Context: Build a spatial risk assessment system correlating air quality
data with children's respiratory disease incidence across Wuhan.
Approach: FastAPI backend serving PostGIS spatial queries, React
frontend with Deck.gl maps, and a PyTorch SpatialTemporalGCN pipeline
for multi-day (1d/3d/7d) risk prediction.
Changes:
- backend/ — FastAPI API with auth (JWT), alerts, risk analysis,
geocoded case data, grid statistics, and report endpoints
- frontend/ — React dashboard with interactive risk maps, alert
monitoring, district comparison charts, and timeline player
- models/ — SpatialTemporalGCN model with trained weights and ONNX
export for inference
- scripts/ — ETL pipeline for weather + medical data, grid generation,
feature engineering, training, and daily inference
- deploy/ — Docker Compose configs for backend, frontend, and MLflow
- docs/ — API docs, deployment guide, user guide, and code review
Impact: Enables spatial risk visualization, alert monitoring, and
ML-driven health risk forecasting for environmental health teams.
