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<br>from the COVID-19 pandemic, risk analysis can help to support decision-making for<br>resuming in-person instructional operation by providing estimates of the relative risk<br>reduction due to different interventions. In particular, a simulation-based risk analysis<br>approach enables scenario evaluation and comparison to guide decision making and<br>action prioritization under uncertainty. We develop a simulation model to characterize<br>the risks and uncertainties associated with infections resulting from aerosol exposure in<br>in-person classes. We demonstrate this approach by applying it to model a semester of<br>courses in a real college with approximately 11,000 students embedded within a larger<br>university. To have practical impact, risk cannot focus on only infections as the end<br>point of interest, we estimate the risks of infection, hospitalizations, and deaths of<br>students and faculty in the college. We incorporate uncertainties in disease transmission,<br>the impact of policies such as masking and facility interventions, and variables outside<br>of the college’s control such as population-level disease and immunity prevalence. We<br>show in our example application that universal use of masks that block 40% of aerosols<br>and the installation of near-ceiling, fan-mounted UVC systems both have the potential<br>to lead to substantial risk reductions and that these effects can be modeled at the<br>individual room level. These results exemplify how such simulation-based risk analysis<br>can inform decision making and prioritization under great uncertainty.<br><br>open-source code available here with generic data: https://github.com/tlswan/in-class_covid_transmission