The limits of estimating COVID-19 intervention effects using Bayesian models

  1. Patrick Bryant
  2. Arne Elofsson

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Abstract

To limit the rapid spread of COVID-19, most governments have introduced different non-pharmaceutical interventions, which might have severe costs for society. Therefore, it is crucial to evaluate the most cost-effective interventions, using, for instance, Bayesian modelling. Such modelling efforts have deemed lockdown to account for 81% of the reduction in R​ 0 , contributing to government policies. Here, we show that these conclusions are unsupported and that policies therefore should not be based on these studies.

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  1. ScreenIT

    SciScore for 10.1101/2020.08.14.20175240: (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: Thank you for sharing your code and data.

    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.

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  2. Version published to 10.1101/2020.08.14.20175240v1 on medRxiv