The mobility gap: estimating mobility thresholds required to control SARS-CoV-2 in Canada

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Abstract

No abstract available

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  1. SciScore for 10.1101/2021.01.28.21250622: (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: We detected the following sentences addressing limitations in the study:
    Our study has some limitations. First, our study did not examine granular patterns of mobility within provinces, limiting potential insights into the effectiveness of the regional approaches pursued in some provinces. Second, our study used comparative measures of mobility, relative to levels in January 2020, adding complexity to interprovincial comparisons. Third, this study lacked data on COVID-19 vaccination, first administered in Canada on December 14, 2020 and remaining below herd immunity levels in January 2021. As vaccination rates increase, these can be embedded into the model as an increasing mobility threshold. Meanwhile, other factors, such as the rapidly spreading variant arising from the United Kingdom and South Africa (30), may lead to a decreasing mobility threshold needed to control COVID-19. This study demonstrates that mobility strongly predicts COVID-19 case growth up to 3-weeks in the future, and that stringent measures will continue to be necessary through the winter 2021 months in Canada. The mobility threshold and mobility gap concepts developed here can be used by public health officials and governments to estimate the level of restrictions needed to control COVID-19 and guide, in real-time, the implementation and intensity of NPIs to control COVID-19.

    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 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.