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