Policy Interventions, Social Distancing, and SARS-CoV-2 Transmission in the United States: A Retrospective State-level Analysis

  1. Nickolas Dreher
  2. Zachary Spiera
  3. Fiona M. McAuley
  4. Lindsey Kuohn
  5. John R. Durbin
  6. Naoum Fares Marayati
  7. Muhammad Ali
  8. Adam Y. Li
  9. Theodore C. Hannah
  10. Alex Gometz
  11. JT Kostman
  12. Tanvir F. Choudhri

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Abstract

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  1. Version published to 10.1016/j.amjms.2021.01.007
  2. ScreenIT

    SciScore for 10.1101/2020.05.01.20088179: (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
    In order to better understand effects of NPI on social mobility, and the effects of social mobility on disease spread, social distancing metrics were collected from the COVID-19 community mobility reports made available by Google.
    Google
    suggested: (Google, RRID:SCR_017097)
    Covariates: We tested the association between five unique policy changes and the change in Rt: stay-at-home orders, school closures, closure of non-essential businesses, and bans on mass gatherings.
    Covariates
    suggested: None
    Statistical Analysis: All analyses were complete in R (Version 1.1.442) and Microsoft Excel.
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)

    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:
    Limitations: Our study has a number of important limitations to consider. First, our state-level analysis may miss variation at the county level. Individual counties may have implemented social distancing measures before mandated state-wide, thus states considered to lack certain policies at the time of 500 cases may in reality have been benefiting from more localized control. Similarly, county variation in COVID-19 cases, resulting deaths, population density, and other demographic factors were not accounted for. Future analyses should consider county-level data to account for these local variations. Our mobility results are further limited by potential flaws in Google’s publicly available phone data that this study relies on for mobility analyses. As noted by Lasry et al., data that tracks phones, not people, are subject to distortion by individuals with multiple devices and people leaving home without their phones.3 Further, these data do not differentiate between individuals leaving home but remaining distanced from others and people who ignore social distancing guidelines altogether while in public. Finally, our analysis focused exclusively on social distancing policies, and did not account for other transmission preventing NPI that states may have employed such as requiring masks. Lastly, though rates of testing have been noted to vary widely between states and serve as a potentially confounding variable, the model used to calculate Rt values analyzed here corrects for t...

    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.

  3. Version published to 10.1101/2020.05.01.20088179v1 on medRxiv