Evaluating the use of whole-genome sequencing and Sanger emm typing in guiding the identification and public health management of invasive group A Streptococcus clusters and outbreaks in England

<b>Introduction.</b> Sanger sequencing-based <i>emm</i> typing has traditionally been used in England to assess case relatedness when managing invasive group A <i>Streptococcus</i> (iGAS) outbreaks.<b>Gap statement.</b> While timely, <i>emm</i> typing can lack resolution. Whole-genome sequencing (WGS), offering greater strain discrimination, is increasingly used, but its comparative value for public health response has not been assessed.<b>Aim.</b> This study evaluates the added value of WGS over Sanger <i>emm</i> typing for group A <i>Streptococcus</i> (GAS) surveillance and outbreak detection, investigation and management in England.<b>Methodology.</b> The evaluation followed the Pathogen Genomics in Public Health Surveillance Evaluation (PG-PHASE) framework (full description in Appendix 1, Table S1). Interviews with laboratory staff and bioinformaticians, and a survey of UK Health Security Agency (UKHSA) health protection and epidemiology iGAS leads, provided insights into <i>emm</i> typing and WGS data use, utility, acceptability and appropriateness. Typing data from outbreak isolates collected between February 2018 and February 2024 were analysed to evaluate the concordance between Sanger <i>emm</i> typing and WGS-based single-nucleotide polymorphism (SNP) clusters. WGS strain diversity and SNP distances within <i>emm</i> types were assessed, using a five-SNP threshold for WGS clusters. The number of cases excluded from outbreaks by <i>emm</i> typing vs. WGS was compared.<b>Results.</b> Laboratory staff indicated that transitioning from a Sanger- to a WGS-based GAS typing service would result in more granular information to inform public health action. Survey participants acknowledged WGS benefits for relatedness resolution but raised concerns about longer turnaround times (a maximum of 14 vs. 5 days for WGS and the Sanger method, respectively) and highlighted the need for training in interpreting WGS data. Suggestions included more standardized, regular, cumulative WGS reports to improve clarity and usefulness. We analysed 178 epidemiologically linked GAS outbreaks, including 1,142 isolates. <i>Emm</i> typing excluded 217 unrelated isolates from 58 outbreaks, while WGS excluded a further 224 isolates (<i>n</i>=44 total) from 114 outbreaks.<b>Conclusion.</b> WGS improves outbreak management, particularly for protracted outbreaks and those with common <i>emm</i> types. WGS better automates and integrates reference laboratory services; however, current turnaround times reduce its benefits for immediate public health action. Our findings suggest that strengthening the timeliness of WGS reporting, co-designing reporting processes with data users and providing training in data interpretation will enhance WGS utility.