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.

reports/baseline_mae.md

@@ -0,0 +1,23 @@
+# Baseline MAE Report
+
+## Naive Baseline: District-Level Historical Mean
+
+### Methodology
+- **Training period**: 2022-12-01 to 2023-06-30
+- **Validation period**: 2023-07-01 to 2024-12-30
+- **Prediction**: District-level historical mean risk score
+- **Risk score**: Weighted combination of outpatient (weight=1) and inpatient (weight=3) case counts, normalized by district mean
+
+### Results
+
+| Horizon | MAE |
+|---------|-----|
+| 1-day   | 0.2314 |
+| 3-day   | 0.5424 |
+| 7-day   | 0.6391 |
+
+### Interpretation
+- These MAE values represent the error of predicting the historical district mean
+- Model must achieve MAE < 0.9x these values to beat the naive baseline
+- 1-day horizon should have lowest MAE (most predictable)
+- 7-day horizon should have highest MAE (least predictable)