In the long shadow of our best intentions: Model-based assessment of the consequences of school reopening during the COVID-19 pandemic

Abstract

As the world grapples with the ongoing COVID-19 pandemic, a particularly thorny set of questions surrounds the reopening of primary and secondary (K-12) schools. The benefits of in-person learning are numerous, in terms of education quality, mental health, emotional well-being, equity and access to food and shelter. Early reports suggested that children might have reduced susceptibility to COVID-19, and children have been shown to experience fewer complications than older adults. Over the past few months, our understanding of COVID-19 has been further shaped by emerging data, and it is now understood that children are as susceptible to infection as adults and have a similar viral load during infection, even if asymptomatic. Based on this updated understanding of the disease, we have used epidemiological modeling to explore the feasibility and consequences of school reopening in the face of differing rates of COVID-19 prevalence and transmission. We focused our analysis on the United States, but the results are applicable to other countries as well. We demonstrate the potential for a large discrepancy between detected cases and true infections in schools due to the combination of high asymptomatic rates in children coupled with delays in seeking testing and receiving results from diagnostic tests. Our findings indicate that, regardless of the initial prevalence of the disease, and in the absence of robust surveillance testing and contact-tracing, most schools in the United States can expect to remain open for 20-60 days without the emergence of sizeable disease clusters. At this point, even if schools choose to close after outbreaks occur, COVID-19 cases will be seeded from these school clusters and amplified into the community. Thus, our findings suggest that the debate between the risks to student safety and benefits of in-person learning frames a false dual choice. Reopening schools without surveillance testing and contact tracing measures in place will lead to spread within the schools and within the communities that eventually forces a return to remote learning and leaves a trail of infection in its wake.

Fig 1. Clusters will develop quickly in schools upon reopening.

A. Simulated time course of percent of school infected for initial confirmed prevalences ranging from 4 in 10,000 to 25 in 1000, demonstrating that a school reopening at low but non-zero prevalence of disease is simply delayed in its epidemic compared to schools with a higher initial disease prevalence. R₀ = 2.5 for all projections. B. The effect of initial disease prevalence on the expected time to close (assuming a 1% threshold rate of detected cases is sufficient to trigger school closure), indicating that most schools in this regime will close between 20 and 70 days after opening. Upper and lower bounds reflect R₀ bounds of 3.5 and 2.2 (See Methods: Estimation of school R₀). C. Simulated time course of percent of school infected for reproductive numbers ranging from 1.5 to 5, demonstrating the speed at which high R₀ s can lead to widespread infection. Initial prevalence is 5 in 1000 for all projections. D. The effect of R₀ on the expected time to close, indicating again that most schools in this transmission regime will close between 20 and 70 days after opening. Upper and lower bounds reflect prevalence of 3 in 1000 and 10 in 1000 (See Methods: Estimation of average prevalence).

Fig 2. Detected cases will form the tip of the iceberg.

A. Example of time course of infections (blue solid line) compared to time course of detected cases (blue dashed line), demonstrating a significant delay and under counting of reported student cases. Horizontal dashed black line represents the threshold for first detected case, and the horizontal black solid line represents the threshold for time to close of 10 detected cases in a school of 1000. Vertical arrows indicate the points on the true infection curve corresponding to the time of detection of the first case and school closure. In this example, at the time of the first detected case, more than 10 individuals are already infected, and at the time of detected 10 cases prompting school closure, more than 80 individuals are already infected. B. Number of true infections per case detected over time, indicating that early in the outbreak due to significant delays in being tested or receiving a diagnosis, true infections can be more than 10x greater than detected cases. C. Number infected out of 1000 at first detected case for different school R₀s and infection prevalences, demonstrating the potential for a major discrepancy between true infections and detected cases at the time of first case detection.

Fig 3. Even schools that close have the potential to seed infections in their community.

A. Number infected at time of school closure as a function of initial prevalence and school R₀, indicating even if a school closes when 1% of the student body has become a detected case, the true number of infections seeded into the community at that time could be anywhere from 2-18x greater than what was reported. B. Additional community cases in the next 100 days in a population of 50,000 as a function of infections seeded by school and a community’s R₀, indicating the extreme risk for significant secondary infections into the community.