Simulating respiratory disease transmission within and between classrooms to assess pandemic management strategies at schools

CMMID COVID-19 Working Group, Akira Endo 遠藤彰, Mitsuo Uchida 内田満夫, Yang Liu 刘扬, Katherine E Atkins, Adam J Kucharski, Sebastian Funk

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

The global spread of coronavirus disease 2019 (COVID-19) has emphasized the need for evidence-based strategies for the safe operation of schools during pandemics that balance infection risk with the society's responsibility of allowing children to attend school. Due to limited empirical data, existing analyses assessing school-based interventions in pandemic situations often impose strong assumptions, for example, on the relationship between class size and transmission risk, which could bias the estimated effect of interventions, such as split classes and staggered attendance. To fill this gap in school outbreak studies, we parameterized an individual-based model that accounts for heterogeneous contact rates within and between classes and grades to a multischool outbreak data of influenza. We then simulated school outbreaks of respiratory infectious diseases of ongoing threat (i.e., COVID-19) and potential threat (i.e., pandemic influenza) under a variety of interventions (changing class structures, symptom screening, regular testing, cohorting, and responsive class closures). Our results suggest that interventions changing class structures (e.g., reduced class sizes) may not be effective in reducing the risk of major school outbreaks upon introduction of a case and that other precautionary measures (e.g., screening and isolation) need to be employed. Class-level closures in response to detection of a case were also suggested to be effective in reducing the size of an outbreak.

Original languageEnglish
Pages (from-to)e2203019119
JournalProceedings of the National Academy of Sciences (PNAS)
Issue number37
Publication statusPublished - 13 Sept 2022

Keywords / Materials (for Non-textual outputs)

  • COVID-19/epidemiology
  • Child
  • Disease Outbreaks/prevention & control
  • Humans
  • Influenza, Human/epidemiology
  • Pandemics/prevention & control
  • Schools


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