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Title
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Reconstruction of SARS-CoV-2 outbreaks in a primary school using epidemiological and genomic data
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Author
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Abstract
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| Mathematical modelling studies have shown that repetitive screening can be used to mitigate SARS-CoV2 transmission in primary schools while keeping schools open. However, not much is known about how transmission progresses within schools and whether there is a risk of importation to households. During the academic year 2020-2021, a prospective surveillance study using repetitive screening was conducted in a primary school and associated households in Liege (Belgium). SARS-CoV-2 screening was performed via throat washing either once or twice a week. We used genomic and epidemiological data to reconstruct the observed school outbreaks using two different models. The outbreaker2 model combines information on the generation time and contact patterns with a model of sequence evolution. For comparison we also used SCOTTI, a phylogenetic model based on the structured coalescent. In addition, we performed a simulation study to investigate how the accuracy of estimated positivity rates in a school depends on the proportion of a school that is sampled in a repetitive screening strategy. We found no difference in SARS-CoV-2 positivity between children and adults and children were not more often asymptomatic compared to adults. Both models for outbreak reconstruction revealed that transmission occurred mainly within the school environment. Uncertainty in outbreak reconstruction was lowest when including genomic as well as epidemiological data. We found that observed weekly positivity rates are a good approximation to the true weekly positivity rate, especially in children, even when only 25% of the school population is sampled. These results indicate that, in addition to reducing infections as shown in modelling studies, repetitive screening in school settings can lead to a better understanding of the extent of transmission in schools during a pandemic and importation risk at the community level. |
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Language
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English
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Source (journal)
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Epidemics
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Publication
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2023
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ISSN
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1755-4365
1878-0067
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DOI
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10.1016/J.EPIDEM.2023.100701
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Volume/pages
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44
(2023)
, p. 1-10
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Article Reference
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100701
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ISI
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001053794500001
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Pubmed ID
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37379776
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Medium
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E-only publicatie
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Full text (Publisher's DOI)
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Full text (open access)
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