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BMC Infect Dis . Endemic-epidemic modelling of school closure to prevent spread of COVID-19 in Switzerland

tetano

Editor, Senior Moderator
BMC Infect Dis


. 2024 Dec 2;24(1):1372.
doi: 10.1186/s12879-024-09674-6. Endemic-epidemic modelling of school closure to prevent spread of COVID-19 in Switzerland

M Bekker-Nielsen Dunbar[SUP] 1 [/SUP], F Hofmann[SUP] 2 [/SUP], S Meyer[SUP] #[/SUP][SUP] 3 [/SUP], L Held[SUP] #[/SUP][SUP] 4 [/SUP]



Affiliations
Abstract

The goal of this work was to quantify the effect of school closure during the first year of coronavirus disease 2019 (COVID-19) pandemic in Switzerland. This allowed us to determine the usefulness of school closures as a pandemic countermeasure for emerging coronaviruses in the absence of pharmaceutical interventions. The use of multivariate endemic-epidemic modelling enabled us to analyse disease spread between age groups which we believe is a necessary inclusion in any model seeking to achieve our goal. Sophisticated time-varying contact matrices encapsulating four different contact settings were included in our complex statistical modelling approach to reflect the amount of school closure in place on a given day. Using the model, we projected case counts under various transmission scenarios (driven by implemented social distancing policies). We compared these counterfactual scenarios against the true levels of social distancing policies implemented, where schools closed in the spring and reopened in the autumn. We found that if schools had been kept open, the vast majority of additional cases would be expected among primary school-aged children with a small fraction of cases filtering into other age groups following the contact matrix structure. Under this scenario where schools were kept open, the cases were highly concentrated among the youngest age group. In the scenario where schools had remained closed, most reduction would also be expected in the lowest age group with less effects seen in other groups.

Keywords: COVID-19; Endemic–epidemic modelling; School closure; Social contacts; Surveillance data.

 
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