A primer on Bayesian estimation of prevalence of COVID-19 patient outcomes | Zendy

Xiang Gao | Zendy; Qunfeng Dong | Zendy

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A primer on Bayesian estimation of prevalence of COVID-19 patient outcomes

Author(s) -

Xiang Gao,

Qunfeng Dong

Publication year - 2020

Publication title -

jamia open

Language(s) - English

Resource type - Journals

ISSN - 2574-2531

DOI - 10.1093/jamiaopen/ooaa062

Subject(s) - bayesian probability , covid-19 , confidence interval , statistics , point estimation , estimation , medicine , bayes estimator , conjugate prior , bayes' theorem , posterior probability , mathematics , outbreak , virology , disease , management , infectious disease (medical specialty) , economics

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.

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