Modelling COVID-19 in the North American region with a metapopulation network and Kalman filter.

ABSTRACT:

Background

Understanding 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.

Methods

We 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

SUBMITTER: Perini M 

PROVIDER: S-EPMC12020409 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature