🏥 MedResilient

Geo-AI Tool for Climate-Resilient & Low-Carbon Medical Supply Routing

MedResilient is a comprehensive web-based dashboard that helps hospitals in Florida identify climate risks to their medical supply chain and automatically recommend low-carbon, flood-resilient routes from manufacturers or distributors to hospitals.

🎯 Features

Frontend (React + Tailwind + Google Maps)

• 🌎 Interactive Map: Display hospitals, manufacturers, and distributors with live flood risk overlay
• 📦 Order Dashboard: View and filter medical device orders by device type or provider
• ⚙️ Weighted Model Recommender: Re-rank suppliers based on customizable flood risk and carbon emission weights
• 🗺️ Route Visualization: Show delivery routes with color-coded flood risk indicators
• 📤 Data Upload Panel: Upload custom CSV files for hospitals, providers, or orders

Backend (Python + Flask)

• 🧾 Data API: Manage hospital orders, suppliers, and risk metrics
• 🌧️ Risk Data Integration: Combine Google Earth Engine precipitation data with FEMA flood zone data
• 🚗 Route Calculator: Calculate distances, travel times, and mode-based carbon emissions
• ⚖️ Weighted Scoring Engine: Rank suppliers using the formula: Score = α × FloodRisk + β × CarbonEmission

🏗️ Architecture

┌───────────────────────────┐
│        FRONTEND           │
│  React + Tailwind         │
│  • Map Visualization      │
│  • Dashboard & Analytics  │
│  • Route Optimization UI  │
└───────────┬───────────────┘
            │ REST API
            ▼
┌───────────────────────────┐
│         BACKEND           │
│  Python + Flask           │
│  • Data Management        │
│  • Risk Scoring Engine    │
│  • Route & Emission Calc  │
└───────────┬───────────────┘
            │
            ▼
┌───────────────────────────┐
│       EXTERNAL APIs       │
│  • Google Earth Engine    │
│  • FEMA Flood Zones       │
│  • Google Maps Directions │
└───────────────────────────┘

🚀 Quick Start

Prerequisites

• Python 3.9+
• Node.js 18+
• Google Earth Engine account
• API keys for FEMA, Google Earth Engine, and Google Maps

Backend Setup

1. Navigate to backend directory:

cd MedResilient/backend

2. Create virtual environment:

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

3. Install dependencies:

pip install -r requirements.txt

4. Configure environment (already set in .env file):

• FEMA_API_KEY
• FEMA_CLIENT_ID
• GEE_PROJECT_ID
• GOOGLE_MAPS_API_KEY

5. Authenticate Google Earth Engine (first time only):

earthengine authenticate

6. Run the backend:

python app.py

Backend will run on http://localhost:5000

Frontend Setup

1. Navigate to frontend directory:

cd MedResilient/frontend

2. Install dependencies:

npm install

3. Start development server:

npm run dev

Frontend will run on http://localhost:3000

📊 Data Structure

Hospitals CSV

hospital_id,name,address,city,state,zip,latitude,longitude
H001,Miami General Hospital,1611 NW 12th Ave,Miami,FL,33136,25.7907,-80.2108

Providers CSV

provider_id,name,type,address,city,state,zip,latitude,longitude,transport_mode
P001,MedEquip Florida,Distributor,1500 NW 89th Ct,Miami,FL,33172,25.7860,-80.3370,truck

Orders CSV

order_id,hospital_id,provider_id,device_name,quantity,order_date,delivery_date
O001,H001,P001,Ventilator,5,2024-01-15,2024-01-20

🔧 API Endpoints

Data Endpoints

• GET /api/hospitals - Get all hospitals
• GET /api/hospitals/:id - Get specific hospital
• GET /api/providers - Get all providers
• GET /api/providers/:id - Get specific provider
• GET /api/orders - Get orders (with optional filters)

Analysis Endpoints

• POST /api/recommendations - Get ranked supplier recommendations
• POST /api/analyze-provider - Analyze specific provider-hospital pair
• POST /api/flood-risk - Get flood risk for coordinates
• GET /api/mapdata - Get all map visualization data

Upload Endpoint

• POST /api/upload - Upload CSV files (hospitals, providers, orders)

📐 Scoring Model

The weighted scoring model ranks suppliers based on:

Score = α × FloodRisk + β × CarbonEmission

Where:

• FloodRisk ∈ [0, 1]: Combined FEMA flood zone + GEE precipitation/elevation data
• CarbonEmission: Distance (km) × EmissionFactor (kg CO₂/km)
• α (alpha): Weight for flood risk (default: 0.6)
• β (beta): Weight for carbon emission (default: 0.4)

Emission Factors (kg CO₂ per km)

• Truck: 0.21
• Van: 0.15
• Air: 1.13
• Rail: 0.03
• Ship: 0.01

🧪 Testing

Run backend tests:

cd backend
pytest tests/ -v

📝 Usage Guide

1. View Orders: See current medical supply orders in the dashboard
2. Select Hospital: Choose a hospital to analyze supplier options
3. Adjust Weights: Use sliders to prioritize flood risk vs. carbon emissions
4. View Recommendations: See ranked suppliers with risk and emission metrics
5. Visualize Routes: Click recommendations to see routes on the map
6. Upload Data: Add custom hospital, provider, or order data via CSV upload

🌟 Key Technologies

Frontend:

• React 18
• TypeScript
• Tailwind CSS
• Zustand (state management)
• Google Maps JavaScript API
• Axios

Backend:

• Flask
• Pandas
• Google Earth Engine Python API
• Google Maps Python Client
• Python-dotenv

📄 License

MIT License

🤝 Contributing

Contributions welcome! Please feel free to submit a Pull Request.

📧 Contact

For questions or support, please open an issue on GitHub.

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Built for rapid disaster response and climate-resilient healthcare supply chains.