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Identification and Estimation of Undetected COVID-19 Cases Using Testing Data from Iceland

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Listed:
  • Karl M. Aspelund
  • Michael C. Droste
  • James H. Stock
  • Christopher D. Walker

Abstract

In the early stages of the COVID-19 pandemic, international testing efforts tended to target individuals whose symptoms and/or jobs placed them at a high presumed risk of infection. Testing regimes of this sort potentially result in a high proportion of cases going undetected. Quantifying this parameter, which we refer to as the undetected rate, is an important contribution to the analysis of the early spread of the SARS-CoV-2 virus. We show that partial identification techniques can credibly deal with the data problems that common COVID-19 testing programs induce (i.e. excluding quarantined individuals from testing and low participation in random screening programs). We use public data from two Icelandic testing regimes during the first month of the outbreak and estimate an identified interval for the undetected rate. Our main approach estimates that the undetected rate was between 89% and 93% before the medical system broadened its eligibility criteria and between 80% and 90% after.

Suggested Citation

  • Karl M. Aspelund & Michael C. Droste & James H. Stock & Christopher D. Walker, 2020. "Identification and Estimation of Undetected COVID-19 Cases Using Testing Data from Iceland," NBER Working Papers 27528, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:27528
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    References listed on IDEAS

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    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.
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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. 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.
    2. Antonio Diez de los Rios, 2022. "A macroeconomic model of an epidemic with silent transmission and endogenous self‐isolation," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 55(S1), pages 581-625, February.
    3. Hortaçsu, Ali & Liu, Jiarui & Schwieg, Timothy, 2021. "Estimating the fraction of unreported infections in epidemics with a known epicenter: An application to COVID-19," Journal of Econometrics, Elsevier, vol. 220(1), pages 106-129.
    4. Andrew T. Levin & William P. Hanage & Nana Owusu-Boaitey & Kensington B. Cochran & Seamus P. Walsh & Gideon Meyerowitz-Katz, 2020. "Assessing the Age Specificity of Infection Fatality Rates for COVID-19: Systematic Review, Meta-analysis, & Public Policy Implications," NBER Working Papers 27597, National Bureau of Economic Research, Inc.
    5. Andrew G. Atkeson & Karen A. Kopecky & Tao Zha, 2024. "Four Stylized Facts About Covid‐19," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 65(1), pages 3-42, February.
    6. Deniz Dutz & Michael Greenstone & Ali Hortaçsu & Santiago Lacouture & Magne Mogstad & Azeem M. Shaikh & Alexander Torgovitsky & Winnie van Dijk, 2023. "Representation and Hesitancy in Population Health Research: Evidence from a COVID-19 Antibody Study," NBER Working Papers 30880, National Bureau of Economic Research, Inc.
    7. Junic Kim & Kelly Ashihara, 2020. "National Disaster Management System: COVID-19 Case in Korea," IJERPH, MDPI, vol. 17(18), pages 1-18, September.

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    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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