bibtype J - Journal Article
ARLID 0578058
utime 20240402214726.9
mtime 20231114235959.9
SCOPUS 85175848114
WOS 001108929300050
DOI 10.1038/s41598-023-45788-8
title (primary) (eng) Rotation-based schedules in elementary schools to prevent COVID-19 spread: a simulation study
specification
page_count 9 s.
media_type E
serial
ARLID cav_un_epca*0386594
ISSN 2045-2322
title Scientific Reports
volume_id 13
publisher
name Nature Publishing Group
keyword agent-based model
keyword covid-19
keyword epidemiological modelling
keyword SEIR
author (primary)
ARLID cav_un_auth*0222657
name1 Brom
name2 C.
country CZ
author
ARLID cav_un_auth*0449183
name1 Diviák
name2 T.
country CZ
author
ARLID cav_un_auth*0051505
name1 Drbohlav
name2 J.
country CZ
author
ARLID cav_un_auth*0363657
name1 Korbel
name2 Václav
institution NHU-N
full_dept Economics Institute
fullinstit Národohospodářský ústav AV ČR, v. v. i.
author
ARLID cav_un_auth*0106308
name1 Levínský
name2 René
institution NHU-N
fullinstit Národohospodářský ústav AV ČR, v. v. i.
author
ARLID cav_un_auth*0100794
name1 Neruda
name2 Roman
institution UIVT-O
full_dept (cz) Oddělení umělé inteligence
full_dept Department of Artificial Intelligence
full_dept Department of Machine Learning
fullinstit Ústav informatiky AV ČR, v. v. i.
author
ARLID cav_un_auth*0434902
name1 Kadlecová
name2 Gabriela
institution UIVT-O
full_dept (cz) Oddělení strojového učení
full_dept Department of Machine Learning
country CZ
fullinstit Ústav informatiky AV ČR, v. v. i.
author
ARLID cav_un_auth*0259020
name1 Šlerka
name2 J.
country CZ
author
ARLID cav_un_auth*0101206
name1 Šmíd
name2 Martin
institution UTIA-B
full_dept (cz) Ekonometrie
full_dept Department of Econometrics
department (cz) E
department E
full_dept Department of Econometrics
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0091778
name1 Trnka
name2 J.
country CZ
garant K
author
ARLID cav_un_auth*0231277
name1 Vidnerová
name2 Petra
institution UIVT-O
full_dept (cz) Oddělení umělé inteligence
full_dept Department of Artificial Intelligence
full_dept Department of Machine Learning
fullinstit Ústav informatiky AV ČR, v. v. i.
source
url https://doi.org/10.1038/s41598-023-45788-8
cas_special
abstract (eng) Rotations of schoolchildren were considered as a non-pharmacological intervention in the COVID-19 pandemic. This study investigates the impact of different rotation and testing schedules. We built an agent-based model of interactions among pupils and teachers based on a survey in an elementary school in Prague, Czechia. This model contains 624 schoolchildren and 55 teachers and about 27 thousands social contacts in 10 layers. The layers reflect different types of contacts (classroom, cafeteria, etc.) in the survey. On this multi-graph structure we run a modified SEIR model of covid-19 infection. The parameters of the model are calibrated on data from the outbreak in the Czech Republic in spring 2020. Weekly rotations of in-class and distance learning are an effective preventative measure in schools reducing the spread of covid-19 by 75–81% . Antigen testing twice a week or PCR once a week significantly reduces infections even when using tests with a lower sensitivity. The structure of social contacts between pupils and teachers strongly influences the transmission. While the density of contact graphs for older pupils is 1.5 times higher than for younger pupils, the teachers’ network is an order of magnitude denser. Teachers moreover act as bridges between groups of children, responsible for 14–18% of infections in the secondary school compared to 8–11% in the primary school. Weekly rotations with regular testing are a highly effective non-pharmacological intervention for the prevention of covid-19 spread in schools and a way to keep schools open during an epidemic.
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permalink https://hdl.handle.net/11104/0347085
cooperation
ARLID cav_un_auth*0339298
name UTIA
cooperation
ARLID cav_un_auth*0345048
name Národohospodářský ústav AV ČR
confidential S
article_num 19156
mrcbC91 A
mrcbT16-e MULTIDISCIPLINARYSCIENCES
mrcbT16-j 1.132
mrcbT16-s 0.973
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mrcbTft \nSoubory v repozitáři: 0578058-aoa.pdf
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mrcbU63 cav_un_epca*0386594 Scientific Reports Roč. 13 1 2023 2045-2322 2045-2322 Nature Publishing Group