Endemic-epidemic modelling of school closure to prevent spread of COVID-19 in Switzerland
Bekker-Nielsen Dunbar, M.; Hofmann, F.; Meyer, S.; Held, L.
Show abstract
The coronavirus disease 2019 (COVID-19) pandemic disrupted daily life and changes to routines were made in accordance with public health regulations. In 2020, nonpharmaceutical interventions were put in place to reduce exposure to and spread of the disease. The goal of this work was to quantify the effect of school closure during the first year of 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 percolating 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.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- The COVID-19 vaccination campaign in Switzerland and its impact on disease spread 98%
- Assessing the effects of non-pharmaceutical interventions on SARS-CoV-2 transmission in Belgium by means of an extended SEIQRD model and public mobility data 97%
- Modeling the early phase of the Belgian COVID-19 epidemic using a stochastic compartmental model and studying its implied future trajectories 96%
Similar papers in this journal
- A novel, scenario-based approach to comparing non-pharmaceutical intervention strategies across nations 95%
- Modelling the impact of household size distribution on the transmission dynamics of COVID-19 95%
- A semi-parametric, state-space compartmental model with time-dependent parameters for forecasting COVID-19 cases, hospitalizations, and deaths 94%
Similar papers in this journal
- Inferring age-specific differences in susceptibility to and infectiousness upon SARS-CoV-2 infection based on Belgian social contact data 96%
- Estimation of introduction and transmission rates of SARS-CoV-2 in a prospective household study 95%
- A mechanistic and data-driven reconstruction of the time-varying reproduction number: Application to the COVID-19 epidemic 95%
Similar papers in this journal
- Pharmaceutical and Non-Pharmaceutical Interventions for Controlling the COVID-19 Pandemic 95%
- Optimal health and economic impact of non-pharmaceutical intervention measures prior and post vaccination in England: a mathematical modelling study 95%
- School and community reopening during the COVID-19 pandemic: a mathematical modeling study 95%
Similar papers in this journal
- The Effect of Gender on Covid-19 Infections and Mortality in Germany: Insights From Age- and Sex-Specific Modelling of Contact Rates, Infections, and Deaths 97%
- The effectiveness of Non Pharmaceutical Interventions in reducing the outcomes of the COVID-19 epidemic in the UK, an observational and modelling study 96%
- Data Driven High Resolution Modeling and Spatial Analyses of the COVID-19 Pandemic in Germany 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.