How to Build a Real-Time Global Disease Outbreak Tracker and Visualizer

Simple Summary

A real-time disease outbreak visualizer that provides interactive maps and data analytics to track and monitor global health threats, empowering users with timely information and insights.

Product Requirements Document (PRD)

Goals:

• Provide real-time visualization of disease outbreaks worldwide
• Offer interactive maps with detailed information on specific outbreaks
• Enable users to track multiple diseases and compare outbreak data
• Deliver timely notifications for new outbreaks or significant updates

Target Audience:

• Public health officials
• Researchers and epidemiologists
• Healthcare professionals
• Journalists and media organizations
• General public interested in global health

Key Features:

1. Interactive global map with disease outbreak markers
2. Detailed view of specific outbreaks with statistics and trends
3. Multi-disease tracking and comparison tools
4. Real-time data updates from reliable health organizations
5. Customizable alerts and notifications
6. Data export and sharing capabilities
7. Mobile-responsive design for on-the-go access

User Requirements:

• Intuitive interface for easy navigation and data exploration
• Accurate and up-to-date information from reputable sources
• Ability to filter and search for specific diseases or regions
• Customizable dashboard for personalized tracking
• Secure user accounts for saving preferences and receiving alerts

User Flows

1. New User Registration and Onboarding:

   • User visits the website and clicks "Sign Up"
   • Enters email, password, and basic profile information
   • Completes a brief onboarding process to select diseases of interest and regions to monitor
   • Lands on personalized dashboard with relevant outbreak information

2. Exploring Disease Outbreak Data:

   • User navigates to the global map view
   • Zooms in on a specific region or country
   • Clicks on outbreak markers to view detailed information
   • Toggles between different diseases or time periods
   • Uses filters to refine the displayed data

3. Setting Up Alerts and Notifications:

   • User accesses the notification settings page
   • Selects diseases, regions, or threshold criteria for alerts
   • Chooses preferred notification methods (email, push, in-app)
   • Saves settings and receives real-time updates based on preferences

Technical Specifications

Frontend:

• React for building the user interface
• Mapbox GL JS or Leaflet for interactive maps
• D3.js for advanced data visualizations
• Redux for state management
• Axios for API requests

Backend:

• Node.js with Express for the server
• PostgreSQL for the main database
• Redis for caching and real-time updates
• Bull for job queuing (data fetching and processing)

APIs and Services:

• WHO, CDC, and other health organization APIs for outbreak data
• Mapbox or Google Maps API for geocoding and map tiles
• SendGrid or Mailgun for email notifications
• Firebase Cloud Messaging for push notifications

DevOps:

• Docker for containerization
• GitHub Actions for CI/CD
• AWS or Google Cloud Platform for hosting

API Endpoints

• POST /api/auth/register
• POST /api/auth/login
• GET /api/outbreaks
• GET /api/outbreaks/:id
• GET /api/diseases
• POST /api/alerts
• GET /api/user/dashboard
• PUT /api/user/preferences
• GET /api/stats/global
• GET /api/stats/regional/:region

Database Schema

Users:

• id (PK)
• email
• password_hash
• name
• created_at
• last_login

Outbreaks:

• id (PK)
• disease_id (FK)
• location
• start_date
• status
• case_count
• death_count
• last_updated

Diseases:

• id (PK)
• name
• description
• symptoms

UserAlerts:

• id (PK)
• user_id (FK)
• disease_id (FK)
• region
• threshold
• notification_type

File Structure

Implementation Plan

1. Project Setup (1-2 days)

   • Initialize React app and Node.js server
   • Set up version control and project structure

2. Data Integration (3-4 days)

   • Implement data fetching from health organization APIs
   • Create data processing and storage scripts

3. Backend Development (5-7 days)

   • Develop API endpoints
   • Set up database and models
   • Implement authentication system

4. Frontend Basic Structure (3-4 days)

   • Create basic React components
   • Implement routing and state management

5. Map Integration (4-5 days)

   • Integrate map library
   • Develop map markers and interactivity

6. Data Visualization (5-6 days)

   • Create charts and graphs for outbreak data
   • Implement filtering and comparison features

7. User Features (3-4 days)

   • Develop user dashboard
   • Implement alert and notification system

8. Testing and Refinement (4-5 days)

   • Conduct thorough testing of all features
   • Optimize performance and fix bugs

9. Deployment Preparation (2-3 days)

   • Set up hosting environment
   • Configure CI/CD pipeline

10. Launch and Monitoring (Ongoing)

    • Deploy application
    • Monitor performance and user feedback
    • Iterate and improve based on usage data

Deployment Strategy

1. Containerize the application using Docker for consistency across environments
2. Use AWS Elastic Beanstalk or Google App Engine for easy scalability
3. Set up a managed PostgreSQL database (e.g., AWS RDS or Google Cloud SQL)
4. Implement Redis caching layer for improved performance
5. Use a content delivery network (CDN) for static assets
6. Set up automated backups and disaster recovery procedures
7. Implement logging and monitoring tools (e.g., ELK stack, Prometheus, Grafana)
8. Use blue-green deployment for zero-downtime updates
9. Implement auto-scaling based on traffic patterns
10. Regularly update and patch all systems for security

Design Rationale

The design focuses on creating a user-friendly, performant, and scalable application for visualizing disease outbreak data. React was chosen for its component-based architecture and large ecosystem, while Node.js provides a JavaScript-based backend for consistency. The use of interactive maps and data visualization libraries ensures an engaging user experience. Real-time updates and notifications keep users informed of critical changes. The modular architecture and use of containers allow for easy scaling and maintenance as the application grows. Security measures, including user authentication and data encryption, protect sensitive health information.