Estimating SARS-CoV-2 infections from deaths, confirmed cases, tests, and random surveys.

ABSTRACT: There are multiple sources of data giving information about the number of SARS-CoV-2 infections in the population, but all have major drawbacks, including biases and delayed reporting. For example, the number of confirmed cases largely underestimates the number of infections, and deaths lag infections substantially, while test positivity rates tend to greatly overestimate prevalence. Representative random prevalence surveys, the only putatively unbiased source, are sparse in time and space, and the results can come with big delays. Reliable estimates of population prevalence are necessary for understanding the spread of the virus and the effectiveness of mitigation strategies. We develop a simple Bayesian framework to estimate viral prevalence by combining several of the main available dat

SUBMITTER: Irons NJ 

PROVIDER: S-EPMC8346866 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature