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Dynamic spatiotemporal graph attention networks for cross-regional multi-disease forecasting and intervention
1Beijing University of Chinese Medicine, Beijing, China.
Frontiers in Public Health
|February 20, 2026
Summary
This study introduces a novel spatiotemporal graph attention network (ST-GAT) for improved infectious disease forecasting and intervention design. The framework enhances prediction accuracy and optimizes public health strategies for better cost-effectiveness and stability.
Area of Science:
- Epidemiology and Public Health
- Computational Biology and Bioinformatics
- Network Science
Background:
- Predicting infectious disease spread across regions is complex due to mobility, multiple pathogens, and spatiotemporal variations.
- Designing effective and economical public health interventions requires accurate forecasting and understanding of transmission pathways.
Purpose of the Study:
- To develop a unified framework for improved multi-disease forecasting and enhanced interpretability of transmission routes.
- To enable data-driven optimization of public health interventions, considering cost, fairness, and feasibility.
Main Methods:
- Developed a spatiotemporal graph attention network (ST-GAT) integrating diverse data sources (surveillance, meteorological, healthcare, NPIs) on a dynamic multi-relational graph.
- Utilized spatial and temporal attention mechanisms with a distribution-aware decoder for calibrated probabilistic forecasts (1-4 weeks).
- Embedded the model in a multi-objective optimization engine to evaluate intervention strategies like vaccine allocation and mobility restrictions.
Main Results:
- ST-GAT demonstrated significant reductions in Mean Absolute Error (MAE) compared to traditional models (ARIMAX, Prophet, LSTM/GRU) across multiple diseases (ILI, HFMD, dengue, RSV).
- Improved Weighted Interval Score (WIS) and Continuous Ranked Probability Score (CRPS) were observed, indicating better forecast calibration.
- Spatial attention identified key transmission corridors, temporal attention highlighted short-term lags (1-4 weeks), and optimization favored a vaccine-first strategy for cost-effectiveness.
Conclusions:
- The ST-GAT framework offers an integrated, interpretable, and generalizable solution for real-time epidemic prediction.
- The study provides a robust tool for equitable public health decision-making, optimizing interventions based on data-driven insights.
