Estimating the cumulative rate of SARS-CoV-2 infection

Christopher Bollinger and Martijn van Hasselt

Economics Letters, 2020, vol. 197, issue C

Abstract: Accurate estimates of the cumulative incidence of SARS-CoV-2 infection remain elusive. Among the reasons for this are that tests for the virus are not randomly administered, and that the most commonly used tests can yield a substantial fraction of false negatives. In this article, we propose a simple and easy-to-use Bayesian model to estimate the infection rate, which is only partially identified. The model is based on the mapping from the fraction of positive test results to the cumulative infection rate, which depends on two unknown quantities: the probability of a false negative test result and a measure of testing bias towards the infected population. Accumulating evidence about SARS-CoV-2 can be incorporated into the model, which will lead to more precise inference about the infection rate.

Keywords: Bayesian inference; Partial identification; Measurement error; Non-random sampling (search for similar items in EconPapers)
JEL-codes: C11 C25 I18 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:197:y:2020:i:c:s0165176520304122

DOI: 10.1016/j.econlet.2020.109652

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