Little Risk of the COVID-19 Resurgence on Students in China (outside Hubei) Caused by School Reopening

  1. Cheng Long
  2. Tieyong Zeng
  3. Xinmiao Fu

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Abstract

Objective

School reopening has not yet started in China where the COVID-19 outbreak has reached ending stage, largely due to a great concern about COVID-19 infections on students. We attempted to quantitatively evaluate the risk of COVID-19 infections on students caused by school reopening.

Study design

We collected the data of the numbers of teachers, population size and newly confirmed COVID-19 cases in the past 14 days in typical provinces/cities of China, and then analyzed the risk of COVID-19 infections in schools with respect to each province/city.

Methods

A step-by-step procedure was explored to calculate the probability of COVID-19 infections on students as transmitted from infected teachers. Two critical assumptions for analysis were proposed: (i) only locally generated cases were counted while imported cases were omitted; (ii) the secondary attack rate of the COVID-19 virus in schools is similar to that in households in China, ranging from 3-10%.

Results

The probability of COVID-19 resurgence within one week on students of primary, middle and high schools in China (outside Hubei) is extremely low (<0.2%) in each province/city, and such probability can be updated daily and weekly based on the newly confirmed cases in the past 14 days. In some areas without newly confirmed cases in the past 14 days, the risk is zero.

Conclusions

Our work provides guidance for local governments to make risk level-based policies for school reopening. Currently, the risk of COVID-19 infections on students is extremely low in China (outside Hubei) and therefore school reopening can be initiated without the endanger of infections on students.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.04.20053645: (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: 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. Version published to 10.1101/2020.04.04.20053645v1 on medRxiv