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

Abstract

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.

Keywords: Bayesian; COVID-19; SARS-CoV-2; asymptomatic; conjugate prior; infection fatality risk.

Figure 1.

The posterior probability densities of infection fatality rate for different age groups in Iceland: (A) 0–70, (B) 70–80, and (C) >80.

Figure 2.

The posterior probability density of the prevalence of asymptomatic children in four different US regions: (A) West, (B) Midwest, (C) South, and (D) Northeast.