A primer on Bayesian estimation of prevalence of COVID-19 patient outcomes.
A common research task in COVID-19 studies often involves the prevalence estimation of certain medical outcomes. Although point estimates with confidence intervals are typically obtained, a better approach is to estimate the entire posterior probability distribution of the prevalence, which can be easily accomplished with a standard Bayesian approach using binomial likelihood and its conjugate beta prior distribution. Using two recently published COVID-19 data sets, we performed Bayesian analysis to estimate the prevalence of infection fatality in Iceland and asymptomatic children in the United States.
['Gao|Xiang|X|', 'Dong|Qunfeng|Q|']
Bayesian COVID-19 asymptomatic SARS-CoV-2 conjugate prior infection fatality risk