PREreview of WGS-Enabled Surveillance Improves Detection Of Transmission Events Within A Large Tertiary Care Hospital Trust In London

This study evaluates whether integrating whole-genome sequencing with patient movement data can improve the detection and characterization of outbreaks caused by carbapenem-producing Enterobacterales in hospital settings, compared to standard infection prevention and control surveillance. Using retrospective data from two outbreak datasets, the authors compare WGS-enabled surveillance with standard IPC methods. The results show that standard approaches miss a substantial proportion of transmission events, while WGS integration significantly improves sensitivity and allows for earlier identification of transmission events. The integrated approach also uncovers transmission pathways that would not be detected through conventional methods. Additionally, the study suggests that implementing this strategy could lead to considerable economic benefits, supporting its potential value in healthcare settings.

This study has several notable strengths. First, it presents an innovative and integrative approach that combines genomic and epidemiological data, offering a more comprehensive understanding of transmission dynamics. Second, it provides clear quantitative comparisons between standard IPC methods and WGS-enabled surveillance, strengthening the validity of its conclusions. Third, the inclusion of an economic analysis enhances the practical relevance of the study and supports its potential implications for healthcare policy and resource allocation.

Despite these strengths, there are important limitations that should be considered. The study is based on retrospective data from a single healthcare system, which may limit the generalizability of the findings to other settings with different infrastructures, patient populations and infection control practices. In addition, the feasibility of implementing WGS in real time is not fully addressed, key aspects such as availability, sequencing turnaround times, costs, and integration into clinical workflows remain uncertain, and in practice, delays in data processing could reduce the impact on infection control decisions. These factors are critical for translating the proposed approach into standard practice.

Another important limitation is that the model infers transmission events based on genomic similarity and patient overlap, but these assumptions may not fully capture more complex transmission pathways such as those involving environmental reservoirs or indirect transmission. Furthermore, the limited incorporation of clinical context may restrict the interpretation of transmission relevance and outbreak dynamics.

In conclusion, this study provides valuable evidence supporting the integration of genomic data into hospital surveillance systems to enhance outbreak detection and inform infection control strategies. However, given its retrospective design, the model likely benefits from more complete datasets than would be available in real time, which may lead to an overestimation of its performance and practical impact in real-world healthcare settings.

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