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Substantial underestimation of SARS-CoV-2 infection in the United States

Author

Listed:
  • Sean L. Wu

    (University of California)

  • Andrew N. Mertens

    (University of California)

  • Yoshika S. Crider

    (University of California
    University of California)

  • Anna Nguyen

    (University of California)

  • Nolan N. Pokpongkiat

    (University of California)

  • Stephanie Djajadi

    (University of California)

  • Anmol Seth

    (University of California)

  • Michelle S. Hsiang

    (University of Texas Southwestern Medical Center
    University of California, San Francisco
    University of California)

  • John M. Colford

    (University of California)

  • Art Reingold

    (University of California)

  • Benjamin F. Arnold

    (University of California
    University of California)

  • Alan Hubbard

    (University of California)

  • Jade Benjamin-Chung

    (University of California)

Abstract

Accurate estimates of the burden of SARS-CoV-2 infection are critical to informing pandemic response. Confirmed COVID-19 case counts in the U.S. do not capture the total burden of the pandemic because testing has been primarily restricted to individuals with moderate to severe symptoms due to limited test availability. Here, we use a semi-Bayesian probabilistic bias analysis to account for incomplete testing and imperfect diagnostic accuracy. We estimate 6,454,951 cumulative infections compared to 721,245 confirmed cases (1.9% vs. 0.2% of the population) in the United States as of April 18, 2020. Accounting for uncertainty, the number of infections during this period was 3 to 20 times higher than the number of confirmed cases. 86% (simulation interval: 64–99%) of this difference is due to incomplete testing, while 14% (0.3–36%) is due to imperfect test accuracy. The approach can readily be applied in future studies in other locations or at finer spatial scale to correct for biased testing and imperfect diagnostic accuracy to provide a more realistic assessment of COVID-19 burden.

Suggested Citation

  • Sean L. Wu & Andrew N. Mertens & Yoshika S. Crider & Anna Nguyen & Nolan N. Pokpongkiat & Stephanie Djajadi & Anmol Seth & Michelle S. Hsiang & John M. Colford & Art Reingold & Benjamin F. Arnold & Al, 2020. "Substantial underestimation of SARS-CoV-2 infection in the United States," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18272-4
    DOI: 10.1038/s41467-020-18272-4
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    Cited by:

    1. Clara S. Grønkjær & Rune H. B. Christensen & Daniel Kondziella & Michael E. Benros, 2023. "Long-term neurological outcome after COVID-19 using all SARS-CoV-2 test results and hospitalisations in Denmark with 22-month follow-up," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    2. Bello, Piera & Rocco, Lorenzo, 2022. "Education and COVID-19 excess mortality," Economics & Human Biology, Elsevier, vol. 47(C).
    3. Ewen Gallic & Michel Lubrano & Pierre Michel, 2021. "Optimal lockdowns: Analysing the efficiency of sanitary policies in Europe during the first wave," AMSE Working Papers 2111, Aix-Marseille School of Economics, France.
    4. Jeffrey E. Harris, 2021. "Los Angeles County SARS-CoV-2 Epidemic: Critical Role of Multi-generational Intra-household Transmission," Journal of Bioeconomics, Springer, vol. 23(1), pages 55-83, April.
    5. Maureen Rebecca Smith & Maria Trofimova & Ariane Weber & Yannick Duport & Denise Kühnert & Max von Kleist, 2021. "Rapid incidence estimation from SARS-CoV-2 genomes reveals decreased case detection in Europe during summer 2020," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    6. Mario Morvan & Anna Lo Jacomo & Celia Souque & Matthew J. Wade & Till Hoffmann & Koen Pouwels & Chris Lilley & Andrew C. Singer & Jonathan Porter & Nicholas P. Evens & David I. Walker & Joshua T. Bunc, 2022. "An analysis of 45 large-scale wastewater sites in England to estimate SARS-CoV-2 community prevalence," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    7. Elina Lampi & Daniel Carelli & Jon Pierre & Björn Rönnerstrand, 2023. "Two pandemics: the COVID-19 pandemic’s impact on future AMR collaboration in Europe," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-10, December.
    8. Imelda Trejo & Nicolas W Hengartner, 2022. "A modified Susceptible-Infected-Recovered model for observed under-reported incidence data," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-23, February.
    9. Mutahhar A. Dar & Bartlomiej Gladysz & Aleksander Buczacki, 2021. "Impact of COVID19 on Operational Activities of Manufacturing Organizations—A Case Study and Industry 4.0-Based Survive-Stabilise-Sustainability (3S) Framework," Energies, MDPI, vol. 14(7), pages 1-28, March.
    10. Shakhany, Mohammad Qaleh & Salimifard, Khodakaram, 2021. "Predicting the dynamical behavior of COVID-19 epidemic and the effect of control strategies," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    11. Spyros Niavis & Dimitris Kallioras & George Vlontzos & Marie-Noelle Duquenne, 2021. "COVID-19 Pandemic and Lockdown Fine Optimality," Economies, MDPI, vol. 9(1), pages 1-26, March.
    12. Bernardo García-Carreras & Matt D. T. Hitchings & Michael A. Johansson & Matthew Biggerstaff & Rachel B. Slayton & Jessica M. Healy & Justin Lessler & Talia Quandelacy & Henrik Salje & Angkana T. Huan, 2023. "Accounting for assay performance when estimating the temporal dynamics in SARS-CoV-2 seroprevalence in the U.S," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    13. Constanza Fosco & Felipe Zurita, 2021. "Assessing the short-run effects of lockdown policies on economic activity, with an application to the Santiago Metropolitan Region, Chile," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-23, June.

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