COVID-19 cluster size and transmission rates in schools from crowdsourced case reports

Curation statements for this article:
  • Curated by eLife

    eLife logo

    Evaluation Summary:

    This paper is the first to characterize overdispersion of COVID-19 spread in schools using crowdsourcing . It has the potential to serve as a useful platform for assessing preventative measures in schools but needs more clarity regarding the sensitivity of the approach to the completeness of input data, as evidence by different model conclusions when sparse data from the US is used as an input as opposed to the more detailed Canadian data.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)

This article has been Reviewed by the following groups

Read the full article See related articles

Abstract

The role of schools in the spread of SARS-CoV-2 is controversial, with some claiming they are an important driver of the pandemic and others arguing that transmission in schools is negligible. School cluster reports that have been collected in various jurisdictions are a source of data about transmission in schools. These reports consist of the name of a school, a date, and the number of students known to be infected. We provide a simple model for the frequency and size of clusters in this data, based on random arrivals of index cases at schools who then infect their classmates with a highly variable rate, fitting the overdispersion evident in the data. We fit our model to reports from four Canadian provinces, providing estimates of mean and dispersion for cluster size, as well as the distribution of the instantaneous transmission parameter β , whilst factoring in imperfect ascertainment. According to our model with parameters estimated from the data, in all four provinces (i) more than 65% of non-index cases occur in the 20% largest clusters, and (ii) reducing instantaneous transmission rate and the number of contacts a student has at any given time are effective in reducing the total number of cases, whereas strict bubbling (keeping contacts consistent over time) does not contribute much to reduce cluster sizes. We predict strict bubbling to be more valuable in scenarios with substantially higher transmission rates.

Article activity feed

  1. Author Response

    Reviewer #1 (Public Review):

    There are several key weaknesses. As the authors describe honestly and thoroughly, the high potential for misclassification of clusters is a real limitation. This is likely to be of higher relevance for the US data. Perhaps this is too subjective on my part, but the Canadian data seems likely to be more complete and less biased, particularly in terms of including singlet events with n=1 case. It also strains belief that the true cluster distribution would differ markedly between Canada and the US based on overlapping demographics, culture, class size, etc....For this reason, I would favor labeling the Canadian data as more representative of reality and interpreting the analysis accordingly. It seems fair to use the US data as a likely surrogate of what occurs when the model is applied to …

  2. Evaluation Summary:

    This paper is the first to characterize overdispersion of COVID-19 spread in schools using crowdsourcing . It has the potential to serve as a useful platform for assessing preventative measures in schools but needs more clarity regarding the sensitivity of the approach to the completeness of input data, as evidence by different model conclusions when sparse data from the US is used as an input as opposed to the more detailed Canadian data.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)

  3. Reviewer #1 (Public Review):

    This is an innovative and interesting study using crowd sourced data to estimate overdispersion of COVID-19 clusters in US & Canadian classrooms. The writing is exceedingly clear and the linkage of the observed overdispersion of case clusters with specific classroom prevention strategies in Figure 7 is potentially extremely useful. I particularly appreciate that the authors very clearly state the limitations of the cluster size data in terms of ascertainment. The writing in these sections is excellent. Overall, the study thoughtfully addresses an important public healthy question with a novel and perhaps less expensive method. The study achieved its aims with caveats listed below.

    There are several key weaknesses. As the authors describe honestly and thoroughly, the high potential for misclassification of …

  4. Reviewer #2 (Public Review):

    This manuscript has a number of strengths. First, the paper concerns a topic of considerable importance and interest as a safe return to in person education will be critical for safe reopening of societies. Second, there have been limited analyses of school transmission clusters, and they present the opportunity to better understand school transmission risks. Finally, the authors integrate an analysis that estimates transmission risk with a previous infection risk framework, which provides actionable guidance to school administrators concerning the most effective mitigation measures.

    However, there are a number of weaknesses in the analysis. First, the manuscript relies on reported cluster data rather than systematically collected datasets. This causes issues related to reporting biases such as differences …

  5. Reviewer #3 (Public Review):

    The strength of this paper lies in its simplicity. The authors have, as above, fitted simple negative binomial models to available school outbreak case distributions. The sensitivity analysis in which plausible variation in ascertainment fraction does relatively little to cluster size estimates is also important.

    More exploration of mechanisms underlying Canada-US differences would be helpful.

  6. SciScore for 10.1101/2021.12.07.21267381: (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
    The US data was gathered from the National Educational Association website (4) (originally started by Alisha Morris, an educator at a Kansas high school) which collected data from news media and from reports submitted by volunteers (39).
    National Educational Association
    suggested: None

    Results from OddPub: Thank you for sharing your code and data.


    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our data and model have some limitations. The data rely on crowdsourcing, and there is reason to believe that reporting is incomplete. …