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A web-based platform for tracking disease cases, predicting outbreaks, and monitoring public health using AI and real-time data. It integrates human and animal health data (One Health approach) to enable early detection and coordinated response efforts.
OneHealth Grid is designed for clinics, veterinarians, and public health teams to easily log patient symptoms, classify potential diseases with AI, visualize outbreak trends, and receive instant alerts. It supports bulk data imports for efficiency and offers public access for demonstrations, while including robust authentication for production use.
Core Goals:
- Detect outbreaks early through AI predictions and rule-based monitoring.
- Provide secure, role-based access for different users (e.g., admins, clinics, vets).
- Enable scalable data management with real-time updates and export options.
This platform promotes faster decision-making in public health by combining technology with practical workflows.
- Case Management: Log individual cases or bulk upload via CSV files, with search, filtering, and export capabilities.
- AI Disease Prediction: Uses a machine learning model to analyze symptoms and suggest disease categories with confidence scores (e.g., flu, COVID, or veterinary conditions).
- Real-Time Dashboard: Displays live statistics, trend charts, geographic maps, and lists of recent cases for quick overviews.
- Outbreak Detection and Alerts: Automatically identifies clusters of cases (e.g., multiple similar reports in a short time frame) and sends notifications to relevant users.
- Analytics and Reporting: Interactive visualizations for disease distributions, age demographics, case statuses, and custom reports with data exports.
- Bulk Data Uploads: User-friendly interface for dragging and dropping CSV files, including validation checks and progress indicators.
- User Authentication: Simple email/password registration and login, with role assignments for controlled access.
- Public Demo Mode: All dashboard and analytics pages are viewable without logging in, ideal for testing or presentations.
- Prototype Demo Login: Bypass authentication with a single click for rapid prototyping and demonstrations.
| Category | Technologies |
|---|---|
| Frontend | Next.js 14 (App Router), React.js, Material-UI (for responsive and accessible UI), Chart.js or Recharts (for visualizations). |
| Backend | Supabase (handles PostgreSQL database, user authentication, file storage, and real-time subscriptions). |
| AI/ML | Multinomial Naive Bayes classifier with TF-IDF text processing; uses NLTK for symptom data preprocessing. |
| File Handling | Pandas library for parsing and validating uploaded CSV files; integrates with Supabase Storage. |
| Deployment | Vercel for hosting the frontend; Supabase for managed backend services. |
| CI/CD & Tools | GitHub Actions for automated workflows; Docker for local development environments. |
| Monitoring | Supabase Dashboard for performance tracking; optional tools like Sentry for error logging. |
This stack ensures the platform is modern, scalable, and easy to maintain.
You'll need Node.js (version 18 or higher), a free Supabase account for the backend, and Git for version control.
- Clone the Repository: Download the project files from GitHub to your local machine using your preferred Git client.
- Install Dependencies: Open the project folder in your terminal or command prompt and install the required packages using your package manager (npm or Yarn).
- Configure Environment: Create a new file named
.env.localin the project root. Add your Supabase project URL and anonymous key (found in the Supabase Dashboard under Settings > API). Optionally, include keys for any external AI services if needed. - Database Setup: Log into your Supabase Dashboard and use the SQL Editor to create the necessary tables for cases, alerts, and outbreaks. Enable Row Level Security (RLS) policies to control data access based on user roles. Add indexes on key fields like symptoms for better performance.
- Seed Sample Data (Optional): Once the app is running, use the built-in seed endpoint or Supabase's import tools to populate the database with example cases for testing.
- Set up Demo User (For Prototype): Create a demo user account in your Supabase project with email
demo@example.comand passworddemopassword123. Run the demo user setup script atscripts/05-create-demo-user.sqlto assign the user to an organization. - Run the Application Locally: Start the development server via your package manager's dev script. The app will be available at http://localhost:3000 in your web browser.
- AI Model Setup: Predictions are handled through Supabase Edge Functions. For local testing, ensure any ML dependencies are integrated via the API routes.
This process should take about 15-30 minutes to get a working local instance.
The project is organized using Next.js conventions:
- App Directory: Contains all pages and layouts, including the home page for routing, authentication pages (login/signup), and dashboard sections (cases, analytics, uploads, etc.).
