Schools are not islands: Balancing COVID-19 risk and educational benefits using structural and temporal countermeasures
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Abstract
Background
School closures around the world contributed to reducing the transmission of COVID-19. In the face of significant uncertainty around the epidemic impact of in-person schooling, policymakers, parents, and teachers are weighing the risks and benefits of returning to in-person education. In this context, we examined the impact of different school reopening scenarios on transmission within and outside of schools and on the share of school days that would need to be spent learning at a distance.
Methods
We used an agent-based mathematical model of COVID-19 transmission and interventions to quantify the impact of school reopening on disease transmission and the extent to which school-based interventions could mitigate epidemic spread within and outside schools. We compared seven school reopening strategies that vary the degree of countermeasures within schools to mitigate COVID-19 transmission, including the use of face masks, physical distancing, classroom cohorting, screening, testing, and contact tracing, as well as schedule changes to reduce the number of students in school. We considered three scenarios for the size of the epidemic in the two weeks prior to school reopening: 20, 50, or 110 detected cases per 100,000 individuals and assumed the epidemic was slowly declining with full school closures ( R e = 0.9). For each scenario, we calculated the percentage of schools that would have at least one person arriving at school with an active COVID-19 infection on the first day of school; the percentage of in-person school days that would be lost due to scheduled distance learning, symptomatic screening or quarantine; the cumulative infection rate for students, staff and teachers over the first three months of school; and the effective reproduction number averaged over the first three months of school within the community.
Findings
In-person schooling poses significant risks to students, teachers, and staff. On the first day of school, 5–42% of schools would have at least one person arrive at school with active COVID-19, depending on the incidence of COVID in the community and the school type. However, reducing class sizes via A/B school scheduling, combined with an incremental approach that returns elementary schools in person and keeps all other students remote, can mitigate COVID transmission. In the absence of any countermeasures in schools, we expect 6 – 25% of teaching and non-teaching staff and 4 – 20% of students to be infected with COVID in the first three months of school, depending upon the case detection rate. Schools can lower this risk to as low as 0.2% for staff and 0.1% for students by returning elementary schools with a hybrid schedule while all other grades continue learning remotely. However, this approach would require 60–85% of all school days to be spent at home. Despite the significant risks to the school population, reopening schools would not significantly increase community-wide transmission, provided sufficient countermeasures are implemented in schools.
Interpretation
Without extensive countermeasures, school reopening may lead to an increase in infections and a significant number of re-closures as cases are identified among staff and students. Returning elementary schools only with A/B scheduling is the lowest risk school reopening strategy that includes some in-person learning.
Research in context
Evidence before this study
Scientific evidence on COVID-19 transmission has been evolving rapidly. We searched PubMed on 6 September 2020 for studies using the phrase (“COVID-19” OR “SARS-CoV-2”) AND (“model” OR “modeling” OR “modelling”) AND (“schools”) AND (“interventions”). This returned 17 studies, of which 6 were retained after screening. A wide variety of impacts from school closures were reported: from 2–4% of deaths at the lower end to reducing peak numbers of infections by 40–60% at the upper end. Drivers of this variability include (a) different epidemic contexts when school closure scenarios are enacted, (b) different timeframes and endpoints, and (c) different model structures and parameterizations. Thus, considerable variation in predicted impacts of school closures has been reported.
Added value of this study
To our knowledge, this is the first modeling study that explores the trade-offs between increased risk of COVID-19 transmission and school days lost, taking into account detailed data on school demographics and contact patterns, a set of classroom countermeasures based on proposed policies, and applies them to range of community transmission levels. If rates of community transmission are high, school reopening will accelerate the epidemic, but will not change its overall course. However, even if rates of community transmission are low, complete school reopening risks returning to exponential epidemic growth. Staged school reopening coupled with aggressive countermeasures is the safest strategy, but even so, reactive school closures will likely be necessary to prevent epidemic spread.
Implications of all the available evidence
The impact of school reopening on the COVID-19 epidemic depends on the transmission context and specific countermeasures used, and no reopening strategies are zero risk. However, by layering multiple types of countermeasures and responding quickly to increases in new infections, the risks of school reopening can be minimized.
Article activity feed
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SciScore for 10.1101/2020.09.08.20190942: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources The Covasim model is fully open source and is available for download via GitHub and the Python Package Index; more information is available at https://covasim.org. 2.2. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Limitations: While agent-based modeling is able to capture many details of populations and disease transmission, our work has important limitations and assumptions that could impact our findings. There is still a high …
SciScore for 10.1101/2020.09.08.20190942: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources The Covasim model is fully open source and is available for download via GitHub and the Python Package Index; more information is available at https://covasim.org. 2.2. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Limitations: While agent-based modeling is able to capture many details of populations and disease transmission, our work has important limitations and assumptions that could impact our findings. There is still a high degree of uncertainty around the susceptibility, symptomiticity/severity, and infectivity of COVID-19 in children, particularly since schools in most locations shut down early in the epidemic. Our analysis is based on the most recent scientific literature for each of these parameters. We assumed individuals under 20 had a 45–50% reduced risk of developing symptoms [8] and 33–66% reduced risk of acquiring infection [31]. We varied this in sensitivity analysis so that individuals under 20 are equally as susceptible to infection as individuals age 20 to 50. We assume that an infectious individual is 5 times more likely per day to transmit to a household contact than a school contact, based on estimated numbers of hours spent in each setting per week. We assume all individuals infected with COVID-19 are equally likely to transmit infection per contact, and varied this assumption in sensitivity analysis so that individuals under 10 years old are half as likely. After being diagnosed, all individuals are assumed to reduce their daily infectivity by 70% for home contacts, 90% for community contacts, and 100% for school and work contacts. Additionally, the household contacts of these individuals may be traced, notified, and school contacts removed from school for a full...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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- No protocol registration statement was detected.
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