The University of Arizona
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Development of COVID-19 exposure models for first responder and healthcare scenarios

Program in Applied Mathematics Brown Bag Seminar

Development of COVID-19 exposure models for first responder and healthcare scenarios
Series: Program in Applied Mathematics Brown Bag Seminar
Location: Zoom Meeting
Presenter: Amanda Wilson, Environmental Health Sciences, University of Arizona

Microbial exposure models describing transfer of microbes to and from surfaces during hand-to-surface contacts have been used in quantitative microbial risk assessment (QMRA) to understand how interactions with everyday surfaces affect infection risk. This approach requires multidisciplinary dialogue between engineers, public health researchers, mathematicians, and microbiologists.  In response to COVID-19, specifically, our multidisciplinary teams comprised of researchers from University of Leeds, England, UK; the Ohio State University, Columbus, OH, USA; and the University of Arizona are currently developing microbial exposure models estimating coronavirus exposures for first responders and for healthcare workers, accounting for procedure-specific sequences of human behaviors, contacts with surfaces in patient rooms, and virus deposition on surfaces. The goals of these models are to gain mechanistic insight into how the virus may be spread in healthcare scenarios, to inform surface disinfection protocols, and to inform strategies for maximizing personal protective equipment (PPE) use, such as when to change out gloves, respirators, or gowns. These models can then be used to estimate infection burden and inform epidemiological models on the population level with estimates on larger time scales. These models can also serve as an education tool, where in training videos we can communicate the importance of interventions in quantitative, risk mitigation terms. Future collaboration with applied mathematicians is needed to advance current exposure modeling approaches, integrate microbial exposure models with epidemiological model frameworks, and explore novel approaches for handling uncertainty in exposure mechanisms.

Zoom Session: