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@article{liuetal23,
	abstract = {During the COVID-19 pandemic, implementing in-person indoor instruction in a safe manner 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 design a safe classroom environment at the University during the COVID-19 pandemic that supported
the higher occupancy levels needed to match pre-pandemic numbers of in-person courses, despite a limited
number of large classrooms. 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 also used to support reopening decisions at Stanford University. In addition, our methods are flexible;
our modeling framework was repurposed to plan for large university events and gatherings. We found that
our approach and methods work in a wide range of indoor settings and could be used to support reopening
planning across various industries, from secondary schools to movie theaters and restaurants.},
	author = {Brian Liu and Yujia Zhang and Shane G. Henderson and David B. Shmoys and Peter I. Frazier},
	date-added = {2023-10-07 07:44:15 -0400},
	date-modified = {2024-08-12 14:02:12 -0400},
	journal = {{INFORMS} Journal on Applied Analytics},
	pages = {To appear},
	title = {Modeling the Risk of In-Person Instruction during the COVID-19 Pandemic},
	url_paper = {https://doi.org/10.1287/inte.2023.0076},
	year = {2024}}