KEMRI-inspired ML system for predicting malaria outbreak risk in Kenya
# 🦟 Malaria Outbreak Predictor
**A machine learning system for predicting malaria outbreak risk in Kenya — inspired by KEMRI's disease surveillance mandate.**
> *Malaria kills over 600,000 people annually, with 90% of deaths occurring in sub-Saharan Africa. Kenya alone reports ~5 million cases per year. Early prediction of outbreaks enables proactive resource allocation, potentially saving thousands of lives.*
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## 🎯 Project Overview
This project builds an end-to-end ML pipeline that predicts malaria outbreak risk using climate data, health indicators, and historical surveillance data — the same types of data that Kenya Medical Research Institute (KEMRI) uses for national disease surveillance.
### What It Does
| Capability | Description |
|-----------|-------------|
| **Outbreak Prediction** | Forecast malaria incidence 1-2 years ahead using climate and health covariates |
| **Climate Analysis** | Correlate rainfall, temperature, and humidity with malaria trends across 8 Kenyan cities |
| **Regional Comparison** | Benchmark Kenya against 8 East African countries |
| **Early Warning** | Flag anomalous conditions that historically precede malaria surges |
| **Interactive Dashboard** | Visualize predictions, trends, and risk factors in real-time |
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## 🔬 Why KEMRI?
The **Kenya Medical Research Institute (KEMRI)** is East Africa's premier health research institution. Established in 1979, KEMRI operates 10+ research centres including:
- **Centre for Infectious & Parasitic Diseases Control Research (CIPDCR)** — Malaria surveillance & control
- **Centre for Geographic Medicine Research-Coast (CGMR-C)** — Coastal malaria patterns
- **Centre for Public Health Research (CPHR)** — Disease outbreak prediction
- **ESACIPAC** — Cross-border parasite control across Eastern & Southern Africa
KEMRI was central to the development of the **RTS,S malaria vaccine** (Mosquirix), partnered with WHO, CDC, and the Wellcome Trust. Their work directly informed this project's d …