Cholera Prediction using Geospatial Data 🌍💧

This Streamlit application leverages Google Earth Engine (GEE) and machine learning to predict cholera outbreak risk zones in Nairobi, Kenya. The model is trained on historical cholera cases and environmental factors such as land use, population density, rainfall, and temperature.

🌟 Features

• Project Overview: Explains the motivation and data sources.
• Interactive Map: Uses geemap for spatial visualization.
• Cholera Risk Prediction: Machine learning model predicts high-risk areas.
• Geospatial Data Integration: Fetches data directly from Google Earth Engine.
• User Controls: Streamlit widgets for interactive exploration.
• Model Training: Trains a Random Forest/XGBoost model on geospatial data.
• Prediction Output: Generates risk maps based on trained model.

📂 Project Structure

streamlit-cholera-prediction/
├── pages/  # Additional pages
│   ├── project_info.py         # Overview of the project
│   ├── cholera_prediction.py   # Main application with interactive map
│   ├── about_author.py         # Information about the developer
├── app.py  # Main entry point
├── requirements.txt  # Dependencies
├── README.md  # Project documentation

🗺️ Data Sources

• Google Earth Engine Assets
  • Historical cholera cases
  • Population density (2019)
  • Land Use/Land Cover (LULC, 2019)
  • Rainfall & temperature (2020)
  • Dumping sites (potential contamination sources)
  • River buffer zones (proximity to water sources)

🔬 Model Training & Prediction

• Algorithm: Uses a supervised machine learning model (e.g., Random Forest, XGBoost).
• Feature Engineering: Extracts geospatial features for training.
• Training Pipeline: Processes data, extracts features, and trains the model.
• Prediction Output: Displays risk zones on an interactive map.
• Evaluation: Assesses model performance using accuracy metrics.

🛠️ Installation & Setup

1. Clone the repository:

   git clone https://github.com/KimutaiLawrence/streamlit-cholera-prediction.git
   cd streamlit-cholera-prediction

2. Create a virtual environment & install dependencies:

   python -m venv venv
   source venv/bin/activate  # On Windows, use: venv\Scripts\activate
   pip install -r requirements.txt

3. Run the Streamlit application:

   streamlit run app.py

📖 Usage

• Navigate to Project Info for background details.
• Use Cholera Prediction to visualize risk zones on the map.
• Visit About Author to learn about the developer.
• Train a new model using train_model.py if needed.
• Run predict.py to generate predictions from the trained model.

📝 Author

👤 Lawrence Kimutai
📧 Email: [email protected]
🔗 GitHub: KimutaiLawrence

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🚀 Let's predict and prevent cholera outbreaks using geospatial intelligence!