ArticleEpidemics2025
Modelling COVID-19 in the North American region with a metapopulation network and Kalman filter.
Article in Epidemics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Networked SIRS model with Kalman filter state estimation for epidemic monitoring in Europe.Communications medicine · 2026Article
- Real-time spatiotemporal tracking of infectious outbreaks in confined environments with a host-pathogen agent-based system.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Data assimilation for estimating time-varying reproduction numbers.Journal of the Royal Society, Interface · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundUnderstanding the dynamics of infectious disease spread and predicting clinical outcomes are critical for managing large-scale epidemics and pandemics, such as COVID-19. Effective modeling of disease transmission in interconnected populations helps inform public health responses and interventions across regions.
methodsWe developed a novel metapopulation model for simulating respiratory virus transmission in the North America region, specifically for the 96 states, provinces, and territories of Canada, Mexico, and the United States. The model is informed by COVID-19 case data, which are assimilated using the Ensemble Adjustment Kalman filter (EAKF), a Bayesian inference algorithm. Additionally, commuting and mobility data are used to build and adjust the network and movement across locations on a daily basis.
resultsThis model-inference system provides estimates of transmission dynamics, infection rates, and ascertainment rates for each of the 96 locations from January 2020 to March 2021. The results highlight differences in disease dynamics and ascertainment among the three countries.
conclusionsThe metapopulation structure enables rapid simulation at a large scale, and the data assimilation method makes the system responsive to changes in system dynamics. This model can serve as a versatile platform for modeling other infectious diseases across the North American region.
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