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

Abstract

Background: Metapopulation models provide platforms for understanding infectious disease dynamics and predicting clinical outcomes across interconnected populations, particularly for large epidemics and pandemics like COVID-19. 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, and commuting and mobility data, which are used to build and adjust the network and movement across locations on a daily basis. Findings: This 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. Interpretation: The metapopulation structure enables rapid simulation at 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.