Ranking the effectiveness of worldwide COVID-19 government interventions

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Abstract

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  1. SciScore for 10.1101/2020.07.06.20147199: (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: We detected the following sentences addressing limitations in the study:
    Strengths & Limitations: The assessment of the effectiveness of NPIs is statistically challenging, as measures were typically implemented simultaneously and because their impact might well depend on the particular implementation sequence. Some NPIs appear in almost all countries whereas others only in few, meaning that we could miss some rare but effective measures due to a lack of statistical power. While some methods might be prone to overestimating effects from an NPI due to insufficient adjustments for confounding effects from other measures, other methods might underestimate the contribution of an NPI by assigning its impact to a highly correlated NPI. As a consequence, estimates of ΔRt might vary substantially across different methods, whereas the agreement on the significance of individual NPIs is much more pronounced. The strength of our study, therefore, lies in the harmonization of these four independent methodological approaches, combined with the usage of an extensive data set on NPIs. This allows us to estimate the structural uncertainty of NPI effectiveness, i.e., the uncertainty introduced by choosing a certain model structure. Moreover, whereas previous studies often subsumed a wide range of social distancing and travel restriction measures under a single entity, our analysis contributes to a more fine-grained understanding of each NPI. The CCCSL data set features non-homogeneous data completeness across the different territories and data collection could be b...

    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.
    • Thank you for including a protocol registration statement.

    About SciScore

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