Estimating the cumulative rate of SARS-CoV-2 infection

Christopher R. Bollinger, Martijn van Hasselt

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish
Article number109652
JournalEconomics Letters
StatePublished - Dec 2020

Bibliographical note

Publisher Copyright:
© 2020 Elsevier B.V.


  • Bayesian inference
  • Measurement error
  • Non-random sampling
  • Partial identification

ASJC Scopus subject areas

  • Finance
  • Economics and Econometrics


Dive into the research topics of 'Estimating the cumulative rate of SARS-CoV-2 infection'. Together they form a unique fingerprint.

Cite this