reports/baseline_mae.md

# 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)
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. 2026-06-05 02:13:49 +08:00			`# 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)`