An examination of school reopening strategies during the SARS-CoV-2 pandemic
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
The SARS-CoV-2 pandemic led to closure of nearly all K-12 schools in the United States of America in March 2020. Although reopening K-12 schools for in-person schooling is desirable for many reasons, officials understand that risk reduction strategies and detection of cases are imperative in creating a safe return to school. Furthermore, consequences of reclosing recently opened schools are substantial and impact teachers, parents, and ultimately educational experiences in children. To address competing interests in meeting educational needs with public safety, we compare the impact of physical separation through school cohorts on SARS-CoV-2 infections against policies acting at the level of individual contacts within classrooms. Using an age-stratified Susceptible-Exposed-Infected-Removed model, we explore influences of reduced class density, transmission mitigation, and viral detection on cumulative prevalence. We consider several scenarios over a 6-month period including (1) multiple rotating cohorts in which students cycle through in-person instruction on a weekly basis, (2) parallel cohorts with in-person and remote learning tracks, (3) the impact of a hypothetical testing program with ideal and imperfect detection, and (4) varying levels of aggregate transmission reduction. Our mathematical model predicts that reducing the number of contacts through cohorts produces a larger effect than diminishing transmission rates per contact. Specifically, the latter approach requires dramatic reduction in transmission rates in order to achieve a comparable effect in minimizing infections over time. Further, our model indicates that surveillance programs using less sensitive tests may be adequate in monitoring infections within a school community by both keeping infections low and allowing for a longer period of instruction. Lastly, we underscore the importance of factoring infection prevalence in deciding when a local outbreak of infection is serious enough to require reverting to remote learning.
Article activity feed
-
-
-
SciScore for 10.1101/2020.08.05.20169086: (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
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:There are several limitations to our simulations. First, we account only for two separate age classes, children and adults. A finer level of age stratification may be better suited for predicting outcomes in specific communities and is, in principle, straightforward to implement within our modeling framework. For example, our model …
SciScore for 10.1101/2020.08.05.20169086: (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
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:There are several limitations to our simulations. First, we account only for two separate age classes, children and adults. A finer level of age stratification may be better suited for predicting outcomes in specific communities and is, in principle, straightforward to implement within our modeling framework. For example, our model currently makes no distinction between high school and elementary school children. High school students may be more easily convinced into wearing masks reliably and practicing physical distancing, but they also may have transmission rates closer to those of adults. Second, we treat all adults, including teachers, as having the same transmission rates and omit interactions between students and teachers within a classroom. The latter are clearly critical in implementing backup protocols that allow the switch to remote learning. A network-based model that accounts for households and classrooms in more detail would be better equipped to identify optimal policies. Third, our model treats school communities in isolation. Schools in urban settings undoubtedly have more diverse commuting patterns and face a greater potential for importing cases from outside adjacent neighborhoods. Finally, our models are deterministic and cannot account for the stochastic nature of infections. The caveats outlined here limit the quantitative accuracy of our predictions, but we contend that our qualitative conclusions are correct. As already mentioned, our simulations sugge...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
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.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
-