Multilayer modular fusion graph attention network (MMF-GAT) for epidemic prediction

This study introduces the multilayer modular fusion graph attention network (MMF-GAT), an interpretable predictive framework that combines principles from network science and sociology to forecast infection risk. The model is based on the premise that infection risk is influenced by an individual’s overlapping membership across various social groups and their participation in shared activities that function as interaction hubs. To represent this complex social fabric, the framework formalizes a multilayer social graph that is assumed to be composed of personal contact, household, and community layers via fourth-order tensor formalism. The MMF-GAT architecture employs layer-specific graph attention network (GAT) modules to preserve context-specific modularity, combined with a late-fusion mechanism that represents how individuals connect different social settings. This approach operationalizes key sociological concepts by processing each layer independently before integration. When applied to a COVID-19 surveillance dataset of 2,264 individuals from Houston, Texas, the MMF-GAT model significantly outperformed five baseline models, achieving an accuracy of 0.78, an area under the curve (AUC) of 0.90, an F1 score of 0.72, and a precision‒recall area under the curve (PRAUC) of 0.89. Explainable AI (XAI) analysis identified structural features, particularly household degree, degree of personal contact, and affiliation with educational centers, as the most influential predictors. Owing to its robust predictive accuracy, the model effectively identifies high-risk individuals and settings, positioning it as a valuable tool for public health operations. This study makes a dual contribution. First, it advances multilayer network science through a novel computational architecture that preserves modularity. Second, it provides a validated, high-performance tool for public health informatics. This computational tool can support targeted interventions by optimizing contact tracing prioritization and resource allocation.