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Simulation-Based Estimation of SARS-CoV-2 Infections Associated With School Closures and Community-Based Nonpharmaceutical Interventions in Ontario, Canada

Educational Objective
To identify the key insights or developments described in this article
1 Credit CME
Key Points

Question  What is the association of school reopening or closure with incident and cumulative COVID-19 case numbers compared with other community-based nonpharmaceutical interventions?

Findings  In this decision analytical modelling study of a synthetic population of 1 000 000 individuals in Ontario, Canada, compared with community-based nonpharmaceutical interventions, school closure was associated with a small change in estimated COVID-19 incidence trajectories and cumulative case counts.

Meaning  These findings suggest that community-based interventions to reduce COVID-19 case counts should take precedence over school closure.

Abstract

Importance  Resurgent COVID-19 cases have resulted in the reinstitution of nonpharmaceutical interventions, including school closures, which can have adverse effects on families. Understanding the associations of school closures with the number of incident and cumulative COVID-19 cases is critical for decision-making.

Objective  To estimate the association of schools being open or closed with the number of COVID-19 cases compared with community-based nonpharmaceutical interventions.

Design, Setting, and Participants  This decision analytical modelling study developed an agent-based transmission model using a synthetic population of 1 000 000 individuals based on the characteristics of the population of Ontario, Canada. Members of the synthetic population were clustered into households, neighborhoods, or rural districts, cities or rural regions, day care facilities, classrooms (ie, primary, elementary, or high school), colleges or universities, and workplaces. Data were analyzed between May 5, 2020, and October 20, 2020.

Exposures  School reopening on September 15, 2020, vs schools remaining closed under different scenarios for nonpharmaceutical interventions.

Main Outcomes and Measures  Incident and cumulative COVID-19 cases between September 1, 2020, and October 31, 2020.

Results  Among 1 000 000 simulated individuals, the percentage of infections among students and teachers acquired within schools was less than 5% across modeled scenarios. Incident COVID-19 case numbers on October 31, 2020, were 4414 (95% credible interval [CrI], 3491-5382) cases in the scenario with schools remaining closed and 4740 (95% CrI, 3863-5691) cases in the scenario for schools reopening, with no other community-based nonpharmaceutical intervention. In scenarios with community-based nonpharmaceutical interventions implemented, the incident case numbers on October 31 were 714 (95% CrI, 568-908) cases for schools remaining closed and 780 (95% CrI, 580-993) cases for schools reopening. When scenarios applied the case numbers observed in early October in Ontario, the cumulative case numbers were 777 (95% CrI, 621-993) cases for schools remaining closed and 803 (95% CrI, 617-990) cases for schools reopening. In scenarios with implementation of community-based interventions vs no community-based interventions, there was a mean difference of 39 355 cumulative COVID-19 cases by October 31, 2020, while keeping schools closed vs reopening them yielded a mean difference of 2040 cases.

Conclusions and Relevance  This decision analytical modeling study of a synthetic population of individuals in Ontario, Canada, found that most COVID-19 cases in schools were due to acquisition in the community rather than transmission within schools and that the changes in COVID-19 case numbers associated with school reopenings were relatively small compared with the changes associated with community-based nonpharmaceutical interventions.

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Article Information

Accepted for Publication: February 6, 2021.

Published: March 31, 2021. doi:10.1001/jamanetworkopen.2021.3793

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Naimark D et al. JAMA Network Open.

Corresponding Author: David Naimark, MD, MSc, Sunnybrook Health Sciences Centre, 1929 Bayview Ave, Room 386, Toronto, ON M4G 3E8, Canada (david.naimark@sunnybrook.ca).

Author Contributions: Dr Naimark had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Naimark, Mishra, Barrett, Ximenes, Sander.

Acquisition, analysis, or interpretation of data: Naimark, Mishra, Khan, Mac, Ximenes, Sander.

Drafting of the manuscript: Naimark, Ximenes.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Naimark, Mishra.

Obtained funding: Naimark, Ximenes, Sander.

Administrative, technical, or material support: Barrett, Khan, Mac.

Conflict of Interest Disclosures: Dr Barrett reported receiving personal fees from Xenios outside the submitted work. No other disclosures were reported.

Funding/Support: This research was supported by COVID-19 Rapid Research Funding (grant No. C-291-2431272-SANDER) through the Ontario Ministry of Health, Ontario Together. Dr Sander is supported in part by a Canada Research Chair in Economics of Infectious Diseases award (grant No. CRC-950-232429). Dr Mishra is supported by a Tier 2 Canada Research Chair in Mathematical Modeling and Program Science award.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Additional Contributions: Richard Shillington, PhD (Statistics Canada), helped with queries of the Social Policy Simulation Database/Model, and Al Chrosny, PhD (TreeAge), provided technical help with the transmission model’s software platform. They were not compensated for their work.

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