Computation for Epidemic Prediction with Graph Neural Network by Model Combination

Abstract

Modelling epidemic events such as COVID-19 cases in both time and space dimensions is an important but challenging task. Building on in-depth review and assessment of two popular graph neural network (GNN)-based regional epidemic forecasting models of \textbf{EpiGNN} and \textbf{ColaGNN}, we propose a novel hybrid graph neural network model, \textbf{EpiHybridGNN}, which integrates the strengths of both EpiGNN and \textbf{ColaGNN}. In the EpiGNN, through its transmission risk encoding module and Region-Aware Graph Learner (RAGL), both multi-scale convolutions and Graph Convolutional Networks (GCNs) are combined, aiming to effectively capture spatio-temporal propagation dynamics between regions and support the integration of external resources to enhance forecasting performance. While, in the ColaGNN, a cross-location attention mechanism, multi-scale dilated convolutions, and graph message passing are utilized to address the challenges of long-term forecasting through dynamic graph structures and spatio-temporal feature fusion. Both enjoy respective advantages but also share mutual shortcomings. Our EpiHybridGNN is therefore designed to combine the advantages of both EpiGNN, in its risk encoding and RAGL, and ColaGNN, in its long-term forecasting capabilities and dynamic attention mechanisms. This helps to form a more comprehensive and robust prediction of spatio-temporal epidemic propagation. The computational architecture, core formulas and their interpretations of our proposed EpiHybridGNN are provided. Multiple numerical real data experiments validate that our EpiHybridGNN significantly outperforms both EpiGNN and ColaGNN in epidemic forecasting with comprehensive insights and references offered.