Comparing SARS-CoV-2 case rates between pupils, teachers and the general population: results from Germany

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Abstract

Given the inconsistent state of research regarding the role of pupils and teachers during the SARS-CoV-2 pandemic in Germany, statewide and nationwide data of infection case rates were analyzed to contribute to the discourse. Infection data from official sources ranging from mid to late 2020 were collected, prepared and analyzed to answer the question if pupils, teachers and general population differed in active case rates or not. The data showed that pupils and teachers case rates didn’t exceeded those of the general population. In conclusion, it seems appropriate to appraise school-related measures to mitigate the SARS-CoV-2 pandemic sufficiently. Data quality is a yet to overcome obstacle to provide good evidence-based recommendations regarding the management around infection cases in schools.

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  1. SciScore for 10.1101/2021.03.04.21252877: (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:
    With regard to potential limitations, it would have been preferable to be able to use higher quality data for state- and nationwide SARS-CoV-2 cases of pupils and teachers, as documentation procedures changed during the analyzed period. Furthermore, during the period of the Autumn vacation, the documentation of SARS-CoV-2 cases was not continued, cases have been artificially set to zero in the BM-RLP dataset. Another important limitation of our results is that the dropout algorithm for the subtraction of recovered SARS-CoV-2 cases from active cases used for the general population data (RKI) differs from the documentation of school-based datasets (pupils and teachers). In the school-based datasets active SARS-CoV-2 cases would drop out if the pupil or teacher continues to go to school (or uses a digital alternative) whereas for the general population dataset an algorithm estimates dropouts (e.g., standard-dropout 14 days after a positive test, dropout after four weeks for cases with pneumonia). Consequently, results should be interpreted with caution. Despite those limitations we conclude to reconsider school-related measures when schools will be opened again in order to mitigate the SARS-CoV-2 pandemic. In order to improve data quality, we encourage statewide officials to implement nationwide consistent methods of tracking and reporting SARS-CoV-2 infection cases of pupils and teachers. This would enable politicians in charge to better evidence-based decisions for the protect...

    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.