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.
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scripts/alert_engine.py
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306
scripts/alert_engine.py
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#!/usr/bin/env python3
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"""
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Alert Engine for Wuhan Respiratory Disease Risk Prediction.
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Dual-path alert logic: Monitoring (medical z-scores) + Warning (model predictions)
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"""
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import os
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import sys
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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import warnings
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warnings.filterwarnings('ignore')
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import numpy as np
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import pandas as pd
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from pathlib import Path
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from datetime import datetime, timedelta
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import json
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PROCESSED_DIR = Path('processed')
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OUTPUT_DIR = Path('outputs/daily')
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OUTPUT_DIR.mkdir(exist_ok=True)
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class AlertLevel:
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"""Alert level enumeration with comparison support."""
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GREEN = 0
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YELLOW = 1
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ORANGE = 2
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RED = 3
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@classmethod
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def from_str(cls, s):
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return {'Green': cls.GREEN, 'Yellow': cls.YELLOW,
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'Orange': cls.ORANGE, 'Red': cls.RED}[s]
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@classmethod
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def to_str(cls, level):
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return {0: 'Green', 1: 'Yellow', 2: 'Orange', 3: 'Red'}[level]
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def compute_zscore(value, historical_mean, historical_std):
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"""Compute z-score; return 0 if std is 0."""
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if historical_std == 0 or np.isnan(historical_std):
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return 0.0
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return (value - historical_mean) / historical_std
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def evaluate_monitoring_alert(outpatient_cases, inpatient_cases,
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out_hist_mean, out_hist_std,
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inp_hist_mean, inp_hist_std):
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"""
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Evaluate monitoring alert based on medical data z-scores.
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Thresholds per PRD:
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- Yellow: outpatient z > 2.0
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- Orange: inpatient z > 2.5
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- Red: combined z > 3.0
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Returns:
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tuple: (AlertLevel, dict with z-scores)
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"""
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out_z = compute_zscore(outpatient_cases, out_hist_mean, out_hist_std)
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inp_z = compute_zscore(inpatient_cases, inp_hist_mean, inp_hist_std)
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combined_z = np.sqrt(out_z**2 + inp_z**2)
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if combined_z > 3.0:
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return AlertLevel.RED, {'out_z': out_z, 'inp_z': inp_z, 'combined_z': combined_z}
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elif inp_z > 2.5:
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return AlertLevel.ORANGE, {'out_z': out_z, 'inp_z': inp_z, 'combined_z': combined_z}
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elif out_z > 2.0:
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return AlertLevel.YELLOW, {'out_z': out_z, 'inp_z': inp_z, 'combined_z': combined_z}
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else:
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return AlertLevel.GREEN, {'out_z': out_z, 'inp_z': inp_z, 'combined_z': combined_z}
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def evaluate_warning_alert(risk_3d, risk_7d):
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"""
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Evaluate warning alert based on model predictions.
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Thresholds per PRD:
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- Orange: risk_3d > 0.6
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- Red: risk_7d > 0.7
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Returns:
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tuple: (AlertLevel, dict with risk values)
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"""
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if risk_7d > 0.7:
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return AlertLevel.RED, {'risk_3d': risk_3d, 'risk_7d': risk_7d}
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elif risk_3d > 0.6:
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return AlertLevel.ORANGE, {'risk_3d': risk_3d, 'risk_7d': risk_7d}
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else:
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return AlertLevel.GREEN, {'risk_3d': risk_3d, 'risk_7d': risk_7d}
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def resolve_alert(monitoring_level, warning_level):
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"""
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Conflict resolution: risk_level = GREATEST(monitoring, warning)
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Where Red > Orange > Yellow > Green
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"""
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return max(monitoring_level, warning_level)
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def generate_alerts(predictions_df, medical_df=None, date=None):
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"""
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Generate alerts with dual-path logic.
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Args:
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predictions_df: DataFrame with risk predictions (node_id, risk_1d, risk_3d, risk_7d, district)
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medical_df: Optional DataFrame with medical data (district, outpatient, inpatient)
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date: Date for alert generation
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Returns:
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list: Alert dictionaries
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"""
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if date is None:
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date = datetime.now().date()
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if isinstance(date, str):
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date = datetime.fromisoformat(date).date()
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alerts = []
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districts = predictions_df['district'].unique() if 'district' in predictions_df.columns else []
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for district in districts:
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district_preds = predictions_df[predictions_df['district'] == district]
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risk_1d = district_preds['risk_1d'].mean()
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risk_3d = district_preds['risk_3d'].mean()
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risk_7d = district_preds['risk_7d'].mean()
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# Warning path
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warn_level, warn_info = evaluate_warning_alert(risk_3d, risk_7d)
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# Monitoring path (if medical data provided)
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if medical_df is not None and district in medical_df['district'].values:
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med_row = medical_df[medical_df['district'] == district].iloc[0]
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mon_level, mon_info = evaluate_monitoring_alert(
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med_row.get('outpatient', 0),
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med_row.get('inpatient', 0),
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med_row.get('out_hist_mean', 0),
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med_row.get('out_hist_std', 1),
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med_row.get('inp_hist_mean', 0),
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med_row.get('inp_hist_std', 1)
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)
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else:
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mon_level = AlertLevel.GREEN
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mon_info = {'out_z': 0, 'inp_z': 0, 'combined_z': 0}
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# Resolve final level
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final_level = resolve_alert(mon_level, warn_level)
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# Determine alert type
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if mon_level > AlertLevel.GREEN and warn_level > AlertLevel.GREEN:
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alert_type = 'combined'
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elif mon_level > AlertLevel.GREEN:
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alert_type = 'monitoring'
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elif warn_level > AlertLevel.GREEN:
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alert_type = 'warning'
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else:
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continue # Skip green alerts
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# Build trigger description
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triggers = []
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if mon_level == AlertLevel.RED:
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triggers.append(f"combined z={mon_info['combined_z']:.2f}")
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elif mon_level == AlertLevel.ORANGE:
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triggers.append(f"inpatient z={mon_info['inp_z']:.2f}")
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elif mon_level == AlertLevel.YELLOW:
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triggers.append(f"outpatient z={mon_info['out_z']:.2f}")
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if warn_level == AlertLevel.RED:
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triggers.append(f"7d risk={risk_7d:.2f}")
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elif warn_level == AlertLevel.ORANGE:
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triggers.append(f"3d risk={risk_3d:.2f}")
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alert = {
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'alert_id': f"ALERT_{date.strftime('%Y%m%d')}_{datetime.now().strftime('%H%M%S')}",
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'alert_type': alert_type,
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'district': district,
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'risk_level': AlertLevel.to_str(final_level),
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'risk_1d': round(float(risk_1d), 4),
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'risk_3d': round(float(risk_3d), 4),
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'risk_7d': round(float(risk_7d), 4),
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'trigger': ' | '.join(triggers),
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'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
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}
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alerts.append(alert)
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return alerts
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def run_alert_engine(date=None, risk_geojson_path=None, medical_csv_path=None):
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"""
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Run alert engine for a specific date.
