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Mitigating COVID-19 outbreaks in workplaces and schools by hybrid telecommuting
- Source :
- 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)
- Publication Year :
- 2021
- Publisher :
- HAL CCSD, 2021.
-
Abstract
- 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.<br />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.
- Subjects :
- 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
Subjects
Details
- Language :
- English
- ISSN :
- 1553734X and 15537358
- Database :
- OpenAIRE
- Journal :
- 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)
- Accession number :
- edsair.doi.dedup.....af6ebec1cc5aa38d6e66d04cd131b4d8
- Full Text :
- https://doi.org/10.1371/journal.pcbi.1009264⟩