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Assessing the effect of school closures on the spread of COVID‐19 in Zurich

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  • Maria Bekker‐Nielsen Dunbar
  • Felix Hofmann
  • Leonhard Held
  • the SUSPend modelling consortium

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.

Suggested Citation

  • Maria Bekker‐Nielsen Dunbar & Felix Hofmann & Leonhard Held & the SUSPend modelling consortium, 2022. "Assessing the effect of school closures on the spread of COVID‐19 in Zurich," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S1), pages 131-142, November.
    • Handle: RePEc:bla:jorssa:v:185:y:2022:i:s1:p:s131-s142
    DOI: 10.1111/rssa.12910
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    References listed on IDEAS

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    1. Cici Bauer & Jon Wakefield, 2018. "Stratified space–time infectious disease modelling, with an application to hand, foot and mouth disease in China," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1379-1398, November.
    2. B. F. Finkenstädt & B. T. Grenfell, 2000. "Time series modelling of childhood diseases: a dynamical systems approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(2), pages 187-205.
    3. Katelyn M Gostic & Lauren McGough & Edward B Baskerville & Sam Abbott & Keya Joshi & Christine Tedijanto & Rebecca Kahn & Rene Niehus & James A Hay & Pablo M De Salazar & Joel Hellewell & Sophie Meaki, 2020. "Practical considerations for measuring the effective reproductive number, Rt," PLOS Computational Biology, Public Library of Science, vol. 16(12), pages 1-21, December.
    4. Bracher, Johannes & Held, Leonhard, 2022. "Endemic-epidemic models with discrete-time serial interval distributions for infectious disease prediction," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1221-1233.
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