Ma, Siying2024-08-162024-08-162024https://hdl.handle.net/1828/20293The COVID-19 pandemic brought the need for novel disease analytic models capable of estimating the true number of infections, including those that evaded detection. Statistical methods, such as the stratified-Petersen estimator, provide effective ways in wildlife population modelling to estimate hard-to-reach population size. We developed a novel disease analytic model to estimate the levels of underreported COVID-19 cases and the true population size based on the idea of developing a Bayesian version of the stratified-Petersen estimator under a state-space formulation using individual-level capture-recapture data. We obtained the capture events from individuals’ electronic health records and treated the occurrence of positive SARS-CoV-2 diagnostic test results and 2020 COVID-19-related hospitalizations as the tagging and recapture processes. Applying this model to the data from the Northern Health Authority region in British Columbia, Canada in 2020 by using a Bayesian Markov chain Monte Carlo (MCMC) approach, we found that the estimate of the size of the COVID-19 population (Nˆ = 2, 967) is 1.58 (95% CI: (1.53, 1.63)) times greater than the observed cases (nobs = 1, 880), which is a comparable result to those reported in other studies.enAvailable to the World Wide Webdisease analytic modelhidden population estimationstratified-Petersen estimatorstate-space formulationThesis