Discipline: Ecology Environmental and Earth Sciences
Amanda M. Wilson - University of Arizona
Co-Author(s): Kelly A. Reynolds, Ph.D., University of Arizona, Tucson, AZ; Robert A. Canales, Ph.D., University of Arizona, Tucson, AZ
Viral pathogens account for a large number of nosocomial infections. The application of mechanistic modeling methodology to answer infection control questions is a novel approach with growing traction. The objective of this study was to develop a stochastic discrete event model that accounts for cleaning time, cleaning frequency and hand hygiene events in order to model and compare the effect of 1 and 2 rounds of cleaning in a 6 hour period on viral infection risk.
Virus concentrations on surfaces were simulated using mixed distributions with parameters informed by literature review. A stochastic discrete event model was developed to model 1,000 nurses – second-by-second hand contact activities and hand hygiene compliance, where the probability of each event occurring was weighted by the contact frequencies per minute found in the literature for each contact event type. Monte Carlo methods were used to capture variability in hand surface area, fraction of viral transfer during contact events, and fraction of hand in contact with surfaces or orifices during contact events. Predicted doses of rotavirus, rhinovirus, and influenza were inputted into dose-response curves to predict infection risk. Percent reductions in hand concentrations per second and total infection risks from baseline (0 cleanings) were calculated for a single cleaning (cleaning at 1 hour from start) and for 2 cleanings (cleaning at 1 and 3 hours from start).
A single cleaning reduced second-by-second viral concentrations on hands by >26.4%. Two cleanings resulted in slightly larger reductions (>36.6%). Infection risk reductions were highest for two cleanings, where reductions ranged from 30.5 ? 32.5%. Percent reductions in infection risk for one cleaning ranged from 19.6 – 22.9%.
This model supports increased surface disinfection use and demonstrates quantitatively that increased surface cleaning events increases reductions in infection risk. Exploring the effect of cleaning event times on the risk reduction will be explored in future models. More information is needed regarding healthcare worker behavior and the rate at which surfaces are re-contaminated following cleaning events. The further development of these types of models is important to inform best practices in infection control cleaning protocols by optimizing cleaning event timing and frequency.
Funder Acknowledgement(s): This project was supported by GOJO Industries, Inc.This research was also supported in part by the Western Alliance to Expand Student Opportunities (WAESO) Louis Stokes Alliance for Minority Participation (LSAMP) Bridge to Doctorate (BD) National Science Foundation (NSF) Grant No. HRD-1608928.
Faculty Advisor: Dr. Kelly A. Reynolds, email@example.com
Role: I developed and coded a majority of the model in RStudio and constructed the research question. Dr. Robert Canales constructed the code for the mixed distribution of virus concentrations on surfaces. I met regularly with Dr. Kelly Reynolds and Dr. Robert Canales to ensure that my microbiological assumptions and modeling methodology were theoretically sound, and our conversations informed my conceptual thinking in designing the model and in accounting for relevant exposure-related variables.