MeteoGST: Meteorology-Driven Spatiotemporal Graph Learning for Epidemic Influenza Forecasting

Abstract

Accurate influenza forecasting is essential for public health emergency preparedness and timely resource allocation. Although meteorological factors are established modulators of influenza transmission, existing deep-learning models rarely exploit this physical knowledge in a principled way. We introduce MeteoGST, a meteorology-driven spatiotemporal graph learning framework that combines (i) multi-scale feature extraction across seven operational meteorological variables (T_max, T_min, DTR, absolute humidity q, relative humidity RH, surface-pressure anomaly p_anom, and 10 m wind speed U10) via parallel dilated convolutions, TCN, and Transformer branches; (ii) a meteorology-aware dynamic graph attention network (MeteoGAT) whose edges blend geographic adjacency with time-varying meteorological similarity through a learned gate α; (iii) residual-trend decomposition with a peak-aware composite loss; and (iv) anti-smoothing meta-learning adaptation. On a 34-city pre-COVID-19 benchmark (2018–2019), MeteoGST achieves an RMSE of 0.65/0.85/1.12, MAE of 0.48/0.63/0.82, R2 of 0.84/0.76/0.70, and Peak F1 of 0.72/0.65/0.59 at 7-, 14-, and 30-day horizons, respectively—improvements of 4–6% over the strongest GNN baseline (MPNN-LSTM) and 31–44% over classical baselines (ARIMA/LSTM). Under a 2021–2022 distribution-shift stress test, the model retains its ranking at 1- and 4-week horizons (PCC 0.78/0.62) and degrades gracefully at 8 weeks, demonstrating robustness beyond the training distribution. MeteoGST offers an operationally deployable tool (MeteoGST-Lite: ≈1 ms per city-week on a laptop-class CPU) for integrated meteorology-aware influenza surveillance.