Modeling for COVID-19 college reopening decisions: Cornell, a case study.

We consider epidemiological modeling for the design of COVID-19 interventions in university populations, which have seen significant outbreaks during the pandemic. A central challenge is sensitivity of predictions to input parameters coupled with uncertainty about these parameters. Nearly 2 y into the pandemic, parameter uncertainty remains because of changes in vaccination efficacy, viral variants, and mask mandates, and because universities' unique characteristics hinder translation from the general population: a high fraction of young people, who have higher rates of asymptomatic infection and social contact, as well as an enhanced ability to implement behavioral and testing interventions. We describe an epidemiological model that formed the basis for Cornell University's decision to reopen for in-person instruction in fall 2020 and supported the design of an asymptomatic screening program instituted concurrently to prevent viral spread. We demonstrate how the structure of these decisions allowed risk to be minimized despite parameter uncertainty leading to an inability to make accurate point estimates and how this generalizes to other university settings. We find that once-per-week asymptomatic screening of vaccinated undergraduate students provides substantial value against the Delta variant, even if all students are vaccinated, and that more targeted testing of the most social vaccinated students provides further value.

Different School Reopening Plans on Coronavirus Disease 2019 Case Growth Rates in the School Setting in the United States.

BACKGROUND: In fall 2020, all public K-12 schools reopened in broadly 3 learning models. The hybrid model was considered a mid-risk option compared with remote and in-person learning models. The current study assesses school-based coronavirus disease 2019 (COVID-19) spread in the early fall using a national data set. METHODS: We assess COVID-19 case growth rates from August 10 to October 14, 2020 based on a crowdsourcing data set from the National Education Association. The study follows a retrospective cohort design with the baseline exposures being 3 teaching models: remote learning only, hybrid, and in-person learning. To assess the consistency of our findings, we estimated the overall, as well as region-specific (Northeast, Midwest, South, and West) and poverty-specific (low, mid, and high) COVID-19 case-growth rates. In addition, we validated our study sample using another national sample survey data. RESULTS: The baseline was from 617 school districts in 48 states, where 47% of school districts were in hybrid, 13% were in remote, and 40% were in-person. Controlling for state-level risk and rural-urban difference, the case growth rates for remote and in-person were lower than the hybrid (odds ratio [OR]: 0.963, 95% confidence interval [CI]: 0.960-0.965 and OR: 0.986, 95% CI: 0.984-0.988, respectively). A consistent result was found among school districts in all 4 regions and each poverty level. CONCLUSIONS: Hybrid may not necessarily be the next logical option when transitioning from the remote to in-person learning models due to its consistent higher case growth rates than the other 2 learning models.