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DOI10.1038/s41467-021-27939-5
Model-based evaluation of alternative reactive class closure strategies against COVID-19
Liu Q.-H.; Zhang J.; Peng C.; Litvinova M.; Huang S.; Poletti P.; Trentini F.; Guzzetta G.; Marziano V.; Zhou T.; Viboud C.; Bento A.I.; Lv J.; Vespignani A.; Merler S.; Yu H.; Ajelli M.
Date Issued2022
ISSN2041-1723
Volume13Issue:1
Other AbstractThere are contrasting results concerning the effect of reactive school closure on SARS-CoV-2 transmission. To shed light on this controversy, we developed a data-driven computational model of SARS-CoV-2 transmission. We found that by reactively closing classes based on syndromic surveillance, SARS-CoV-2 infections are reduced by no more than 17.3% (95%CI: 8.0–26.8%), due to the low probability of timely identification of infections in the young population. We thus investigated an alternative triggering mechanism based on repeated screening of students using antigen tests. Depending on the contribution of schools to transmission, this strategy can greatly reduce COVID-19 burden even when school contribution to transmission and immunity in the population is low. Moving forward, the adoption of antigen-based screenings in schools could be instrumental to limit COVID-19 burden while vaccines continue to be rolled out. © 2022, The Author(s).
Language英语
scopus keywordsantigen; closure; COVID-19; disease transmission; immunity; probability; computer simulation; diagnosis; epidemiology; growth, development and aging; human; immunology; Italy; legislation and jurisprudence; mass screening; organization and management; pathogenicity; prevention and control; quarantine; school; statistical model; student; Computer Simulation; COVID-19; COVID-19 Serological Testing; Humans; Italy; Mass Screening; Models, Statistical; Physical Distancing; Quarantine; SARS-CoV-2; Schools; Students
journalNature Communications
Document Type期刊论文
Identifierhttp://gcip.llas.ac.cn/handle/2XKMVOVA/251253
AffiliationCollege of Computer Science, Sichuan University, Chengdu, China; 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; Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, United States; Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy; Dondena Centre for Research on Social Dynamics and Public Policy, Bocconi University, Milan, Italy; Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China; Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, United States; Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, ...
Recommended Citation
GB/T 7714
Liu Q.-H.,Zhang J.,Peng C.,et al. Model-based evaluation of alternative reactive class closure strategies against COVID-19[J],2022,13(1).
APA Liu Q.-H..,Zhang J..,Peng C..,Litvinova M..,Huang S..,...&Ajelli M..(2022).Model-based evaluation of alternative reactive class closure strategies against COVID-19.Nature Communications,13(1).
MLA Liu Q.-H.,et al."Model-based evaluation of alternative reactive class closure strategies against COVID-19".Nature Communications 13.1(2022).
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