COVID-19 Trends in Florida K-12 Schools, August 10 – November 14, 2020

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Abstract

Data collected from 38 states from August 3 – November 15, 2020 showed more than 250,000 confirmed student and staff cases of SARS-CoV-2 in K-12 schools 1 . Yet, analysis of COVID-19 case data in USA schools has been extremely limited 2,3 . To date, no large-scale or state-wide analyses by school level and grade has been published, opening a wide gap in understanding COVID-19 in American schools. A large-scale assessment of available data and trends could provide a baseline for understanding the virus in the K-12 learning environment and dispel misconceptions about the prevalence of COVID-19 in schools.

Table of Contents Summary

Using the most comprehensive database of K-12 COVID-19 case data in the country, Florida provides clues for understanding student and staff cases in schools.

What’s known on this subject

Florida schools began reopening to in-person instruction in August have reported more than 18,000 student and staff cases of COVID-19 as of November 14, 2020. Incidence of COVID-19 cases in K-12 students and staff is of urgent public health concern.

What this study adds

COVID-19 cases reported in Florida schools were most influenced by community case rates, district mask policies, and percent of students attending face-to-face. Student case rates were highest in high schools (12.5 per 1,000).

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  1. SciScore for 10.1101/2020.11.30.20241224: (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: 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.

  2. SciScore for 10.1101/2020.11.30.20241224: (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 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.


    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.