Mobility vs. Contiguity: Spatially Explicit Graph Neural Networks for COVID-19 Forecasting

Abstract

Assessing how different graph constructions affect spatio-temporal forecasting is essential for avoiding misleading predictions and ineffective interventions across domains such as epidemic control, transportation, and environmental monitoring. Graph neural networks (GNNs) offer a powerful framework for modelling these processes, but the choice of graph representation, whether based on geographic contiguity, transport networks, or mobility flows, remains underexplored. This study evaluates and compares five graph designs for forecasting daily COVID-19 infection rates per 100,000 residents in Glasgow and Edinburgh (2020–2023), including contiguity, road distance, mobility, and hybrids that integrate mobility with either contiguity or road distance. Using a 7-day sliding input window to predict the subsequent 7 days, and assessing performance via MAE, RMSE, MAPE, and R^2, we observe city-specific heterogeneity: mobility-informed hybrids achieve the best forecasts in Glasgow but not in Edinburgh. These findings suggest that the value of mobility is context-dependent, shaped by urban form and travel intensity. More broadly, the study points to the potential importance of tailoring graph construction to local conditions, offering methodological insights for epidemic modelling and other spatio-temporal forecasting applications.

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https://creativecommons.org/licenses/by/4.0/legalcode

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Identifiers

DOI

https://doi.org/10.5281/zenodo.17660772

Repository URI

https://eprints.gla.ac.uk/373603/