IDEAS home Printed from https://ideas.repec.org/a/wly/mgtdec/v43y2022i6p1880-1887.html
   My bibliography  Save this article

Test sensitivity for infection versus infectiousness of SARS‐CoV‐2

Author

Listed:
  • Joshua S. Gans

Abstract

The most commonly used test for the presence of SARS‐CoV‐2 is a PCR test that is able to detect very low viral loads and inform on treatment decisions. Medical research has confirmed that many individuals might be infected with SARS‐CoV‐2 but not infectious. Knowing whether an individual is infectious is the critical piece of information for a decision to isolate an individual or not. This paper examines the value of different tests from an information‐theoretic approach and shows that applying treatment‐based approval standards for tests for infection will lower the value of those tests and likely causes decisions based on them to have too many false positives (i.e., individuals isolated who are not infectious). The conclusion is that test scoring be tailored to the decision being made.

Suggested Citation

  • Joshua S. Gans, 2022. "Test sensitivity for infection versus infectiousness of SARS‐CoV‐2," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(6), pages 1880-1887, September.
    • Handle: RePEc:wly:mgtdec:v:43:y:2022:i:6:p:1880-1887
    DOI: 10.1002/mde.3496
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/mde.3496
    Download Restriction: no
    File URL: https://libkey.io/10.1002/mde.3496?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
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Bergstrom, Ted & Bergstrom, Carl & Li, Haoran, 2020. "Frequency and Accuracy in Proactive Testing for COVID-19," University of California at Santa Barbara, Economics Working Paper Series qt8nf4c0jd, Department of Economics, UC Santa Barbara.
    2. Martin S Eichenbaum & Sergio Rebelo & Mathias Trabandt, 2021. "The Macroeconomics of Epidemics [Economic activity and the spread of viral diseases: Evidence from high frequency data]," The Review of Financial Studies, Society for Financial Studies, vol. 34(11), pages 5149-5187.
    3. Maximilian Kasy & Alexander Teytelboym, 2020. "Adaptive targeted infectious disease testing," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 36(Supplemen), pages 77-93.
    4. Michael A. Boozer & Tomas J. Philipson, 2000. "The Impact of Public Testing for Human Immunodeficiency Virus," Journal of Human Resources, University of Wisconsin Press, vol. 35(3), pages 419-446.
    5. Giorgia Guglielmi, 2021. "Rapid coronavirus tests: a guide for the perplexed," Nature, Nature, vol. 590(7845), pages 202-205, February.
    6. Joshua S. Gans, 2020. "The Economic Consequences of R̂ = 1: Towards a Workable Behavioural Epidemiological Model of Pandemics," NBER Working Papers 27632, National Bureau of Economic Research, Inc.
    7. Daron Acemoglu & Ali Makhdoumi & Azarakhsh Malekian & Asuman Ozdaglar, 2024. "Testing, Voluntary Social Distancing, and the Spread of an Infection," Operations Research, INFORMS, vol. 72(2), pages 533-548, March.
    8. Ely, Jeffrey & Galeotti, Andrea & Jann, Ole & Steiner, Jakub, 2021. "Optimal test allocation," Journal of Economic Theory, Elsevier, vol. 193(C).
    9. Maximilian Kasy & Alexander Teytelboym, 0. "Adaptive targeted infectious disease testing," Oxford Review of Economic Policy, Oxford University Press, vol. 36(Supplemen), pages 77-93.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Hedlund, Jonas & Hernandez-Chanto, Allan & Oyarzun, Carlos, 2024. "Contagion management through information disclosure," Journal of Economic Theory, Elsevier, vol. 218(C).
    2. Andrew Atkeson & Michael Droste & Michael J. Mina & James H. Stock, 2020. "Economic Benefits of COVID-19 Screening Tests," Staff Report 616, Federal Reserve Bank of Minneapolis.
    3. 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.
    4. Bergstrom, Ted & Bergstrom, Carl & Li, Haoran, 2020. "Frequency and Accuracy in Proactive Testing for COVID-19," University of California at Santa Barbara, Economics Working Paper Series qt8nf4c0jd, Department of Economics, UC Santa Barbara.

