publication . Article . Preprint . 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
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 ...
Medical Subject Headings: educationgenetic structuresendocrine system
free text keywords: Article, Computational models, SARS-CoV-2, Epidemiology, Computational science, [SDV]Life Sciences [q-bio], General Physics and Astronomy, General Biochemistry, Genetics and Molecular Biology, General Chemistry, lcsh:Science, lcsh:Q, Progressive education, Attendance, Medical education, Political science, School level, Coronavirus disease 2019 (COVID-19)
  • COVID-19
Funded by
ANR| DataRedux
Big Data reduction for predictive computational modeling
  • Funder: French National Research Agency (ANR) (ANR)
  • Project Code: ANR-19-CE46-0008
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
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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