Factors affecting the transmission of SARS‐CoV‐2 in school settings

  1. Haokun Yuan
  2. Connor Reynolds
  3. Sydney Ng
  4. Wan Yang

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Abstract

Background

Several studies have reported SARS‐CoV‐2 outbreaks in schools, with a wide range of secondary attack rate (SAR; range: 0–100%). We aimed to examine key risk factors to better understand SARS‐CoV‐2 transmission in schools.

Methods

We collected records of 35 SARS‐CoV‐2 school outbreaks globally published from January 2020 to July 2021 and compiled information on hypothesized risk factors. We utilized the directed acyclic graph (DAG) to conceptualize risk mechanisms, used logistic regression to examine each risk‐factor group, and further built multirisk models.

Results

The best‐fit model showed that the intensity of community transmission (adjusted odds ratio [aOR]: 1.11, 95% CI: 1.06–1.16, for each increase of 1 case per 10 000 persons per week) and individualism (aOR: 2.72, 95% CI: 1.50–4.95, above vs. below the mean) was associated higher risk, whereas preventive measures (aOR: 0.25, 95% CI: 0.19–0.32, distancing and masking vs. none) and higher population immunity (aOR: 0.57, 95% CI: 0.46–0.71) were associated with lower risk of SARS‐CoV‐2 transmission in schools. Compared with students in high schools, the aOR was 0.47 (95% CI: 0.23–0.95) for students in preschools and 0.90 (95% CI: 0.76–1.08) for students in primary schools.

Conclusions

Preventive measures in schools (e.g., social distancing and mask wearing) and communal efforts to lower transmission and increase vaccination uptake (i.e., vaccine‐induced population immunity) in the community should be taken to collectively reduce transmission and protect children in schools.

Article activity feed

  1. Version published to 10.1111/irv.12968
  2. Version published to 10.1101/2021.06.18.21259156v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2021.06.18.21259156: (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
    Data sources: Studies were searched for on the “Living Evidence for COVID-19” database [24], which retrieves articles from EMBASE via Ovid, PubMed, BioRxiv, and MedRxiv.
    EMBASE
    suggested: (EMBASE, RRID:SCR_001650)
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    BioRxiv
    suggested: (bioRxiv, RRID:SCR_003933)
    The best performing model took the following form:

    All statistical analyses were performed in RStudio, a user interface for R (R Foundation for Statistical Computing, Vienna, Austria).

    RStudio
    suggested: (RStudio, RRID:SCR_000432)

    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:
    This study has several limitations. First, the analyses included a limited number of studies (n = 26), due to the inclusion criteria and the dearth of published reports on this topic to date. Future research with additional relevant studies may enhance the statistical power of inference for the risk factors examined here. Second, due to a lack of detailed information for each specific school setting, we used proxy measures in the analyses (e.g., class size at the national level was used rather than for each reporting school), which may have limited the ability of the models to identify the association of these factors with SARS-CoV-2 transmission risk. Similarly, due to the lack of data, we were not able to examine other key factors such as ventilation in classrooms, social economic status of individual students and their households, and potential differences in susceptibility and transmissibility by age group. Future work with comprehensive study designs and data collection is warranted to provide further insights into how infections, not limited to SARS-CoV-2, spread in schools and the broad, bi-directional impact of school and community transmission. This would be invariable to inform better strategies to combat future infectious disease outbreaks.

    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.

    Results from scite Reference Check: We found no unreliable references.

    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.

  4. Version published to 10.1101/2021.06.18.21259156v1 on medRxiv