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Args:
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date: Date for alert generation
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risk_geojson_path: Path to risk GeoJSON file
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medical_csv_path: Optional path to medical data CSV
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"""
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if date is None:
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date = datetime.now().date()
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if isinstance(date, str):
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date = datetime.fromisoformat(date).date()
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date_str = date.strftime('%Y%m%d')
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print(f"\n=== Alert Engine: {date_str} ===")
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# Load risk predictions from GeoJSON
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if risk_geojson_path is None:
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risk_geojson_path = OUTPUT_DIR / f'risk_{date_str}.geojson'
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if not Path(risk_geojson_path).exists():
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print(f" Risk GeoJSON not found: {risk_geojson_path}")
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print(" Run inference_daily.py first")
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return []
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with open(risk_geojson_path) as f:
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geojson = json.load(f)
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# Convert GeoJSON to DataFrame
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predictions = []
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for feat in geojson['features']:
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props = feat['properties']
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predictions.append({
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'node_id': props['node_id'],
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'lat': props['lat'],
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'lon': props['lon'],
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'risk_1d': props['risk_1d'],
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'risk_3d': props['risk_3d'],
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'risk_7d': props['risk_7d'],
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'class_1d': props['class_1d'],
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'class_3d': props['class_3d'],
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'class_7d': props['class_7d'],
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'district': props.get('district', 'unknown')
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})
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predictions_df = pd.DataFrame(predictions)
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print(f" Loaded predictions: {len(predictions_df)} nodes")
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# Load medical data if available
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medical_df = None
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if medical_csv_path and Path(medical_csv_path).exists():
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medical_df = pd.read_csv(medical_csv_path)
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print(f" Loaded medical data: {len(medical_df)} districts")
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# Generate alerts
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alerts = generate_alerts(predictions_df, medical_df, date)
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print(f" Generated alerts: {len(alerts)}")
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# Save alerts
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if len(alerts) > 0:
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out_file = OUTPUT_DIR / f'alerts_{date_str}.json'
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with open(out_file, 'w') as f:
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json.dump(alerts, f, indent=2)
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print(f" Saved: {out_file}")
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# Print summary
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print("\n Alert Summary:")
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for alert in alerts:
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print(f" [{alert['risk_level']}] {alert['district']}: {alert['trigger']}")
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else:
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print(" No alerts generated")
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return alerts
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# --- Unit tests ---
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def test_alert_resolution():
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"""Unit test: simultaneous Yellow + Orange → result Orange."""
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# Yellow monitoring + Orange warning
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result = resolve_alert(AlertLevel.YELLOW, AlertLevel.ORANGE)
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assert result == AlertLevel.ORANGE, f"Expected ORANGE, got {AlertLevel.to_str(result)}"
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# Red monitoring + Yellow warning
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result = resolve_alert(AlertLevel.RED, AlertLevel.YELLOW)
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assert result == AlertLevel.RED, f"Expected RED, got {AlertLevel.to_str(result)}"
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# Green monitoring + Red warning
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result = resolve_alert(AlertLevel.GREEN, AlertLevel.RED)
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assert result == AlertLevel.RED, f"Expected RED, got {AlertLevel.to_str(result)}"
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# Both Yellow
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result = resolve_alert(AlertLevel.YELLOW, AlertLevel.YELLOW)
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assert result == AlertLevel.YELLOW, f"Expected YELLOW, got {AlertLevel.to_str(result)}"
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# Both Green
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result = resolve_alert(AlertLevel.GREEN, AlertLevel.GREEN)
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assert result == AlertLevel.GREEN, f"Expected GREEN, got {AlertLevel.to_str(result)}"
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print("All unit tests passed!")
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if __name__ == '__main__':
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import argparse
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parser = argparse.ArgumentParser(description='Alert engine for respiratory disease risk')
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parser.add_argument('--date', type=str, default=None, help='Date YYYY-MM-DD')
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parser.add_argument('--risk-geojson', type=str, default=None, help='Path to risk GeoJSON')
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parser.add_argument('--medical', type=str, default=None, help='Path to medical CSV')
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parser.add_argument('--test', action='store_true', help='Run unit tests')
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args = parser.parse_args()
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if args.test:
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test_alert_resolution()
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else:
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date = datetime.fromisoformat(args.date) if args.date else datetime.now()
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run_alert_engine(date, args.risk_geojson, args.medical)
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