Mitigating COVID-19 outbreaks in workplaces and schools by hybrid telecommuting
| Autor: | Paolo Frasca, Claire Mathieu, Vincent Cohen-Addad, Laurent Viennot, Lulla Opatowski, Simon Mauras, Guillaume Duboc, Max Dupré la Tour |
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| Přispěvatelé: | Institut de Recherche en Informatique Fondamentale (IRIF (UMR_8243)), Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Recherche Opérationnelle (RO), LIP6, Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), École normale supérieure - Lyon (ENS Lyon), Dynamics and Control of Networks (DANCE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GIPSA Pôle Automatique et Diagnostic (GIPSA-PAD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Centre de recherche en épidémiologie et santé des populations (CESP), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria), LO received funding from the Fondation de France (grant 106059) as part of the alliance framework 'Tous unis contre le virus', and the Université Paris-Saclay (AAP Covid-19 2020), C.M. received research funding from the 'Fonds d’urgence MESRI Covid19', https://www. enseignementsup-recherche.gouv.fr/, and from the French National Research Agency (grant ANR-19-CE48-0016)., ANR-19-CE48-0016,AlgoriDAM,Théorie algorithmique de nouveaux modèles de données(2019), Gestionnaire, HAL Sorbonne Université 5, Théorie algorithmique de nouveaux modèles de données - - AlgoriDAM2019 - ANR-19-CE48-0016 - AAPG2019 - VALID, École normale supérieure de Lyon (ENS de Lyon), Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
RNA viruses
Viral Diseases Time Factors Computer science Epidemiology Coronaviruses Psychological intervention Basic Reproduction Number Social Sciences Infographics Disease Outbreaks law.invention Social group Random Graphs Medical Conditions 0302 clinical medicine Sociology Telecommuting law Medicine and Health Sciences 030212 general & internal medicine Biology (General) Marketing Workplace Pathology and laboratory medicine Data Management 0303 health sciences Schools Ecology 4. Education Medical microbiology 3. Good health Infectious Diseases Geography Transmission (mechanics) Computational Theory and Mathematics Work (electrical) Modeling and Simulation Viruses France Public Health SARS CoV 2 Pathogens Graphs Research Article medicine.medical_specialty Computer and Information Sciences SARS coronavirus QH301-705.5 Control (management) Immunology Personnel Staffing and Scheduling [INFO] Computer Science [cs] Network topology Models Biological Microbiology Education Education Distance Cellular and Molecular Neuroscience 03 medical and health sciences Genetics medicine Humans Computer Simulation [INFO]Computer Science [cs] Baseline (configuration management) Molecular Biology Ecology Evolution Behavior and Systematics 030304 developmental biology Stochastic Processes Biology and life sciences SARS-CoV-2 Public health Data Visualization Teleworking Organisms Viral pathogens Immunity COVID-19 Computational Biology Outbreak Covid 19 Microbial pathogens Ranking Medical Risk Factors Demographic economics Contact Tracing Basic reproduction number |
| Zdroj: | PLoS Computational Biology PLoS Computational Biology, 2021, 17 (8), pp.1-24. ⟨10.1371/journal.pcbi.1009264⟩ PLoS Computational Biology, Public Library of Science, 2021, 17 (8), pp.1-24. ⟨10.1371/journal.pcbi.1009264⟩ PLoS Computational Biology, Vol 17, Iss 8, p e1009264 (2021) |
| ISSN: | 1553-734X 1553-7358 |
| DOI: | 10.1371/journal.pcbi.1009264⟩ |
| Popis: | The COVID-19 epidemic has forced most countries to impose contact-limiting restrictions at workplaces, universities, schools, and more broadly in our societies. Yet, the effectiveness of these unprecedented interventions in containing the virus spread remain largely unquantified. Here, we develop a simulation study to analyze COVID-19 outbreaks on three real-life contact networks stemming from a workplace, a primary school and a high school in France. Our study provides a fine-grained analysis of the impact of contact-limiting strategies at workplaces, schools and high schools, including: (1) Rotating strategies, in which workers are evenly split into two shifts that alternate on a daily or weekly basis; and (2) On-Off strategies, where the whole group alternates periods of normal work interactions with complete telecommuting. We model epidemics spread in these different setups using a stochastic discrete-time agent-based transmission model that includes the coronavirus most salient features: super-spreaders, infectious asymptomatic individuals, and pre-symptomatic infectious periods. Our study yields clear results: the ranking of the strategies, based on their ability to mitigate epidemic propagation in the network from a first index case, is the same for all network topologies (workplace, primary school and high school). Namely, from best to worst: Rotating week-by-week, Rotating day-by-day, On-Off week-by-week, and On-Off day-by-day. Moreover, our results show that below a certain threshold for the original local reproduction number R0local within the network (< 1.52 for primary schools, < 1.30 for the workplace, < 1.38 for the high school, and < 1.55 for the random graph), all four strategies efficiently control outbreak by decreasing effective local reproduction number to R0local < 1. These results can provide guidance for public health decisions related to telecommuting. Author summary The COVID-19 epidemics has forced most countries to impose prolonged contact-limiting restrictions at workplaces, universities, schools. Using simulation and taking into account the most salient epidemiological features of SARS-CoV-2, we analyze the risk of outbreak and the impact of contact-limiting strategies on three real-life contact networks stemming from a workplace, a primary school and a high school. The strategies investigated involve (1) Rotation, in which workers are evenly split into two shifts that alternate on a daily or weekly basis; and (2) On-Off, where the whole group alternates periods of normal work interactions with complete telecommuting. Our study yields clear results, whatever the studied network (workplace, primary school and high school), we find that, from best to worst: Rotating week-by-week, Rotating day-by-day, On-Off week-by-week, and On-Off day-by-day can all help mitigate transmission below a certain epidemicity threshold. In the current context where institutions and companies have to quickly take local organizational decisions and review their planning or agendas, our results should help inform public health decisions. |
| Databáze: | OpenAIRE |
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