Understanding the Bias between the Number of Confirmed Cases and Actual Number of Infections in the COVID-19 Pandemic

  1. Xingang Chen
  2. Dhiraj Kumar Hazra

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Abstract

The number of positive cases confirmed in the viral tests is a probe of the actual number of infections of COVID-19. The bias between these two quantities is a key element underlying the determination of some important parameters of this disease and the policy-making during the pandemic. To study the dependence of this bias on measured variables, we introduce a parameterization model that motivates a method of organizing the daily data of the numbers of the total tests, confirmed cases, hospitalizations and fatalities. After comparing with the historical data of the USA in the past few months, we find a simple formula relating these four variables. As a few applications, we show, among other things, how this formula can be used to project the number of actual infections, to provide guidance on how the test volume should be adjusted, and to derive an upper bound on the overall infection fatality rate of COVID-19 ( < 0.64%, 95% C.L.) and a theoretical estimate of its value.

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  1. ScreenIT

    SciScore for 10.1101/2020.06.22.20137208: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

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  2. Version published to 10.1101/2020.06.22.20137208 on medRxiv