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

Principal Investigator(s): View help for Principal Investigator(s) Tessa Swanson, University of Michigan; Seth Guikema, University of Michigan; James Bagian, University of Michigan

Version: View help for Version V2

Name	File Type	Size	Last Modified
classrooms.csv	text/csv	139.1 KB	05/02/2022 01:26:PM
classrooms_codebook.csv	text/csv	1.6 KB	05/02/2022 01:25:PM
data_documentation.docx	application/vnd.openxmlformats-officedocument.wordprocessingml.document	13.4 KB	05/02/2022 01:34:PM
schedule.csv	text/csv	12.8 MB	04/25/2022 11:59:AM

Project Citation:

Swanson, Tessa, Guikema, Seth, and Bagian, James. COVID-19 aerosol transmission simulation-based risk analysis for in-person learning. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2022-06-07. https://doi.org/10.3886/E172081V2

Project Description

Summary:

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

Scope of Project

Subject Terms: aerosol transmission; simulation models; risk analysis; COVID-19

Data Type(s): administrative records data

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Published Versions

V2 [2022-06-07]

V1 [2022-06-07]

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