Akiba So
fc468464b2
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.
1.2 KiB
1.2 KiB
Models — SpatialTemporalGCN
Architecture
Spatiotemporal GCN for Wuhan respiratory disease risk prediction:
- Temporal: Transformer encoder (3 layers, 4 heads) over 14-day weather windows
- Spatial: 2-layer GCN (48→128→64) with elevation/population scaling
- Output:
[N, 3]risk probabilities (1-day, 3-day, 7-day horizons)
Files
models/spatiotemporal_gcn/
model.py # SpatialTemporalGCN class + ONNX export
sampler.py # Graph sampling utilities
best_model.pt # Trained weights (gitignored)
Input Shape
- Node features:
[N, T=14, 48]— N nodes, 14 timesteps, 48 weather features - Edge index:
[2, E]— sparse adjacency from 100m grid graph - Spatial scalars: elevation + population density per node
Training
python scripts/train_model.py # Full pipeline with MLflow tracking
Baseline MAE targets: 1-day=0.2314, 3-day=0.5424, 7-day=0.6391
Anti-Patterns
- Don't change model architecture without updating
scripts/train_model.pyandscripts/inference_*.py - Don't load
best_model.ptwithout matching the exactSpatialTemporalGCNconstructor args - Don't skip ONNX export validation after architecture changes
- Don't train without MLflow logging