- API Routes: Dedicated folder for backend endpoints like case management, AI predictions, and bulk uploads, all integrated with Supabase.
- Components: Reusable UI elements such as dashboards, charts, modals, and forms.
- Lib Folder: Utilities for Supabase client setup, data validation, and helper functions.
- Public Folder: Static assets like icons and images.
- Scripts Folder: SQL scripts for database initialization and seeding.
- Root files include configuration for Next.js, package dependencies, and this README.
This structure keeps code modular and easy to navigate.
- Logging a Case: Visit the Cases page on the dashboard, fill out the form with patient details and symptoms, and submit for AI classification.
- Getting AI Predictions: Enter symptoms in the prediction form; the system analyzes them and returns the top disease matches with confidence levels.
- Bulk Uploading Data: Go to the Upload page, select a CSV file matching the sample format, and monitor the import progress. The app validates data before adding it to the database.
- Monitoring Alerts: On the Outbreaks page, view active alerts that update in real-time as new cases are reported. Mark them as read to clear notifications.
- Generating Reports: Use the Analytics or Reports page to filter data, view charts, and download CSV exports for further analysis.
- Demo Login: On the login page, click the "Demo Login (Bypass)" button to quickly access the dashboard without entering credentials. This is useful for rapid prototyping and demonstrations.
For public demos, simply navigate to any page without signing in.
| Endpoint | Method | Description | Authentication Required |
|---|---|---|---|
/api/cases |
GET/POST | Retrieve or create individual cases | Optional |
/api/predict |
POST | Analyze symptoms and return disease predictions | No |
/api/upload-cases |
POST | Process and store bulk CSV uploads | Optional |
/api/detect-outbreaks |
GET | Scan recent cases for potential outbreaks | No |
/api/alerts |
GET/POST | Fetch or send user notifications | Optional |
All endpoints connect seamlessly to Supabase for data handling.
- Local Database: Use the Supabase CLI to run a local instance for offline development and testing.
- Unit and Integration Tests: The project supports Jest for component and logic testing; run them via the package scripts.
- End-to-End Testing: Cypress is recommended for simulating user flows like logins, uploads, and dashboard interactions.
- Debugging Tips: Access the debug endpoint for logs and diagnostics. Check the browser console for real-time subscription issues, and verify RLS policies in the Supabase Dashboard for access errors. Common fixes include ensuring CSV formats match required headers (e.g., symptoms, location) and optimizing queries for large datasets.
Regular testing ensures reliability, especially for AI accuracy and data security.
- Frontend on Vercel: Connect your GitHub repository to Vercel for automatic builds and deployments on every push. Add environment variables in the Vercel Dashboard for Supabase integration.
- Backend with Supabase: The database, authentication, and storage are fully managed by Supabase—no separate server needed. Deploy any custom Edge Functions for advanced AI processing.
- Full Production Setup: For scalability, enable Supabase's auto-scaling features. Use Netlify as an alternative host if preferred over Vercel.
- Docker for Consistency: Package the app in Docker for local or custom cloud deployments, ensuring consistent environments across teams.
Deploying to production typically takes under an hour with these tools.
We welcome contributions to improve OneHealth Grid! To get started:
- Fork the repository on GitHub.
- Create a new branch for your feature or fix.
- Make your changes, focusing on clean code and adding tests where possible.
- Commit your updates with clear messages describing the work.
- Push the branch to your fork and open a Pull Request to the main repository.
Follow the project's style guidelines (ESLint and Prettier for formatting). For bugs or feature ideas, please open an Issue with details on your environment and steps to reproduce.
Your input helps advance public health technology—thank you!
This project is licensed under the MIT License. See the LICENSE file for full details, including permissions for use, modification, and distribution.
- Repository: github.com/yourusername/onehealth-grid – Fork, star, or watch for updates.
- Issues & Discussions: Use GitHub Issues for bug reports, questions, or suggestions.
- Email: your-email@example.com (for direct inquiries).
- Community: Join the conversation on GitHub Discussions or connect via LinkedIn for collaborations.
Stars, forks, and contributions are greatly appreciated! Let's work together to enhance global health monitoring. 🌍✨