Multi-Regional Infectious Disease Transmission Forecasting Based on Graph-Structure-Enhanced Large Language Model

Abstract

Infectious disease outbreaks strain medical resources and public-health systems, making accurate multi-horizon, multi-regional forecasting essential for early warning and resource allocation. Existing approaches face a trade-off: data-driven deep models can capture nonlinear spatiotemporal patterns but often overlook transmission mechanisms, whereas mechanistic models are interpretable but limited in modeling complex regional dependencies. To address this challenge, we propose Epidemic Spatial–Temporal Large Language Model (EpiSTLLM), which is a graph-structure-enhanced large language model for multi-regional infectious disease forecasting. EpiSTLLM injects regional adjacency information into Transformer-based representation learning to capture cross-regional transmission structure and long-term temporal dependencies. A temporal-gated cross-attention module generates horizon-specific latent transmission and recovery parameters, while a latent-space SIR-inspired propagation mechanism with a residual correction branch enables stable multi-horizon forecasting without requiring fully observed compartmental states. Experiments on the FluView state-level influenza-like illness dataset and NHSN state-level influenza hospitalization dataset show that EpiSTLLM achieves the best or highly competitive performance in most evaluation settings against statistical, deep learning, graph-based, and mechanism-guided baselines across 4-, 8-, and 12-week horizons. For example, EpiSTLLM reduces MAE and RMSE values by 9.5% and 6.2% at H=4 on FluView, and by 12.0% and 13.1% at H=8 on NHSN compared with the strongest baselines, respectively.