Multi-event dynamic capture-recapture model for big data: Estimating undetected COVID-19 cases in British Columbia, Canada.

ABSTRACT: The accurate quantification of the impact of COVID-19 pandemic on both public health and the economy is essential for informed policy-making. However, the true scope of the pandemic remains challenging to ascertain due to undetected cases, particularly when relying on reported cases, which rely heavily on test availability and strategies. To accurately quantify COVID-19 cases in British Columbia (BC), we develop a Susceptible-Infectious-Recovered multi-event capture-recapture (SIRMECR) model to capture the dynamics of COVID-19. Specifically, we present a time-varying Markov model to estimate the number of undetected COVID-19 cases in five Health Authority Regions in BC, Canada, during the year 2020. We utilize individual-level information available from Population Data BC database to estim

SUBMITTER: Olobatuyi K 

PROVIDER: S-EPMC12819040 | biostudies-literature | 2026 Jun

REPOSITORIES: biostudies-literature