In the absence of full scale serological testing, estimation of infectiousness and fatality of the SARS-CoV-2 virus in the COVID-19 global pandemic is complicated by ascertainment bias resulting from not all infected individuals being detected and recorded as COVID-19 cases. Here, I will outline a modeling strategy to obtain more plausible estimates of the true values of key epidemiological variables by fitting a set of mechanistic Bayesian latent-variable SIR models to confirmed COVID-19 cases, deaths, and recoveries, for all regions (countries and US states) independently.

This seminar is jointly run by CMM, CAMBAM and the University of Waterloo.

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