Tool for Monitoring and Predicting Nosocomial Pathogens in Veterinary Health-care Settings

Principal Investigator: Renata Ivanek

Co-PI: Laura Goodman, Anil Thachil, Gillian Perkins

Department of Population Medicine and Diagnostic Sciences
Sponsor: 2018/19 Research Grants Program in Animal Health
Title: Tool for Monitoring and Predicting Nosocomial Pathogens in Veterinary Health-care Settings
Project Amount: $50,000
Project Period: October 2018 to September 2019

DESCRIPTION (provided by applicant): 

There is a need for improved monitoring, predicting and testing of surveillance approaches for nosocomial pathogens in veterinary health-care facilities that would account for the great diversity and heterogeneity in these environments. We propose to develop a customizable software and environmental monitoring mapping tool for optimizing detection of nosocomial pathogens, visualizing problem areas, and analyzing data for long term trends under different scenarios and interventions. Specifically, we will repurpose our existing highly spatially granular agent-based model developed for environmental monitoring of microorganisms in the complex environments in food-production facilities to monitoring of nosocomial pathogens. Salmonella spp. in the Cornell’s Equine and Nemo Farm Animal Hospitals (ENFAH) will be used as a model system. The model will be adapted to specific hospital features and environmental conditions, validated with the hospitals’ historical data, and used for rapid virtual experimentation of a variety of sampling plans and interventions (e.g., cleaning & disinfection and patient isolation) to monitor and control Salmonella in the facility. Next, we will conduct an extensive, prospective risk-based collection of environmental samples in model-predicted locations in ENFAH to determine the contamination status of the hospitals and prioritize locations for future monitoring. As a result, the project will provide ENFAH with science-based resources for selecting appropriate sampling schemes and interventions in their unique facility. Even more importantly, findings that are consistent across scenarios will be communicated as best practices generalizable to other health-care facilities, with the ability to refine these for specific circumstances and attributes in those settings. As such, the results of this study will provide an alternative to the trial and error approaches to optimization of nosocomial pathogen surveillance in specific veterinary health-care facilities by challenging the “one-size-fits-all” mentality.