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Assessing virus infection probability in an office setting using stochastic simulation
R David Contreras1, Amanda M Wilson1, Fernanda Garavito1
1Environment, Exposure Science and Risk Assessment Center, Mel and Enid Zuckerman College of Public Health, The University of Arizona, Tucson, Arizona, USA.
Abstract:
Viral infections are an occupational health concern for office workers and employers. The objectives of this study were to estimate rotavirus, rhinovirus, and influenza A virus infection risks in an office setting and quantify infection risk reductions for two hygiene interventions. In the first intervention, research staff used an ethanol-based spray disinfectant to clean high-touch non-porous surfaces in a shared office space. The second intervention included surface disinfection and also provided workers with alcohol-based hand sanitizer gel and hand sanitizing wipes to promote hand hygiene. Expected changes in surface concentrations due to these interventions were calculated. Human exposure and dose were simulated using a validated, steady-state model incorporated into a Monte Carlo framework. Stochastic inputs representing human behavior, pathogen transfer efficiency, and pathogen fate were utilized, in addition to a mixed distribution that accounted for surface concentrations above and below a limit of detection. Dose-response curves were then used to estimate infection risk. Estimates of percent risk reduction using mean values from baseline and surface disinfection simulations for rotavirus, rhinovirus, and influenza A infection risk were 14.5%, 16.1%, and 32.9%, respectively. For interventions with both surface disinfection and the promotion of personal hand hygiene, reductions based on mean values of infection risk were 58.9%, 60.8%, and 87.8%, respectively. This study demonstrated that surface disinfection and the use of personal hand hygiene products can help decrease virus infection risk in communal offices. Additionally, a variance-based sensitivity analysis revealed a greater relative importance of surface concentrations, assumptions of relevant exposure routes, and inputs representing human behavior in estimating risk reductions.
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