    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. Daron Acemoglu & Ali Makhdoumi & Azarakhsh Malekian & Asuman Ozdaglar, 2024. "Testing, Voluntary Social Distancing, and the Spread of an Infection," Operations Research, INFORMS, vol. 72(2), pages 533-548, March.
    2. Goodkin-Gold, Matthew & Kremer, Michael & Snyder, Christopher M. & Williams, Heidi, 2022. "Optimal vaccine subsidies for endemic diseases," International Journal of Industrial Organization, Elsevier, vol. 84(C).
    3. Ely, Jeffrey & Galeotti, Andrea & Jann, Ole & Steiner, Jakub, 2021. "Optimal test allocation," Journal of Economic Theory, Elsevier, vol. 193(C).
    4. Bisin, Alberto & Moro, Andrea, 2022. "JUE insight: Learning epidemiology by doing: The empirical implications of a Spatial-SIR model with behavioral responses," Journal of Urban Economics, Elsevier, vol. 127(C).
    5. Lipnowski, Elliot & Ravid, Doron, 2021. "Pooled testing for quarantine decisions," Journal of Economic Theory, Elsevier, vol. 198(C).
    6. Carnehl, Christoph & Fukuda, Satoshi & Kos, Nenad, 2023. "Epidemics with behavior," Journal of Economic Theory, Elsevier, vol. 207(C).
    7. Thomas Ash & Antonio M. Bento & Daniel Kaffine & Akhil Rao & Ana I. Bento, 2022. "Disease-economy trade-offs under alternative epidemic control strategies," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    8. Bahety, Girija & Bauhoff, Sebastian & Patel, Dev & Potter, James, 2021. "Texts don’t nudge: An adaptive trial to prevent the spread of COVID-19 in India," Journal of Development Economics, Elsevier, vol. 153(C).
    9. Shibamoto, Masahiko & Hayaki, Shoka & Ogisu, Yoshitaka, 2022. "COVID-19 infection spread and human mobility," Journal of the Japanese and International Economies, Elsevier, vol. 64(C).
    10. Thomas Hellmann & Veikko Thiele, 2022. "A theory of voluntary testing and self‐isolation in an ongoing pandemic," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 24(5), pages 873-911, October.
    11. Timo Boppart & Karl Harmenberg & John Hassler & Per Krusell & Jonna Olsson, 2020. "Integrated Epi-Econ Assessment," NBER Working Papers 28282, National Bureau of Economic Research, Inc.
    12. Yasushi Iwamoto, 2021. "Welfare economics of managing an epidemic: an exposition," The Japanese Economic Review, Springer, vol. 72(4), pages 537-579, October.
    13. Dizioli, Allan & Pinheiro, Roberto, 2021. "Information and inequality in the time of a pandemic," Journal of Economic Dynamics and Control, Elsevier, vol. 130(C).
    14. Masashige Hamano & Munechika Katayama & So Kubota, 2020. "COVID-19 Misperception and Macroeconomy," Working Papers 2016, Waseda University, Faculty of Political Science and Economics.
    15. Łukasz Rachel, 2020. "An Analytical Model of Covid-19 Lockdowns," Discussion Papers 2029, Centre for Macroeconomics (CFM).
    16. Andrew G. Atkeson & Karen Kopecky & Tao Zha, 2021. "Behavior and the Transmission of COVID-19," AEA Papers and Proceedings, American Economic Association, vol. 111, pages 356-360, May.
    17. 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.
    18. Joshua S. Gans, 2020. "The Economic Consequences of R̂ = 1: Towards a Workable Behavioural Epidemiological Model of Pandemics," NBER Working Papers 27632, National Bureau of Economic Research, Inc.
    19. Mark Whitmeyer, 2021. "An imperfect test for a virus can Be worse than No test at all," Health Economics, John Wiley & Sons, Ltd., vol. 30(6), pages 1347-1360, June.
    20. Hu, Ju & Zhou, Zhen, 2022. "Disclosure in epidemics," Journal of Economic Theory, Elsevier, vol. 202(C).

    More about this item

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • 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:wly:mgtdec:v:43:y:2022:i:6:p:1880-1887. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/7976 .

    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.