EmmanuelOwusuAdu7583/disease-outbreak-predictor

An AI-powered Disease Outbreak Prediction System built with Python Flask and machine learning. Predicts outbreak risks across Ghana regions using linear regression, historical data, and environmental risk factor # Disease Outbreak Prediction System An AI-powered web-based disease outbreak prediction system built with Python Flask and machine learning. Analyzes historical disease data and environmental risk factors to predict outbreak risks across Ghana's regions — and now includes a community case reporting system that allows real users to submit live observations for ongoing analysis and research. ## Features ### Predictive Analytics - Linear regression based disease trend forecasting - 30-day outbreak predictions for 5 major diseases - Risk score calculation using multiple factors - Confidence scoring for all predictions ### Risk Assessment - Regional risk scores across all Ghana regions - Disease-specific risk analysis - Environmental risk factor integration (rainfall, temperature, healthcare access, sanitation) - Top 10 high-risk outbreak scenario identification ### Interactive Dashboard - Real-time disease trend visualization - Interactive 30-day forecasts switchable by disease - Regional risk score cards with visual indicators - Comprehensive risk table with trend indicators ### Community Case Reporting (New) - Public submission form at `/submit` allowing anyone to report observed disease cases - Collects reporter name, email, organization, region, disease, case count, date observed, and free-text notes - No password required for public users — low friction reporting designed to maximize participation - Automatically links repeat submissions from the same email to a single submitter profile ### Admin Review Dashboard (New) - Password-protected admin panel at `/admin` for reviewing all community submissions - Summary statistics: total submissions, unique submitters, total cases reported - Visual breakdown of reported cases by region and by disease - Full submissions table with submitter details and notes - One-click CSV export of all submissions for external analysis and reporting ### Diseases Monitored - Malaria - Cholera - Typhoid - Meningitis - COVID-19 ### …