COVID-19 aerosol transmission simulation-based risk analysis for in-person learning

As educational institutions begin a school year following a year and a half of disruption from the COVID-19 pandemic, risk analysis can help to support decision-making for resuming in-person instructional operation by providing estimates of the relative risk reduction due to different interventions. In particular, a simulation-based risk analysis approach enables scenario evaluation and comparison to guide decision making and action prioritization under uncertainty. We develop a simulation model to characterize the risks and uncertainties associated with infections resulting from aerosol exposure in in-person classes. We demonstrate this approach by applying it to model a semester of courses in a real college with approximately 11,000 students embedded within a larger university. To have practical impact, risk cannot focus on only infections as the end point of interest, we estimate the risks of infection, hospitalizations, and deaths of students and faculty in the college. We incorporate uncertainties in disease transmission,

the impact of policies such as masking and facility interventions, and variables outside of the college’s control such as population-level disease and immunity prevalence. We show in our example application that universal use of masks that block 40% of aerosols and the installation of near-ceiling, fan-mounted UVC systems both have the potential to lead to substantial risk reductions and that these effects can be modeled at the individual room level. These results exemplify how such simulation-based risk analysis
can inform decision making and prioritization under great uncertainty.

open-source code available here with generic data: https://github.com/tlswan/in-class_covid_transmission

The following publications are supplemented by the data in this project.

• Swanson, Tessa, Seth Guikema, James Bagian, Christopher Schemanske, and Claire Payne. “COVID-19 Aerosol Transmission Simulation-Based Risk Analysis for in-Person Learning.” Cold Spring Harbor Laboratory, October 5, 2021. https://doi.org/10.1101/2021.10.04.21263860.