EpiPredict Kenya AI is a disease surveillance platform designed for Kenyan healthcare organizations. It helps hospitals, pharmacies, and county health departments predict disease outbreaks 14-21 days in advance with high accuracy, enabling proactive public health responses.
# EpiPredict Kenya AI 🇰🇪
**AI-Powered Disease Outbreak Prediction for Kenya**
Demo • Features • Architecture • Quick Start • Kubernetes • Contributing
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## Why This Matters
**The Problem:** Healthcare response to disease outbreaks in Kenya is often **reactionary**. Hospitals and counties deal with outbreaks *after* they happen, leading to overwhelmed facilities, medication shortages, and preventable loss of life.
**The Pain Points:**
- 📉 **Delayed Data**: Paper records take weeks to aggregate.
- 🏥 **Overwhelmed Hospitals**: Sudden patient surges catch facilities off guard.
- 💊 **Supply Chain Gaps**: Pharmacies run out of critical meds during peak demand.
**The Solution:** EpiPredict Kenya AI flips the script from *reaction* to *prediction*. By analyzing patterns in real-time, we give decision-makers a **2-week head start** to mobilize resources, stock medicines, and warn communities.
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## Features
| Feature | Description |
|---------|-------------|
| 📊 **Real-time Dashboard** | Monitor disease trends across all 47 Kenyan counties |
| 🔮 **Predictive Analytics** | AI-powered outbreak predictions 14-21 days ahead |
| 🚨 **Smart Alerts** | Receive notifications when risk levels change |
| 🧠 **ML Prediction Engine** | Gaussian Naive Bayes classifier trained on epidemiological data |
| 🤖 **AI Chatbot (EpiBot)** | Context-aware LLM-powered health advisor |
| 🗺️ **County Mapping** | Visualize outbreak data geographically |
| 🐳 **Containerized** | Full Docker Compose & Kubernetes orchestration |
| 🌙 **Dark Mode** | Full dark mode support |
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## 🏗 Architecture
The application follows a **microservices architecture** with 4 independently deployable tiers:
```mermaid
graph TD
Browser["🌐 Browser"] -->|Port 80 / 30080| Frontend["📱 Frontend React + Nginx"]
Frontend -->|HTTP /api| Backend["⚙️ Backend FastAPI Gateway"]
Backend -->|SQLAlchemy| Database[("🐘 Database PostgreSQL 15")]
Backend -->|HTTP /predict, /train| MLService["🧠 ML Service FastAPI + sc …