Modeling the Risk of In-Person Instruction during the COVID-19 Pandemic
Brian Liu, Yujia Zhang, Shane G. Henderson, David B. Shmoys, Peter I., Frazier

TL;DR
This paper presents a mathematical model to assess and optimize COVID-19 transmission risk in university classrooms, enabling safer in-person instruction through effective intervention strategies.
Contribution
The study introduces a flexible, generalizable framework for modeling indoor transmission risk, supporting reopening decisions and intervention planning across various settings.
Findings
Masking in dense classrooms with high vaccination rates is effective and low-cost.
The model accurately predicted safety of classroom configurations in retrospective analysis.
Framework was successfully applied to university reopening and large event planning.
Abstract
During the COVID-19 pandemic, safely implementing in-person indoor instruction was a high priority for universities nationwide. To support this effort at the 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 the University during the pandemic and optimize the set of interventions that would allow higher occupancy levels, matching the pre-pandemic numbers of in-person courses. Importantly, we determined that requiring masking in dense classrooms with unrestricted seating with more than 90% of students 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…
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Taxonomy
TopicsEducational Environments and Student Outcomes · Urban Green Space and Health
