560. Relating Whole-Genome Sequencing of Methicillin-Resistant Staphylococcus aureus Isolates to Transmission Dynamics and Efficacy of Control Interventions

BACKGROUND: Methicillin-resistant staphylococcus aureus (MRSA) colonization of hospitalized patients is associated with higher readmission rates and increased morbidity. Depending on the mechanisms of transmission, numerous potential control interventions exist to reduce the burden of disease. However, given the preponderance of asymptomatic colonization, it is challenging to quantify the relative importance of different transmission mechanisms and assess control efficacy. By identifying clusters of transmission, whole-genome sequencing (WGS) provides an opportunity to overcome these challenges. METHODS: We sought to apply cluster analysis techniques to WGS data for MRSA, in order to assess MRSA prevalence, transmissibility, the degree of transmission heterogeneity and the potential effectiveness of control. Our model builds upon previous work that showed a direct relationship between the size distribution of infection clusters, the effective reproduction number (R) and the dispersion parameter (k). To demonstrate its functionality, our model was applied to existing WGS data for MRSA isolates collected during a 12 month period in the East of England (DOI: 10.1126/scitranslmed.aak9745) RESULTS: The effective reproduction number for the East of England data is 0.29 (95% CI: 0.24–0.36). The dispersion parameter is 0.09 (0.03–0.33) reflecting a high degree of transmission heterogeneity. This implies all transmission is caused by just 12% of the cases. Targeted control of these cases could have decreased overall burden of MRSA colonization by 29% during the time period of the study. CONCLUSION: The high degree of transmission heterogeneity seen in MRSA transmission suggests that the risk for infection is variable.This observation motivates the need for more detailed mechanistic modeling of hospital-based MRSA transmission that integrates patients-specific factors, movement data and genome sequencing. Such models could be used to forecast which patients are at greatest risk for either acquiring or transmitting MRSA, thereby improving targeted control. DISCLOSURES: All authors: No reported disclosures.