No abstract

The Centers for Disease Control and Prevention (CDC) estimates that more than two million people contract antibiotic-resistant infections every year, and at least 23,000 die as a result of these infections in the U.S. alone. Traditionally, tracking hospital outbreaks with drug-resistant pathogens focuses on transmission chains of infected or colonized patients as the reservoir for organisms to be transferred to new patients via healthcare workers, but it has become increasingly recognized that non-patient reservoirs within the hospital may play a larger role than previously realized in acting as a niche for the transmission of drug-resistant pathogens. Non-patient sources for pathogen acquisition may require incorporating environmental culture data into existing transmission models. However, the number of risk factors, potential interactions and inherent complexity of the data continue to increase, and thus, exploratory analysis is required to aid in knowledge discovery. Interactive visualization of these data over space and time enables exploration and hypothesis generation to better inform transmission models. This thesis presents an interactive visualization system for the analysis of spatiotemporal environmental and patient data to aid in understanding nosocomial infection. Interactive dashboards allow users to view patient movement through hospital environments while overlaying multivariate environmental microbiological data as it evolves over time. Furthermore, a multivariate logistic regression model is constructed to understand the factors associated with sink contamination. The results show that temporal factors, including the presence of infected patients in the past 14 days and use of interventions in the past 7 days, and spatial factors, including the presence of infected patients in adjacent rooms and the presence of contaminated sinks in adjacent rooms, are significant factors in sink contamination.ii Acknowledgements I would like to thank individuals and organizations that have assisted me throughout this research and my time at University of Virginia. Thank you Prof. Laura Barnes for giving me the opportunity to work on this thesis. I am grateful to her for her continuous support, insightful thoughts and patient guidance throughout this work. Her high expectations pushed me beyond limits known to me and were instrumental in successful completion of this work. Next I wish to thank my committee chair, Dr. Brown for his valuable statistical guidance and constructive comments. Most importantly he served as a role model and inspiration. I also want to thank Dr. Amy Mathers for her contribution of time, ideas and domain expertise throughout this study. I thank Dr. Hyojung Kang for always being there when I needed advice on my thesis and appreciate her thoughtful comments and feedback on literature review.I also want to thank following people for their helpful discussions and suggestions: