COVID-19 cluster size and transmission rates in schools from crowdsourced case reports
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.
Data availability
Code and data have been deposited in GitHub https://github.com/PaulFredTupper/covid-19-clusters-in-schools and Zenodo https://zenodo.org/record/7117270#.YzM0E-zMKjA
Article and author information
Author details
Funding
Natural Sciences and Engineering Research Council of Canada (RGPIN-2019-06911)
- Paul Tupper
Natural Sciences and Engineering Research Council of Canada (RGPIN-2019-06624)
- Caroline Colijn
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2022, Tupper et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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