Mobility-informed metapopulation models predict the spatio-temporal spread of respiratory epidemics across scales

Abstract Understanding the spatiotemporal dynamics of infectious disease spread is critical for anticipating epidemic trajectories and guiding public health responses. Accurate forecasts of where and when outbreaks are likely to emerge can support efficient resource allocation, particularly during the early stages of epidemics when surveillance data are limited. In this study, we used empirical human mobility data derived from county-level commuting and air traffic flows, and a theoretical mobility model (the radiation model) to study the relative order of epidemic onset across spatial scales. These mobility models were incorporated into a metapopulation framework to predict the spread of three major respiratory pathogens: COVID-19, seasonal influenza, and respiratory syncytial virus (RSV). We applied this framework to county-level transmission within South Carolina and state-level introductions across the United States. In both empirical and theoretical mobility scenarios, we found that effective distance, a network-based measure of mobility-informed proximity, reliably predicts the relative timing of epidemic onset. These results demonstrate that mobility-informed metapopulation models can capture consistent spatiotemporal patterns across disease systems and spatial scales, even in the absence of detailed epidemiological parameters. This highlights their potential as scalable, data-efficient tools for outbreak forecasting and early public health planning. Competing Interest Statement The authors have declared no competing interest. Funding Statement This project was supported by the Center for Forecasting and Outbreak Analytics of the Centers for Disease Control and Prevention (CDC) under Award no. NU38FT000011 (AP, LZ, LR) and by the National Library of Medicine of the National Institutes of Health (NIH) under Award no. R01LM014193 (LR). Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethical review for this study was obtained by the Institutional Review Board of Clemson University (2020-0150). No consent was needed for this study. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability All data produced in the present study are available upon reasonable request to the authors.