Bayesian, Universal COVID Testing

  1. Christian T. Meyer
  2. Joel M. Kralj

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Abstract

During the SARS-COV2 pandemic, there has been a persistent call for universal testing to better inform policy decisions. However, a little considered aspect of this call is the relationship between a test’s accuracy and the tested demographic. What are the implications of frequent, universal testing in otherwise asymptomatic demographics? By applying Bayesian statistics, it becomes clear that as the odds of having COVID decreases, there is a non-linear increase in the odds that each positive test is, in fact, a false positive. This phenomenon has precedence in the historical narrative surrounding universal mammogram screening which is no longer recommended due to the unacceptably high rate of false positives. The solution to combat the inflation of false positives is also suggested by Bayesian statistics: intelligently integrating multiple COVID diagnostic tests and symptoms via Bayes’ Theorem, an approach conceptually similar to pre-screening for mammograms. This extra information is readily available ( e . g . olfactory function and fever) and will minimize the economic and emotional costs incurred by false positives while simultaneously improving the information available for policy-makers. In summary, along with the push for universal testing should be an equally rigorous approach to interpreting the test results.

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  1. Version published to 10.1101/2021.04.23.21255984v2 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2021.04.23.21255984: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot 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.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2021.04.23.21255984v1 on medRxiv