🌍 AegisEpi

A high-fidelity, production-grade disease monitoring and risk prediction platform.
This system leverages Machine Learning to analyze historical outbreak data, weather patterns, and real-time reports to provide actionable insights for public health officials and the general public.

Unlike traditional systems, it focuses on detecting early weak signals — identifying outbreaks before they become obvious.

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📱 Mobile Implementation (Screenshots)

📊 Dashboard & Map

Dashboard	Risk Map

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📈 Analytics & Trends

Analytics

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❗ Problem Statement

Most current outbreak detection systems:

• Reactive, not Proactive: Depend on confirmed hospital data which is often delayed.
• Late Detection: Outbreaks are identified only after significant community spread.
• Data Silos: Lack of integration between environmental factors and clinical data.

👉 This platform bridges the gap by detecting early signals before they escalate into crises.

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💡 Our Approach

Instead of waiting for large spikes, the system analyzes "Silent Signals":

• Micro-Trends: Gradual increases in symptoms or cases over 7–14 days.
• Environmental Context: Real-time integration of humidity, rainfall, and temperature data.
• Pattern Recognition: Regional disease patterns compared against historical baselines.

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⚙️ Key Features

1️⃣ Real-time Risk Engine

• AI-powered predictions using Random Forest and Gradient Boosting models.
• Dynamic risk scoring (Low / Medium / High) updated as new data flows in.
• Explainable risk factors (Weather + Growth + Volume).

2️⃣ Geographic Intelligence

• Interactive maps with outbreak clusters and hyperlocal risk detection.
• Seamless navigation between global, regional, and area-specific data.

3️⃣ Advanced Analytics

• Time-series tracking for multiple pathogens (Dengue, Malaria, Flu, etc.).
• Comparative analysis of current trends against historical seasonal averages.

4️⃣ Data Management

• Secure CSV / Excel upload support for rapid data ingestion.
• Automated cleaning and normalization of clinical reports.

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🧠 Core Concept

"Detecting the invisible before it becomes inevitable."

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🛠 Technology Stack

📱 Frontend (Mobile)

• Framework: React Native (Expo)
• UI/UX: Premium Glassmorphic Design System
• Visualization: react-native-chart-kit, react-native-svg
• Mapping: react-native-maps

🌐 Web Dashboard

• Framework: React.js (Vite)
• Charts: Chart.js / Recharts
• Styling: Tailwind CSS / Modern CSS Variables

⚙️ Backend & API

• Runtime: Node.js / Express.js
• Database: MongoDB (Atlas)
• Architecture: RESTful API with automated geocoding services.

🧠 Machine Learning

• Language: Python
• Library: Scikit-learn
• Models: Random Forest Regressor, Gradient Boosting
• Serving: Flask / FastAPI microservice

🌍 External Services

• Weather Data: OpenWeather / WeatherStack API
• Geocoding: OpenCage / Nominatim API

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⚙️ System Flow

graph TD
    A[Clinical Data + Reports] --> B[Processing & Cleaning]
    C[Weather & Env Data] --> B
    B --> D[Trend Analysis last 30 Days]
    D --> E[Weak Signal Detection]
    E --> F[ML Risk Engine]
    F --> G[Insight Generation]
    G --> H[Visualization & Alerts]

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📁 Project Structure

├── Backend/           # Express server, API controllers, and DB services
├── mobile-app/        # React Native source code (iOS/Android)
├── ml/               # Python ML models and prediction engine
├── frontend/          # React.js web dashboard
└── screenshots/       # Visual documentation of the implementation

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🚀 Getting Started

1. Backend Setup

cd Backend
npm install
npm start

2. Mobile App Setup

cd mobile-app
npm install
npx expo start

3. ML Service Setup

cd ml
pip install -r requirements.txt
python server.py

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👥 The Team and contribution

• **Tejas Kulkarni-- -CSV Upload & Validation -Data Preprocessing & Cleaning -Input Handling & Error Checking -UI for Data Handling - Deployment and database handling

• **Yashashri Rajput-- - Environment Data Integration - API Development & Integration - UI for Data Visualization

• **Samruddhi Patil-- - Risk Calculation Logic - Outbreak Prediction & Scoring - UI for Analytics & Insights

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💡 Future Roadmap

• Real-time Integration: Direct hooks into hospital management systems.
• Push Alerts: Geofenced notifications for high-risk zones.
• Deep Learning: Implementing LSTM models for better time-series forecasting.
• Multi-Agent AI: Specialized agents for pandemic simulation.

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Developed as part of the AI-Based Disease Outbreak Prediction System Project.