A0954
Title: Multievent dynamic capture-recapture model: Estimating undetected COVID-19 cases in British Columbia, Canada
Authors: Kehinde Olobatuyi - University of Victoria (Canada) [presenting]
Patrick Brown - University of Toronto (Canada)
Laura Cowen - ()
Abstract: The accurate quantification of the impact of the 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), a Susceptible-Infectious-Recovered-multi-event capture-recapture (SIRMECR) model is developed to capture the dynamics of COVID-19. Specifically, the number of undetected COVID-19 cases is estimated in five health authority regions in BC, Canada, in 2020. Individual-level information available from the population data BC database is utilized to estimate the case detection probability, infection probability, survival probability, and recovery probability by incorporating testing volumes as covariates that improve the estimate of the parameters. A Markov chain Monte Carlo (MCMC) algorithm is developed to estimate MECMR model parameters. To address the computational challenges encountered, divide-and-conquer strategies are developed. The application provides an estimate of the total COVID-19 burden in the year 2020 and found the percentage of undetected varying from 77.4\% to \$84.0\%.
