Risk quantification for SARS-CoV-2 infection through airborne transmission in university settings

Abstract

The COVID-19 pandemic has significantly impacted learning as many institutions switched to remote or hybrid instruction. An in-depth assessment of the risk of infection that considers environmental setting and mitigation strategies is needed to make safe and informed decisions regarding reopening university spaces. A quantitative model of infection probability that accounts for space-specific parameters is presented to enable assessment of the risk in reopening university spaces at given densities. The model uses the fraction of the campus population that are viral shedders, room capacity, face covering filtration efficiency, air exchange rate, room volume, and time spent in the space as parameters to calculate infection probabilities in teaching spaces, dining halls, dorms, and shared bathrooms. The model readily calculates infection probabilities in various university spaces, with face covering filtration efficiency and air exchange rate being among the dominant variables. When applied to university spaces, this model demonstrated that, under specific conditions that are feasible to implement, in-person classes could be held in large lecture halls with an infection risk over the semester <1%. Meal pick-ups from dining halls and usage of shared bathrooms in residential dormitories among small groups of students could also be accomplished with low risk. The results of applying this model to spaces at Harvard University (Cambridge and Allston campuses) and Stanford University are reported. Finally, a user-friendly web application was developed using this model to calculate infection probability following input of space-specific variables. The successful development of a quantitative model and its implementation through a web application may facilitate accurate assessments of infection risk in university spaces. However, since this model is thus far unvalidated, validation using infection rate and contact tracing data from university campuses will be crucial as such data becomes available at larger scales. In light of the impact of the COVID-19 pandemic on universities, this tool could provide crucial insight to students, faculty, and university officials in making informed decisions.

Keywords:

• Airborne viral transmission
• coronavirus
• COVID-19
• risk analysis
• SARS-CoV-2
• university

Acknowledgments

We would like to acknowledge the Harvard Active Learning Labs and the Harvard Face Mask Committee for providing excellent guidance throughout our investigation process. We would especially like to acknowledge committee members Stephen Blacklow, John Doyle, Willy Shih, Mary Corrigan, Sarah Fortune, and Sara Malconian for their invaluable support and advice. We would also like to recognize Alena Blaise, DaLoria Boone, Jose Gonzalez, Evan Hunsicker, Julia Luehr, Katia Osei, Meghan Turner, and James Young for their contributions during the Harvard SEAS engineering design course. Last but not least, we would like to acknowledge the Harvard CS50 course staff for their assistance with publishing the website.

Data availability statement

The code and data used to perform the calculations described in this manuscript are available in Github at https://github.com/mythriambatipudi/RiskAnalysis. Fields marked in gray on the datasheet, however, are private third-party data that cannot be distributed. Access to this data may be granted by the contacts listed in the datasheet.