IDEAS home Printed from https://ideas.repec.org/p/ifs/cemmap/20-20.html
   My bibliography  Save this paper

Estimating the COVID-19 Infection Rate: Anatomy of an Inference Problem

Author

Listed:
  • Charles F. Manski

    (Institute for Fiscal Studies and Northwestern University)

  • Francesca Molinari

    (Institute for Fiscal Studies and Cornell University)

Abstract

As a consequence of missing data on tests for infection and imperfect accuracy of tests, reported rates of cumulative population infection by the SARS CoV-2 virus are lower than actual rates of infection. Hence, reported rates of severe illness conditional on infection are higher than actual rates. Understanding the time path of the COVID-19 pandemic has been hampered by the absence of bounds on infection rates that are credible and informative. This paper explains the logical problem of bounding these rates and reports illustrative findings, using data from Illinois, New York, and Italy. We combine the data with assumptions on the infection rate in the untested population and on the accuracy of the tests that appear credible in the current context. We find that the infection rate might be substantially higher than reported. We also find that the infection fatality rate in Illinois, New York, and Italy is substantially lower than reported.

Suggested Citation

  • Charles F. Manski & Francesca Molinari, 2020. "Estimating the COVID-19 Infection Rate: Anatomy of an Inference Problem," CeMMAP working papers CWP20/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:20/20
    as

    Download full text from publisher

    File URL: https://www.ifs.org.uk/uploads/CWP2020-Estimating-the-COVID-19-Infection-Rate.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. repec:hrv:faseco:34728615 is not listed on IDEAS
    2. Charles F. Manski & John V. Pepper, 2018. "How Do Right-to-Carry Laws Affect Crime Rates? Coping with Ambiguity Using Bounded-Variation Assumptions," The Review of Economics and Statistics, MIT Press, vol. 100(2), pages 232-244, May.
    3. Francesca Molinari, 2020. "Microeconometrics with Partial Identification," Papers 2004.11751, arXiv.org.
    4. Charles F. Manski & John V. Pepper, 2000. "Monotone Instrumental Variables, with an Application to the Returns to Schooling," Econometrica, Econometric Society, vol. 68(4), pages 997-1012, July.
    5. Charles F. Manski, 1989. "Anatomy of the Selection Problem," Journal of Human Resources, University of Wisconsin Press, vol. 24(3), pages 343-360.
    6. Elie Tamer, 2010. "Partial Identification in Econometrics," Annual Review of Economics, Annual Reviews, vol. 2(1), pages 167-195, September.
    Full references (including those not matched with items on IDEAS)

    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. Dimitris Christelis & Dimitris Georgarakos & Tullio Jappelli & Geoff Kenny, 2020. "The Covid-19 Crisis and Consumption: Survey Evidence from Six EU Countries," Working Papers 2020_31, Business School - Economics, University of Glasgow.
    2. Brunello, Giorgio & Christelis, Dimitris & Sanz-de-Galdeano, Anna & Terskaya, Anastasia, 2022. "Does College Selectivity Reduce Obesity? A Partial Identification Approach," IZA Discussion Papers 15612, Institute of Labor Economics (IZA).
    3. Lina Zhang & David T. Frazier & D. S. Poskitt & Xueyan Zhao, 2020. "Decomposing Identification Gains and Evaluating Instrument Identification Power for Partially Identified Average Treatment Effects," Papers 2009.02642, arXiv.org, revised Sep 2022.
    4. Amin, Vikesh & Behrman, Jere R. & Fletcher, Jason M. & Flores, Carlos A. & Flores-Lagunes, Alfonso & Kohler, Hans-Peter, 2022. "Does Schooling Improve Cognitive Abilities at Older Ages: Causal Evidence from Nonparametric Bounds," IZA Discussion Papers 15371, Institute of Labor Economics (IZA).
    5. Christelis, Dimitris & Messina, Julián, 2019. "Partial Identification of Population Average and Quantile Treatment Effects in Observational Data under Sample Selection," IDB Publications (Working Papers) 9520, Inter-American Development Bank.
    6. Charles F. Manski, 2021. "Probabilistic Prediction for Binary Treatment Choice: with Focus on Personalized Medicine," NBER Working Papers 29358, National Bureau of Economic Research, Inc.
    7. Breunig, Christoph & Mammen, Enno & Simoni, Anna, 2018. "Nonparametric estimation in case of endogenous selection," Journal of Econometrics, Elsevier, vol. 202(2), pages 268-285.
    8. Monique De Haan & Edwin Leuven, 2020. "Head Start and the Distribution of Long-Term Education and Labor Market Outcomes," Journal of Labor Economics, University of Chicago Press, vol. 38(3), pages 727-765.
    9. Aibo Gong, 2021. "Bounds for Treatment Effects in the Presence of Anticipatory Behavior," Papers 2111.06573, arXiv.org, revised Dec 2022.
    10. Markus Frölich, 2004. "Programme Evaluation with Multiple Treatments," Journal of Economic Surveys, Wiley Blackwell, vol. 18(2), pages 181-224, April.
    11. Ho, Katherine & Rosen, Adam M., 2015. "Partial Identification in Applied Research: Benefits and Challenges," CEPR Discussion Papers 10883, C.E.P.R. Discussion Papers.
    12. Xiaoyu Cheng, 2022. "Robust Data-Driven Decisions Under Model Uncertainty," Papers 2205.04573, arXiv.org.
    13. Lukáš Lafférs, 2019. "Identification in Models with Discrete Variables," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 657-696, February.
    14. Matias D. Cattaneo & Xinwei Ma & Yusufcan Masatlioglu & Elchin Suleymanov, 2020. "A Random Attention Model," Journal of Political Economy, University of Chicago Press, vol. 128(7), pages 2796-2836.
    15. Choudhury, Sanchari, 2019. "WTO membership and corruption," European Journal of Political Economy, Elsevier, vol. 60(C).
    16. Matthew A. Masten & Alexandre Poirier, 2018. "Identification of Treatment Effects Under Conditional Partial Independence," Econometrica, Econometric Society, vol. 86(1), pages 317-351, January.
    17. Ivan A. Canay & Azeem M. Shaikh, 2016. "Practical and theoretical advances in inference for partially identified models," CeMMAP working papers CWP05/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    18. Sung Jae Jun & Sokbae (Simon) Lee, 2018. "Identifying the effect of persuasion," CeMMAP working papers CWP19/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    19. Grafova, Irina B. & Freedman, Vicki A. & Lurie, Nicole & Kumar, Rizie & Rogowski, Jeannette, 2014. "The difference-in-difference method: Assessing the selection bias in the effects of neighborhood environment on health," Economics & Human Biology, Elsevier, vol. 13(C), pages 20-33.
    20. Magne Mogstad & Andres Santos & Alexander Torgovitsky, 2018. "Using Instrumental Variables for Inference About Policy Relevant Treatment Parameters," Econometrica, Econometric Society, vol. 86(5), pages 1589-1619, September.

    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • I19 - Health, Education, and Welfare - - Health - - - Other

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:ifs:cemmap:20/20. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/cmifsuk.html .

    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: Emma Hyman (email available below). General contact details of provider: https://edirc.repec.org/data/cmifsuk.html .

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.