Modelling the impact of reopening schools in the UK in early 2021 in the presence of the alpha variant and with roll-out of vaccination against SARS-CoV-2

  1. J. Panovska-Griffiths
  2. R.M. Stuart
  3. C.C. Kerr
  4. K. Rosenfield
  5. D. Mistry
  6. W. Waites
  7. D.J. Klein
  8. C. Bonell
  9. R.M. Viner

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Abstract

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  1. Version published to 10.1016/j.jmaa.2022.126050
  4. Version published to 10.1101/2021.02.07.21251287v2 on medRxiv
  5. ScreenIT

    SciScore for 10.1101/2021.02.07.21251287: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    For this study, as in our previous work [7-8], we used Covasim’s default parameters, pre-populated demographic data on population age structures and household sizes and contact patterns in school, community and household setting for the UK, and the population stratified across four population contact network layers: schools, workplaces, households and community settings.
    Covasim’s
    suggested: None
    The level of reduction in transmission in households, workplaces and community that we modelled was a combination of using Google mobility data [23] within households, workplaces and community but also fitting to data during the calibration process.
    Google
    suggested: (Google, RRID:SCR_017097)

    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.

    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.

  6. Version published to 10.1101/2021.02.07.21251287v1 on medRxiv