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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

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 classroom configuration would be 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 could 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.

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

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 classroom configuration would be 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 could 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.
Paper Structure (46 sections, 22 equations, 13 figures, 8 tables)

This paper contains 46 sections, 22 equations, 13 figures, 8 tables.

Figures (13)

  • Figure 1: Timeline of significant events during the planning period for the Fall 2021 Semester.
  • Figure 2: Floor plan of Olin 155, a large lecture hall at the University.
  • Figure 3: Daily COVID-19 cases counts for New York State based on reports from state and local health agencies times_2021.
  • Figure 4: Example illustration of classroom simulation tool.
  • Figure 5: Average number of secondary infections over one hour of lecture with one positive student for different intervention settings, assuming 90% vaccination rate. Blue and orange represent unrestricted and fixed seating respectively. Solid and dashed lines represent 0% and 100% masking respectively.
  • ...and 8 more figures