ABSTRACT
The effect of school closure on the spread of COVID-19 has been discussed intensively in the literature and the news. To capture the interdependencies between children and adults, we consider daily age-stratified incidence data and contact patterns between age groups which change over time to reflect social distancing policy indicators. We fit a multivariate time-series endemic-epidemic model to such data from the Canton of Zurich, Switzerland and use the model to predict the age-specific incidence in a counterfactual approach (with and without school closures). The results indicate a 17% median increase of incidence in the youngest age group (0-14 year olds), whereas the relative increase in the other age groups drops to values between 2% and 3%. We argue that our approach is more informative to policy makers than summarising the effect of school closures with time-dependent effective reproduction numbers, which are difficult to estimate due to the sparsity of incidence counts within the relevant age groups.
ABSTRACT
Nunes et al. ([54]) provide an overview of mathematical models used to analyse epidemics and techniques for conducting studies to obtain parameter estimates for such models. They discuss the SEIR model which has been used in much coronavirus disease 2019 (COVID-19) analysis. Our discussion presents a modelling framework based in time series analysis developed for the analysis of infectious disease surveillance data, as well as our use of the framework in analysing COVID-19. We believe many of the purposes of modelling infectious disease outlined by Nunes et al. ([54]) as well as the benefits of mathematical modelling highlighted can also be found in the statistical modelling techniques we use in our work.