Abstract View
Using Modeled Respiratory Aerosol and Measured CO2 to Inform COVID-19 Risk Prevention on a University Campus
J. ALEX HUFFMAN, Nora Carmody, University of Denver
Abstract Number: 544
Working Group: Translating Aerosol Research for Societal Impact: Science Communication and Public Outreach
Abstract
Due to the COVID-19 pandemic, colleges struggled to offer in-person learning opportunities and faced even deeper challenges with experiential learning opportunities, i.e. ensemble music performance. Navigating the options to reduce risk was bewildering for most organizations, and limited budgets forced tough choices between interventions. To help quantify risk and provide a framework by which decisions could be made, I utilized available aerosol modeling tools to inform university administrators about preventative steps that would provide the greatest reduction of COVID infection risk.
The Wells-Riley box model for infectious aerosol transmission has been utilized for many decades as a basic tool to estimate relative risk. I built the model onto the Igor Pro platform and added functionality with respect to CO2 concentrations and wait times after room use. By analyzing a variety of room types on the University of Denver (DU) campus (including music performance and practice, lecture classrooms, and gym spaces) I was able to individually investigate intervention parameters such as room occupancy loads, duration and wait times of classes, effect of added ventilation and in-room HEPA filtration. By analyzing the relative infection risk by varying key parameters, we were able to focus on strategies that would have the most impact on reducing risk, without wasting resources that were unlikely to reduce respiratory aerosol burden and risk.
In parallel, twelve CO2 sensors deployed in rooms on the campus provided real-time feedback to reveal when ventilation rates or schedules were insufficient for given spaces. Comparing measured CO2 values, used as a proxy for respiratory aerosol output, to the modeled results also helped test model input assumptions. Results of aerosol modeling and CO2 measurement results will be shown, focusing on lessons learned about how available tools can be leveraged for best use of available intervention resources.