joanjosh17/Flood-Risk-and-Waterborne-Disease-Outbreak-Analysis-in-Nigeria

Analyzes flood risk and waterborne disease outbreaks in Nigeria using synthetic public health data, machine learning, and environmental indicators to identify hotspots, predict outbreak risks, and support climate-health preparedness through data-driven insights # 🌊 Flood Risk and Waterborne Disease Outbreak Analysis in Nigeria > *Can flood-related environmental indicators help predict waterborne disease outbreaks in Nigeria?* --- ## 📌 Overview Flooding is one of the most devastating environmental disasters in Nigeria, frequently triggering outbreaks of waterborne diseases due to contaminated water sources, poor sanitation, and damaged health infrastructure. This project analyzes the relationship between **flood risk and waterborne disease outbreaks across Nigerian states and LGAs**, combining climate-related variables, public health indicators, and machine learning to identify flood-prone regions, disease hotspots, and predict outbreak risks. **Diseases studied:** - Cholera - Typhoid Fever - Diarrheal Diseases - Hepatitis A (conceptual inclusion) --- ## 🎯 Objectives - Analyze flood patterns across Nigerian states and LGAs - Investigate relationships between flooding and waterborne diseases - Identify high-risk outbreak regions - Explore environmental and healthcare factors affecting outbreaks - Predict disease outbreak risks using machine learning - Generate actionable insights for public health preparedness --- ## 🧠 Problem Statement Nigeria experiences frequent flooding — especially during rainy seasons — resulting in water contamination, poor sanitation, population displacement, and increased disease transmission. Despite this recurring challenge, early outbreak detection remains difficult. This project asks: **Can we use flood-related environmental data to predict waterborne disease outbreaks before they happen?** --- ## 📂 Dataset A **large realistic synthetic dataset** simulating Nigerian flood and health conditions across multiple states and LGAs, with 17,000+ rows of weekly observations. | Feature | Description | |---|---| | `date` | Observation date | | `state` | Nigerian state | | `lga` | Local government area | | `season` | Rainy / Dry season | | `rainfall_mm` | Rainfall amount | | `flood_risk_in …