Model-based evaluation of alternative reactive class closure strategies against COVID-19.


Journal

medRxiv : the preprint server for health sciences
Titre abrégé: medRxiv
Pays: United States
ID NLM: 101767986

Informations de publication

Date de publication:
23 Apr 2021
Historique:
entrez: 28 4 2021
pubmed: 29 4 2021
medline: 29 4 2021
Statut: epublish

Résumé

There are contrasting results concerning the effect of reactive school closure on SARS-CoV-2 transmission. To shed light on this controversy, here we develop a data-driven computational model of SARS-CoV-2 transmission to investigate mechanistically the effect on COVID-19 outbreaks of school closure strategies based on syndromic surveillance and antigen screening of students. We found that by reactively closing classes based on syndromic surveillance, SARS-CoV-2 infections are reduced by no more than 13.1% (95%CI: 8.6%-20.2 %), due to the low probability of timely symptomatic case identification among the young population. We thus investigated an alternative triggering mechanism based on repeated screening of students using antigen tests. Should population-level social distancing measures unrelated to schools enable maintaining the reproduction number (

Identifiants

pubmed: 33907769
doi: 10.1101/2021.04.18.21255683
pmc: PMC8077629
pii:
doi:

Types de publication

Preprint

Langues

eng

Commentaires et corrections

Type : UpdateIn

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Auteurs

Quan-Hui Liu (QH)

College of Computer Science, Sichuan University, Chengdu, China.

Juanjuan Zhang (J)

School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China.

Cheng Peng (C)

School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China.

Maria Litvinova (M)

Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA.

Shudong Huang (S)

College of Computer Science, Sichuan University, Chengdu, China.

Piero Poletti (P)

Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy.

Filippo Trentini (F)

Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy.

Giorgio Guzzetta (G)

Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy.

Valentina Marziano (V)

Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy.

Tao Zhou (T)

Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China.
Tianfu Complexity Science Research Center, Chengdu, China.

Cecile Viboud (C)

Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA.

Ana I Bento (AI)

Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA.

Jiancheng Lv (J)

College of Computer Science, Sichuan University, Chengdu, China.

Alessandro Vespignani (A)

Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA.
ISI Foundation, Turin, Italy.

Stefano Merler (S)

Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy.

Hongjie Yu (H)

School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China.
Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China.
Department of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.

Marco Ajelli (M)

Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA.
Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA.

Classifications MeSH