Enhancing Surveillance Methods for Genomic Epidemiology in Healthcare - PROJECT SUMMARY/ABSTRACT This is an application for a K01 award for Dr. Alexander Sundermann, an Assistant Professor of Medicine at the University of Pittsburgh. Dr. Sundermann aims to become an independent investigator focusing on genomic epidemiology to prevent healthcare-associated infection (HAI) transmission. This award will facilitate his development in: (1) advanced genomic and bioinformatics techniques, particularly in bacterial pathogen genomics; (2) comprehensive biostatistical methods including regression modeling and machine learning on risks of HAI transmission; and (3) applied healthcare epidemiology, emphasizing the dynamics of pathogen transmission. To achieve these goals, Dr. Sundermann has created a mentorship team of: Dr. Lee Harrison (primary mentor), a leader in genomic epidemiology; Dr. Daria Van Tyne (co-mentor), an established genomic and bioinformatic researcher; and Dr. Graham Snyder (co-mentor), an infectious disease physician and hospital epidemiologist who utilizes genomic surveillance. Additionally, Dr. Sundermann has also created an advisory team of experts in statistics, modeling, informatics, and machine learning. Whole genome sequencing (WGS) surveillance of bacterial pathogens has revealed a large number of previously undetected healthcare outbreaks. Real-time application of WGS surveillance successfully detects these outbreaks, but interventions to control them remain unchanged given the unknown of the exact transmission dynamics. Moreover, efforts to control outbreaks are still reactive for a suspected outbreak rather than proactive prevention. This proposal will test the hypothesis that the combination of real-time WGS surveillance and targeted environmental cultures will enhance the identification and control of outbreaks. Additionally, this proposal aims to build an HAI transmission risk prediction model capable of identifying high-risk transmitters and recipients, allowing for precise interventions to prevent transmission. The specific aims proposed by Dr. Sundermann will test these hypotheses and create data for future studies and translational application. In Specific Aim 1, Dr. Sundermann will test the hypothesis that targeted environmental surveillance cultures will significantly enhance the identification of HAI transmission pathways and population-level transmission risk compared to real-time WGS of clinical isolates alone. In Specific Aim 2 he will expand upon a comprehensive risk scoring system for HAI transmission and acquisition to guide targeted interventions, potentially preventing outbreaks before they begin. The proposed research is impactful because it addresses the critical challenge of understanding HAI outbreak dynamics, early detection, and interventions, potentially transforming current reactive approaches into proactive strategies. The proposed research is also innovative because it leverages a novel combination of real-time WGS surveillance and environmental culturing to identify and interrupt transmission pathways before outbreaks can escalate. This interdisciplinary approach blends cutting-edge modeling and bioinformatics with traditional epidemiological methods, setting a new standard for infection control practices.
