Dynamic spatiotemporal graph attention networks for cross-regional multi-disease forecasting and intervention optimization

Introduction: Accurately predicting cross-regional spread of infectious diseases and designing cost-effective interventions is challenging due to population mobility, multi-pathogen circulation, and spatiotemporal heterogeneity. This study aims to build a unified framework that improves multi-disease forecasting, enhances interpretability of transmission pathways, and enables data-driven optimization of public-health interventions. Methods: We develop a spatiotemporal graph attention network (ST-GAT) that integrates surveillance, meteorological, healthcare, and NPI data on a dynamic multi-relational graph combining geographic adjacency and origin-destination mobility. Spatial and temporal attention with a distribution-aware NB/ZINB decoder generates calibrated 1-4-week probabilistic forecasts, and the model is embedded in a multi-objective optimization engine to evaluate vaccine allocation and mobility restriction strategies under cost, fairness, and feasibility constraints. Results: Using ILI, HFMD, dengue, and RSV data, ST-GAT reduces MAE (34% vs ARIMAX, 27% vs Prophet, 15% vs LSTM/GRU) and improves WIS/CRPS across diseases. Spatial attention identifies high-weight transmission corridors, temporal attention highlights short lags of 1-4 weeks, and optimization shows a vaccine-first strategy achieves the best cost-effectiveness and stability. Discussion: The framework provides an integrated, interpretable, and generalizable solution for real-time epidemic prediction and equitable public-health decision-making.