Modeling the Risk of In-Person Instruction During the COVID-19 Pandemic

During the COVID-19 pandemic, safely implementing in-person indoor instruction was a high priority for universities nationwide. To support this effort at Cornell University, we developed a mathematical model for estimating the risk of SARS-CoV-2 transmission in university classrooms. This model was used to evaluate combinations of feasible interventions for classrooms at Cornell during the COVID-19 pandemic and identify the best set of interventions that allow for higher occupancy levels, matching the prepandemic numbers of in-person courses, despite a limited number of large classrooms. Importantly, we determined that requiring masking in dense classrooms with unrestricted seating when more than 90% of students were vaccinated was easy to implement, incurred little logistical or financial cost, and allowed classes to be held at full capacity. A retrospective analysis at the end of the semester confirmed the model’s assessment that the proposed classroom configuration was safe. Our framework is generalizable and was used to support reopening decisions at Stanford University. In addition, our framework is flexible and applies to a wide range of indoor settings. It was repurposed for large university events and gatherings, and it can be used to support planning indoor space use to avoid transmission of infectious diseases across various industries, from secondary schools to movie theaters and restaurants.

History: This paper was refereed.

Funding: This work was supported by the NSF Division of Mathematical Sciences [Grant 2230023].

Supplemental Material: The online appendix is available at https://doi.org/10.1287/inte.2023.0076.