IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v197y2020ics0165176520304122.html
   My bibliography  Save this article

Estimating the cumulative rate of SARS-CoV-2 infection

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176520304122
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econlet.2020.109652?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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," 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    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

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jörg Stoye, 2022. "Bounding infection prevalence by bounding selectivity and accuracy of tests: with application to early COVID-19," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 1-14.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jörg Stoye, 2022. "Bounding infection prevalence by bounding selectivity and accuracy of tests: with application to early COVID-19," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 1-14.
    2. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2018. "Bayesian Vector Autoregressions," The Warwick Economics Research Paper Series (TWERPS) 1159, University of Warwick, Department of Economics.
    3. Raffaella Giacomini & Toru Kitagawa, 2021. "Robust Bayesian Inference for Set‐Identified Models," Econometrica, Econometric Society, vol. 89(4), pages 1519-1556, July.
    4. Yuan Liao & Anna Simoni, 2012. "Semi-parametric Bayesian Partially Identified Models based on Support Function," Papers 1212.3267, arXiv.org, revised Nov 2013.
    5. Christiane Baumeister & James D. Hamilton, 2015. "Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information," Econometrica, Econometric Society, vol. 83(5), pages 1963-1999, September.
    6. Martijn van Hasselt & Christopher R. Bollinger & Jeremy W. Bray, 2022. "A Bayesian approach to account for misclassification in prevalence and trend estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(2), pages 351-367, March.
    7. Raffaella Giacomini & Toru Kitagawa & Matthew Read, 2021. "Identification and Inference Under Narrative Restrictions," Papers 2102.06456, arXiv.org.
    8. Francis J. DiTraglia & Camilo Garcia-Jimeno, 2020. "A Framework for Eliciting, Incorporating, and Disciplining Identification Beliefs in Linear Models," Papers 2011.07276, arXiv.org.
    9. Yuan Liao & Anna Simoni, 2016. "Bayesian Inference for Partially Identified Convex Models: Is it Valid for Frequentist Inference?," Departmental Working Papers 201607, Rutgers University, Department of Economics.
    10. Francis DiTraglia & Camilo Garcia-Jimeno, 2015. "A Framework for Eliciting, Incorporating, and Disciplining Identification Beliefs in Linear Models, Third Version," PIER Working Paper Archive 15-030, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 09 Sep 2015.
    11. Francesco Flaviano Russo, 2020. "Testing Policies During an Epidemic," CSEF Working Papers 591, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    12. Giacomini, Raffaella & Kitagawa, Toru & Read, Matthew, 2022. "Robust Bayesian inference in proxy SVARs," Journal of Econometrics, Elsevier, vol. 228(1), pages 107-126.
    13. Raffaella Giacomini & Toru Kitagawa & Alessio Volpicella, 2022. "Uncertain identification," Quantitative Economics, Econometric Society, vol. 13(1), pages 95-123, January.
    14. Bollinger, Christopher R. & van Hasselt, Martijn, 2017. "Bayesian moment-based inference in a regression model with misclassification error," Journal of Econometrics, Elsevier, vol. 200(2), pages 282-294.
    15. Kitagawa, Toru & Wang, Guanyi, 2023. "Who should get vaccinated? Individualized allocation of vaccines over SIR network," Journal of Econometrics, Elsevier, vol. 232(1), pages 109-131.
    16. Georgiadis, Georgios & Müller, Gernot J. & Schumann, Ben, 2024. "Global risk and the dollar," Journal of Monetary Economics, Elsevier, vol. 144(C).
    17. Brendan Kline & Elie Tamer, 2024. "Counterfactual Analysis in Empirical Games," Papers 2410.12731, arXiv.org.
    18. Francis DiTraglia & Camilo Garcia-Jimeno, 2015. "A Framework for Eliciting, Incorporating, and Disciplining Identification Beliefs in Linear Models, Second Version," PIER Working Paper Archive 15-028, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 31 Aug 2015.
    19. Arthur Lewbel, 2019. "The Identification Zoo: Meanings of Identification in Econometrics," Journal of Economic Literature, American Economic Association, vol. 57(4), pages 835-903, December.
    20. Lee, Sokbae & Liao, Yuan & Seo, Myung Hwan & Shin, Youngki, 2021. "Sparse HP filter: Finding kinks in the COVID-19 contact rate," Journal of Econometrics, Elsevier, vol. 220(1), pages 158-180.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecolet:v:197:y:2020:i:c:s0165176520304122. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.