IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i3p1090-d487392.html
   My bibliography  Save this article

Estimating Unreported COVID-19 Cases with a Time-Varying SIR Regression Model

Author

Listed:
  • Zhenghong Peng

    (Department of Graphics and Digital Technology, School of Urban Design, Wuhan University, Wuhan 430072, China)

  • Siya Ao

    (Department of Graphics and Digital Technology, School of Urban Design, Wuhan University, Wuhan 430072, China)

  • Lingbo Liu

    (Department of Urban Planning, School of Urban Design, Wuhan University, Wuhan 430072, China)

  • Shuming Bao

    (China Data Institute, Ann Arbor, MI 48108, USA)

  • Tao Hu

    (Center for Geographic Analysis, Harvard University, Cambridge, MA 02138, USA)

  • Hao Wu

    (Department of Graphics and Digital Technology, School of Urban Design, Wuhan University, Wuhan 430072, China)

  • Ru Wang

    (Department of Graphics and Digital Technology, School of Urban Design, Wuhan University, Wuhan 430072, China)

Abstract

Background: Potential unreported infection might impair and mislead policymaking for COVID-19, and the contemporary spread of COVID-19 varies in different counties of the United States. It is necessary to estimate the cases that might be underestimated based on county-level data, to take better countermeasures against COVID-19. We suggested taking time-varying Susceptible-Infected-Recovered (SIR) models with unreported infection rates (UIR) to estimate factual COVID-19 cases in the United States. Methods: Both the SIR model integrated with unreported infection rates (SIRu) of fixed-time effect and SIRu with time-varying parameters (tvSIRu) were applied to estimate and compare the values of transmission rate (TR), UIR, and infection fatality rate (IFR) based on US county-level COVID-19 data. Results: Based on the US county-level COVID-19 data from 22 January (T 1 ) to 20 August (T 212 ) in 2020, SIRu was first tested and verified by Ordinary Least Squares (OLS) regression. Further regression of SIRu at the county-level showed that the average values of TR, UIR, and IFR were 0.034%, 19.5%, and 0.51% respectively. The ranges of TR, UIR, and IFR for all states ranged from 0.007–0.157 (mean = 0.048), 7.31–185.6 (mean = 38.89), and 0.04–2.22% (mean = 0.22%). Among the time-varying TR equations, the power function showed better fitness, which indicated a decline in TR decreasing from 227.58 (T 1 ) to 0.022 (T 212 ). The general equation of tvSIRu showed that both the UIR and IFR were gradually increasing, wherein, the estimated value of UIR was 9.1 (95%CI 5.7–14.0) and IFR was 0.70% (95%CI 0.52–0.95%) at T 212 . Interpretation: Despite the declining trend in TR and IFR, the UIR of COVID-19 in the United States is still on the rise, which, it was assumed would decrease with sufficient tests or improved countersues. The US medical system might be largely affected by severe cases amidst a rapid spread of COVID-19.

Suggested Citation

  • Zhenghong Peng & Siya Ao & Lingbo Liu & Shuming Bao & Tao Hu & Hao Wu & Ru Wang, 2021. "Estimating Unreported COVID-19 Cases with a Time-Varying SIR Regression Model," IJERPH, MDPI, vol. 18(3), pages 1-13, January.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:3:p:1090-:d:487392
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/3/1090/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/3/1090/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Paolo Contiero & Alessandro Borgini & Martina Bertoldi & Anna Abita & Giuseppe Cuffari & Paola Tomao & Maria Concetta D’Ovidio & Stefano Reale & Silvia Scibetta & Giovanna Tagliabue & Roberto Boffi & , 2022. "An Epidemiological Study to Investigate Links between Atmospheric Pollution from Farming and SARS-CoV-2 Mortality," IJERPH, MDPI, vol. 19(8), pages 1-12, April.

    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:gam:jijerp:v:18:y:2021:i:3:p:1090-:d:487392. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.