Smart Health Surveillance and Early Warning System

A digital health platform for detecting, monitoring, and preventing outbreaks of water-borne diseases in vulnerable communities, particularly in the Northeastern Region (NER) of India.

Project Overview

This system addresses the challenge of water-borne diseases such as diarrhea, cholera, typhoid, and hepatitis A that are prevalent in many rural areas and tribal belts, especially during the monsoon season. The platform collects health data, analyzes patterns, monitors water quality, and provides alerts and educational resources.

Key Features

• Data Collection: Mobile app for health workers, ASHA workers, and community volunteers to report symptoms and cases
• AI-based Prediction: Machine learning models to detect patterns and predict potential outbreaks
• Water Quality Monitoring: Integration with water testing kits and IoT sensors
• Alert System: Real-time notifications to health authorities and local leaders
• Educational Resources: Multilingual content for hygiene awareness and disease prevention
• Offline Functionality: Support for areas with limited connectivity
• Multilingual Support: Interface in local and tribal languages
• Administrative Dashboard: Visualization tools for health departments to track hotspots and allocate resources

Project Structure

├── backend/            # Node.js Express backend API
│   ├── api/            # API routes and controllers
│   ├── models/         # Database models
│   ├── services/       # Business logic services
│   ├── utils/          # Utility functions
│   └── middleware/     # Express middleware
├── frontend/           # React.js web dashboard
│   ├── public/         # Static assets
│   └── src/            # React components and logic
├── mobile/             # React Native mobile application
│   ├── src/            # App source code
│   └── assets/         # App assets
├── ml/                 # Machine learning models and scripts
│   ├── models/         # Trained ML models
│   ├── data/           # Training and test datasets
│   └── notebooks/      # Jupyter notebooks for analysis
└── docs/               # Documentation

Technology Stack

Backend

• Node.js with Express
• MongoDB for database
• Socket.io for real-time communication
• JWT for authentication

Frontend

• React.js with Material-UI
• D3.js for data visualization
• Leaflet for maps

Mobile App

• React Native for cross-platform support
• Offline-first architecture
• Geolocation services

Machine Learning

• Python with TensorFlow/PyTorch
• Time-series analysis for outbreak prediction
• Anomaly detection algorithms

Getting Started

Prerequisites

• Node.js (v14 or higher)
• MongoDB
• Python 3.8+ (for ML components)
• React Native environment (for mobile app)

Backend Setup

1. Navigate to the backend directory:

   cd backend
   

2. Install dependencies:

   npm install
   

3. Create a .env file based on .env.example

4. Start the development server:

   npm run dev
   

Frontend Setup

1. Navigate to the frontend directory:

   cd frontend
   

2. Install dependencies:

   npm install
   

3. Start the development server:

   npm start
   

Mobile App Setup

1. Navigate to the mobile directory:

   cd mobile
   

2. Install dependencies:

   npm install
   

3. Start the Metro bundler:

   npx react-native start
   

4. Run on Android or iOS:

   npx react-native run-android
   # or
   npx react-native run-ios
   

ML Setup

1. Navigate to the ml directory:

   cd ml
   

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   

3. Install dependencies:

   pip install -r requirements.txt
   

Contributing

Please read CONTRIBUTING.md for details on our code of conduct and the process for submitting pull requests.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Ministry of Health and Family Welfare, Government of India
• ASHA workers and community health volunteers
• Local health departments in the Northeastern Region