feat: Initial CBPOA commit — 武汉儿童呼吸疾病风险评估系统

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.

models/CLAUDE.md

@@ -0,0 +1,39 @@
+# 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
+
+```bash
+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.py` and `scripts/inference_*.py`
+- Don't load `best_model.pt` without matching the exact `SpatialTemporalGCN` constructor args
+- Don't skip ONNX export validation after architecture changes
+- Don't train without MLflow logging