Model-based evaluation of school- and non-school-related measures to control the COVID-19 pandemic



Rozhnova, Ganna, van Dorp, Christiaan H, Bruijning-Verhagen, Patricia, Bootsma, Martin CJ, van de Wijgert, Janneke HHM, Bonten, Marc JM and Kretzschmar, Mirjam E
(2021) Model-based evaluation of school- and non-school-related measures to control the COVID-19 pandemic. Nature Communications.

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Abstract

<jats:p>Background: In autumn 2020, many countries, including the Netherlands, are experiencing a second wave of the COVID-19 pandemic. Health policymakers are struggling with choosing the right mix of measures to keep the COVID-19 case numbers under control, but still allow a minimum of social and economic activity. The priority to keep schools open is high, but the role of school-based contacts in the epidemiology of SARS-CoV-2 is incompletely understood. We used a transmission model to estimate the impact of school contacts on the transmission of SARS-CoV-2 and to assess the effects of school-based measures, including school closure, on controlling the pandemic at different time points during the pandemic. Methods and Findings: The age-structured model was fitted to age-specific seroprevalence and hospital admission data from the Netherlands during spring 2020. Compared to adults older than 60 years, the estimated susceptibility was 23% (95%CrI 20-28%) for children aged 0 to 20 years and 61% (95%CrI 50%-72%) for the age group of 20 to 60 years. The time points considered in the analyses were (i) August 2020 when the effective reproduction number (R_e) was estimated to be 1.31 (95%CrI 1.15-2.07), schools just opened after the summer holidays and measures were reinforced with the aim to reduce R_e to a value below 1, and (ii) November 2020 when measures had reduced R_e to 1.00 (95%CrI 0.94-1.33). In this period schools remained open. Our model predicts that keeping schools closed after the summer holidays, in the absence of other measures, would have reduced R_e by 10% (from 1.31 to 1.18 (95%CrI 1.04-1.83)) and thus would not have prevented the second wave in autumn 2020. Reducing non-school-based contacts in August 2020 to the level observed during the first wave of the pandemic would have reduced R_e to 0.83 (95%CrI 0.75-1.10). Yet, this reduction was not achieved and the observed R_e in November was 1.00. Our model predicts that closing schools in November 2020 could reduce R_e from the observed value of 1.00 to 0.84 (95%CrI 0.81-0.90), with unchanged non-school based contacts. Reductions in R_e due to closing schools in November 2020 were 8% for 10 to 20 years old children, 5% for 5 to 10 years old children and negligible for 0 to 5 years old children. Conclusions: The impact of measures reducing school-based contacts, including school closure, depends on the remaining opportunities to reduce non-school-based contacts. If opportunities to reduce R_e with non-school-based measures are exhausted or undesired and R_e is still close to 1, the additional benefit of school-based measures may be considerable, particularly among the older school children.</jats:p>

Item Type: Article
Divisions: Faculty of Health and Life Sciences
Faculty of Health and Life Sciences > Institute of Infection, Veterinary and Ecological Sciences
Depositing User: Symplectic Admin
Date Deposited: 30 Mar 2021 13:53
Last Modified: 12 Jun 2021 23:11
DOI: 10.1038/s41467-021-21899-6
URI: https://livrepository.liverpool.ac.uk/id/eprint/3118217

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