Associations of Stay-at-Home Order and Face-Masking Recommendation with Trends in Daily New Cases and Deaths of Laboratory-Confirmed COVID-19 in the United States

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Abstract

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  1. SciScore for 10.1101/2020.05.01.20088237: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The study was exempt from the review by an Institutional Review Board for the use of publicly available de-identified data.
    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:
    Strengths and weaknesses of this study: The major strength of this study is the use of State-based national data of laboratory-confirmed cases in the analyses.2 Moreover, two piece-wise log-linear regression methods were used to rigorously examine trends changes, according to the guidelines on trend analyses of population data and other methodological considerations.19-22 Further, the simulation studies provide comprehensive estimates of trends changes linked to early-implementation of SAHO at various time points and early-removal of SAHO with various extents. Several limitations of this study are noteworthy. First, the positive rates varied among states and by time, suggesting under-testing of the potential patients. The exact COVID-19 case numbers thus are not available, although efforts were made to estimate them using the Johns Hopkins data repository of COVID-19 cases.28 Given the data inconsistence we noticed,1 such an estimation in our view was not optimal. We were more confident in the reliability of the laboratory-confirmed case numbers. Inclusion of positive-test rate in our models may alleviate the variances in test rate cross the time. Second, there was a lag in COVID-19 reporting,29 which may lead to inaccurate estimation of the case numbers. However, the increases in the proportion of the tested population appeared stable in the U.S.2 It suggests the lag in reporting may not change significantly as the time changes, and will have minimal impact on the trend anal...

    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.