikit-learn tensorflow prophet folium plotly geopandas pillow ``` 4. **Set up Environment …