publication . Preprint . Article . 2021

Modelling safe protocols for reopening schools during the COVID-19 pandemic in France

Laura Di Domenico; Giulia Pullano; Chiara E. Sabbatini; Pierre-Yves Boëlle; Vittoria Colizza;
Open Access English
  • Published: 01 Dec 2021 Journal: Nature Communications, volume 12 (eissn: 2041-1723, Copyright policy)
  • Publisher: Nature Publishing Group UK
  • Country: France
Abstract
As countries in Europe implement strategies to control the COVID-19 pandemic, different options are chosen regarding schools. Through a stochastic age-structured transmission model calibrated to the observed epidemic in Île-de-France in the first wave, we explored scenarios of partial, progressive, or full school reopening. Given the uncertainty on children’s role, we found that reopening schools after lockdown may increase COVID-19 cases, yet protocols exist to keep the epidemic controlled. Under a scenario with stable epidemic activity if schools were closed, reopening pre-schools and primary schools would lead to up to 76% [67, 84]% occupation of ICU beds if no other school level reopened, or if middle and high schools reopened later. Immediately reopening all school levels may overwhelm the ICU system. Priority should be given to pre- and primary schools allowing younger children to resume learning and development, whereas full attendance in middle and high schools is not recommended for stable or increasing epidemic activity. Large-scale test and trace is required to keep the epidemic under control. Ex-post assessment shows that progressive reopening of schools, limited attendance, and strong adoption of preventive measures contributed to a decreasing epidemic after lifting the first lockdown.
The role of children in the spread of COVID-19 is not fully understood, and the circumstances under which schools should be opened are therefore debated. Here, the authors demonstrate protocols by which schools in France can be safely opened without overwhelming the healthcare system.
Subjects
Medical Subject Headings: educationgenetic structuresendocrine system
free text keywords: Article, Computational models, SARS-CoV-2, Epidemiology, Computational science, [SDV]Life Sciences [q-bio], Science, Q, General Physics and Astronomy, General Biochemistry, Genetics and Molecular Biology, General Chemistry, Pandemic, School level, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Attendance, Demography, Political science, Coronavirus disease 2019 (COVID-19), Test (assessment), 2019-20 coronavirus outbreak
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Funded by
ANR| DataRedux
Project
DataRedux
Big Data reduction for predictive computational modeling
  • Funder: French National Research Agency (ANR) (ANR)
  • Project Code: ANR-19-CE46-0008
,
EC| MOOD
Project
MOOD
MOnitoring Outbreak events for Disease surveillance in a data science context
  • Funder: European Commission (EC)
  • Project Code: 874850
  • Funding stream: H2020 | RIA
Validated by funder
,
ANR| SPHINx
Project
SPHINx
Spread of Pathogens on Healthcare Institutions Networks: a modeling study
  • Funder: French National Research Agency (ANR) (ANR)
  • Project Code: ANR-17-CE36-0008

This study is partially funded by: ANR projects SPHINX (ANR-17-CE36-0008-05) and DATAREDUX (ANR-19-CE46-0008-03); EU H2020 grants RECOVER (H2020- 101003589) and MOOD (H2020-874850); REACTing COVID-19 modeling grant; INSERM-INRIA partnership on data science and public health. We thank Chiara Poletto, Alain Barrat, Juliette Paireau, and Santé publique France for useful discussions.

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