Real-Time Detection of Staphylococcus aureus Transmission in Hospital Settings

SUMMARY Staphylococcus aureus infections, especially methicillin-resistant strains (MRSA), lead to high morbidity and mortality rates in hospitals. Currently, hospitals primarily rely on clinical cultures for MRSA surveillance, which often fails to accurately detect transmission events due to their inability to assess genetic relatedness among bacterial strains. Using whole-genome sequencing (WGS) for early outbreak recognition is crucial for enhancing infection control measures that will reduce the morbidity, mortality, and dissemination of pathogenic genetic variants. However, our preliminary hospital-wide WGS analysis indicates that detecting MRSA transmission in real-time remains challenging because individuals involved in asymptomatic transmission events are not sampled. Consequently, asymptomatic acquisitions often remain unnoticed until readmission or subsequent screening, delaying timely intervention and effective cluster management. Timely detection of transmission events is essential to stop the spread of MRSA and prevent outbreaks. To address the issue at the implementation level, we propose a two-pronged approach. In Aim 1, we will integrate weekly colonization sampling with hospital-wide genomic surveillance to analyze phylogenetic relationships among S. aureus strains. This will help establish a method for detecting real-time transmission events in high-risk hospital wards. Aim 2 focuses on developing predictive models that combine patient, bacterial, and contact network characteristics linked to in-hospital transmission events. These models will synthesize demographic, spatiotemporal, and genomic data to optimize sampling intervals and, ultimately guiding future studies of real- time targeted interventions. We will also compare the cost-effectiveness of different sampling strategies. By enhancing our understanding of transmission dynamics and personalized predictive modeling, this research will shift hospitals from reactive to proactive management of S. aureus transmission clusters. The results will also provide crucial inputs to inform the design of future randomized trials to evaluate these strategies’ effects on morbidity, mortality, and cost. These insights are expected to have broad implications for the surveillance and management of other hospital-acquired pathogens, ultimately contributing to better patient outcomes and more efficient use of healthcare resources. Project Number: 1R01AI193629-01 | Fiscal Year: 2025 | NIH Institute/Center: National Institute of Allergy and Infectious Diseases (NIAID) | Principal Investigator: BO SHOPSIN (+1 co-PI) | Institution: NEW YORK UNIVERSITY SCHOOL OF MEDICINE, NEW YORK, NY | Award Amount: $3,210,845 | Activity Code: R01 | Study Section: Analytics and Statistics for Population Research Panel B Study Section[ASPB] View on NIH RePORTER: https://reporter.nih.gov/project-details/1R01AI19362901