Early warning of foodborne disease outbreaks using an enhanced graph attention network on patient association graphs

Foodborne diseases (FBDs) pose a significant threat to public health worldwide, with clustered outbreaks resulting in substantial economic and healthcare burdens. Conventional surveillance methods struggle to capture the multi-dimensional, complex relationships among cases, limiting early warning and targeted intervention. To address this gap, we propose a data-driven framework for FBD outbreak risk prediction by integrating patient association graphs and an improved graph attention network (GAT). First, we construct the patient association graph, where nodes represent individual FBD cases and edge weights are derived from the fusion of four key similarity dimensions, including spatiotemporal similarity (STS), food exposure similarity (FES), symptom similarity (SS), and demographic similarity (DS). Second, we introduce GatedResGAT, a novel graph attention network (GAT) enhanced with gated residual connections (GRC) and skip residual connections (SRC), to alleviate feature over-smoothing and gradient vanishing. Evaluated on real-world surveillance data from Wuxi, China, GatedResGAT achieves the area under the precision-recall curve (PR-AUC) of 0.7553, F1-Score of 0.7465, and Matthews Correlation Coefficient (MCC) of 0.7050, outperforming the best baseline model by 7.12%, 6.86%, and 12.64%, respectively. Ablation studies verify that FES and STS are the primary indicators for identifying case clustering, and both residual connections significantly enhance model performance. Early warning analysis of FBD outbreaks identifies high-risk transmission sub-networks with central outbreak nodes and potential contamination sources. These findings provide public health authorities with direct evidence for rapid source tracing and precise intervention