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.
18 lines
332 B
Plaintext
18 lines
332 B
Plaintext
fastapi==0.109.0
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uvicorn[standard]==0.27.0
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pydantic==2.5.3
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pydantic-settings==2.1.0
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asyncpg==0.29.0
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asyncpg-stubs==0.29.0
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geoalchemy2==0.14.3
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shapely==2.0.2
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python-multipart==0.0.6
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python-jose[cryptography]==3.3.0
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passlib[bcrypt]==1.7.4
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python-dotenv==1.0.0
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scipy>=1.11.0
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pandas>=2.0.0
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numpy>=1.24.0
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pyarrow>=14.0.0
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openpyxl>=3.1.0
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