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Estimating the cumulative rate of SARS-CoV-2 infection

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  • Bollinger, Christopher R.
  • van Hasselt, Martijn

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.

Suggested Citation

  • Bollinger, Christopher R. & van Hasselt, Martijn, 2020. "Estimating the cumulative rate of SARS-CoV-2 infection," Economics Letters, Elsevier, vol. 197(C).
    • Handle: RePEc:eee:ecolet:v:197:y:2020:i:c:s0165176520304122
    DOI: 10.1016/j.econlet.2020.109652
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    References listed on IDEAS

    as
    1. Manski, Charles F. & Molinari, Francesca, 2021. "Estimating the COVID-19 infection rate: Anatomy of an inference problem," Journal of Econometrics, Elsevier, vol. 220(1), pages 181-192.
    2. Franco Peracchi & Daniele Terlizzese, 2020. "Estimating the prevalence of the COVID-19 infection, with an application to Italy," EIEF Working Papers Series 2013, Einaudi Institute for Economics and Finance (EIEF), revised May 2020.
    3. Jörg Stoye, 2022. "Bounding infection prevalence by bounding selectivity and accuracy of tests: with application to early COVID-19 [False-negative results of initial RT-PCR assays for COVID-19: a systematic review]," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 1-14.
    4. Daniel W. Sacks & Nir Menachemi & Peter Embi & Coady Wing, 2020. "What can we learn about SARS-CoV-2 prevalence from testing and hospital data?," Papers 2008.00298, arXiv.org, revised Mar 2021.
    5. Poirier, Dale J., 1998. "Revising Beliefs In Nonidentified Models," Econometric Theory, Cambridge University Press, vol. 14(4), pages 483-509, August.
    6. Hyungsik Roger Moon & Frank Schorfheide, 2012. "Bayesian and Frequentist Inference in Partially Identified Models," Econometrica, Econometric Society, vol. 80(2), pages 755-782, March.
    7. Panos Toulis, 2020. "Estimation of Covid-19 Prevalence from Serology Tests: A Partial Identification Approach," Papers 2006.16214, arXiv.org.
    8. Panos Toulis, 2020. "Estimation of COVID-19 Prevalence from Serology Tests: A Partial Identification Approach," Working Papers 2020-54_Revised, Becker Friedman Institute for Research In Economics.
    9. Charles F. Manski, 2020. "Bounding the Predictive Values of COVID-19 Antibody Tests," NBER Working Papers 27226, National Bureau of Economic Research, Inc.
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    RePEc Biblio mentions

    As found on the RePEc Biblio, the curated bibliography for Economics:
    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Specific pandemics > Covid-19 > Health > Measurement

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    Cited by:

    1. Jörg Stoye, 2022. "Bounding infection prevalence by bounding selectivity and accuracy of tests: with application to early COVID-19 [False-negative results of initial RT-PCR assays for COVID-19: a systematic review]," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 1-14.

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    More about this item

    Keywords

    Bayesian inference; Partial identification; Measurement error; Non-random sampling;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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