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

Quan-Hui Liu; Juanjuan Zhang; Cheng Peng; Maria Litvinova; Shudong Huang; Piero Poletti; Filippo Trentini; Giorgio Guzzetta; Valentina Marziano; Tao Zhou; Cecile Viboud; Ana I Bento; Jiancheng Lv; Alessandro Vespignani; Stefano Merler; Hongjie Yu; Marco Ajelli

doi:10.1038/s41467-021-27939-5

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

Nat Commun. 2022 Jan 14;13(1):322. doi: 10.1038/s41467-021-27939-5.

Authors

Quan-Hui Liu^#¹, Juanjuan Zhang^#^{2

3

4}, Cheng Peng², Maria Litvinova⁵, Shudong Huang¹, Piero Poletti⁶, Filippo Trentini⁷, Giorgio Guzzetta⁶, Valentina Marziano⁶, Tao Zhou⁸, Cecile Viboud⁹, Ana I Bento⁵, Jiancheng Lv¹, Alessandro Vespignani¹⁰, Stefano Merler⁶, Hongjie Yu^{11

12

13}, Marco Ajelli¹⁴

Affiliations

¹ College of Computer Science, Sichuan University, Chengdu, China.
² School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China.
³ Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China.
⁴ Department of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.
⁵ Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA.
⁶ Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy.
⁷ Dondena Centre for Research on Social Dynamics and Public Policy, Bocconi University, Milan, Italy.
⁸ Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China.
⁹ Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA.
¹⁰ Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA.
¹¹ School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China. yhj@fudan.edu.cn.
¹² Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China. yhj@fudan.edu.cn.
¹³ Department of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China. yhj@fudan.edu.cn.
¹⁴ Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA. marco.ajelli@gmail.com.

^# Contributed equally.

Abstract

There 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.

Publication types

Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, P.H.S.

MeSH terms

COVID-19 / diagnosis
COVID-19 / epidemiology*
COVID-19 / prevention & control*
COVID-19 / transmission
COVID-19 Serological Testing
Computer Simulation
Humans
Italy / epidemiology
Mass Screening / trends
Models, Statistical*
Physical Distancing
Quarantine / organization & administration*
SARS-CoV-2 / growth & development
SARS-CoV-2 / immunology
SARS-CoV-2 / pathogenicity*
Schools / legislation & jurisprudence
Schools / organization & administration*
Students / legislation & jurisprudence

Grants and funding

NU38OT000297/CC/CDC HHS/United States