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Estimating the Fraction of Unreported Infections in Epidemics with a Known Epicenter: An Application to COVID-19

Author

Listed:
  • Ali Hortaçsu

    (University of Chicago and NBER)

  • Jiarui Liu

    (University of Chicago)

  • Timothy Schwieg

    (University of Chicago)

Abstract

We develop an analytically tractable method to estimate the fraction of unreported infections in epidemics with a known epicenter and estimate the number of unreported COVID-19 infections in the US during the first half of March 2020. Our method utilizes the covariation in initial reported infections across US regions and the number of travelers to these regions from the epicenter, along with the results of an early randomized testing study in Iceland. Using our estimates of the number of unreported infections, which are substantially larger than the number of reported infections, we also provide estimates for the infection fatality rate using data on reported COVID-19 fatalities from U.S. counties.

Suggested Citation

  • Ali Hortaçsu & Jiarui Liu & Timothy Schwieg, 2020. "Estimating the Fraction of Unreported Infections in Epidemics with a Known Epicenter: An Application to COVID-19," Working Papers 2020-37, Becker Friedman Institute for Research In Economics.
    • Handle: RePEc:bfi:wpaper:2020-37
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    References listed on IDEAS

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    1. Fernando Alvarez & David Argente, 2020. "A Simple Planning Problem for COVID-19 Lockdown," Working Papers 2020-34, Becker Friedman Institute for Research In Economics.
    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]," Review of Financial Studies, Society for Financial Studies, vol. 34(11), pages 5149-5187.
    3. James H. Stock, 2020. "Data Gaps and the Policy Response to the Novel Coronavirus," NBER Working Papers 26902, National Bureau of Economic Research, Inc.
    4. David Berger & Kyle Herkenhoff & Chengdai Huang & Simon Mongey, 2022. "Testing and Reopening in an SEIR Model," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 43, pages 1-21, January.
    5. David Berger & Kyle Herkenhoff & Chengdai Huang & Simon Mongey, 2022. "Testing and Reopening in an SEIR Model," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 43, pages 1-21, January.
    6. Korolev, Ivan, 2021. "Identification and estimation of the SEIRD epidemic model for COVID-19," Journal of Econometrics, Elsevier, vol. 220(1), pages 63-85.
    Full references (including those not matched with items on IDEAS)

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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:

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    2. David Berger & Kyle Herkenhoff & Chengdai Huang & Simon Mongey, 2022. "Testing and Reopening in an SEIR Model," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 43, pages 1-21, January.
    3. Fernández-Villaverde, Jesús & Jones, Charles I., 2022. "Estimating and simulating a SIRD Model of COVID-19 for many countries, states, and cities," Journal of Economic Dynamics and Control, Elsevier, vol. 140(C).
    4. Junic Kim & Kelly Ashihara, 2020. "National Disaster Management System: COVID-19 Case in Korea," IJERPH, MDPI, vol. 17(18), pages 1-18, September.
    5. Daniel L. Millimet & Christopher F. Parmeter, 2022. "COVID‐19 severity: A new approach to quantifying global cases and deaths," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1178-1215, July.
    6. Titan Alon & Minki Kim & David Lagakos & Mitchell VanVuren, 2020. "How Should Policy Responses to the COVID-19 Pandemic Differ in the Developing World?," NBER Working Papers 27273, National Bureau of Economic Research, Inc.
    7. Cem Cakmakli & Yasin Simsek, 2020. "Bridging the COVID-19 Data and the Epidemiological Model using Time Varying Parameter SIRD Model," Papers 2007.02726, arXiv.org, revised Feb 2021.
    8. David Argente & Chang-Tai Hsieh & Munseob Lee, 2022. "The Cost of Privacy: Welfare Effects of the Disclosure of COVID-19 Cases," The Review of Economics and Statistics, MIT Press, vol. 104(1), pages 176-186, March.
    9. Melosi, Leonardo & Rottner, Matthias, 2020. "Pandemic Recessions and Contact Tracing," CEPR Discussion Papers 15482, C.E.P.R. Discussion Papers.
    10. David Berger & Kyle Herkenhoff & Chengdai Huang & Simon Mongey, 2022. "Testing and Reopening in an SEIR Model," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 43, pages 1-21, January.
    11. Xiaowei Chen & Wing Fung Chong & Runhuan Feng & Linfeng Zhang, 2020. "Pandemic risk management: resources contingency planning and allocation," Papers 2012.03200, arXiv.org.
    12. Jinjie Chen & Joon Jin Song & James D. Stamey, 2022. "A Bayesian Hierarchical Spatial Model to Correct for Misreporting in Count Data: Application to State-Level COVID-19 Data in the United States," IJERPH, MDPI, vol. 19(6), pages 1-15, March.
    13. Panos Toulis, 2020. "Estimation of Covid-19 Prevalence from Serology Tests: A Partial Identification Approach," Papers 2006.16214, arXiv.org.
    14. Arun G. Chandrasekhar & Paul Goldsmith-Pinkham & Matthew O. Jackson & Samuel Thau, 2021. "Interacting regional policies in containing a disease," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 118(19), pages 2021520118-, May.
    15. Richard Gearhart & Lyudmyla Sonchak-Ardan & Nyakundi Michieka, 2022. "The efficiency of COVID cases to COVID policies: a robust conditional approach," Empirical Economics, Springer, vol. 63(6), pages 2903-2948, December.
    16. Leonardo Melosi & Matthias Rottner, 2020. "Pandemic Recessions and Contact Tracing," Working Paper Series WP-2020-31, Federal Reserve Bank of Chicago.
    17. Gourieroux, C. & Jasiak, J., 2023. "Time varying Markov process with partially observed aggregate data: An application to coronavirus," Journal of Econometrics, Elsevier, vol. 232(1), pages 35-51.
    18. Toulis, Panos, 2021. "Estimation of Covid-19 prevalence from serology tests: A partial identification approach," Journal of Econometrics, Elsevier, vol. 220(1), pages 193-213.
    19. Acedański, Jan, 2021. "Optimal lockdown policy during the election period," Economic Analysis and Policy, Elsevier, vol. 72(C), pages 102-117.
    20. Malkov, Egor, 2020. "Simulation of coronavirus disease 2019 (COVID-19) scenarios with possibility of reinfection," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    21. Jordan J Bird & Chloe M Barnes & Cristiano Premebida & Anikó Ekárt & Diego R Faria, 2020. "Country-level pandemic risk and preparedness classification based on COVID-19 data: A machine learning approach," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-20, October.
    22. Robert S. Pindyck, 2020. "COVID-19 and the Welfare Effects of Reducing Contagion," NBER Working Papers 27121, National Bureau of Economic Research, Inc.
    23. Subhasish Dey & Jessie Davidson, 2021. "The Determinants of Non-COVID-19 Excess Deaths During the COVID-19 Pandemic: A Cross-country Panel Study," Studies in Microeconomics, , vol. 9(2), pages 196-226, December.
    24. 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.

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