Exploiting network analysis to create a novel sentinel surveillance system for efficient, rapid detection of emerging Clostridioides difficile strains in England

Abstract Whole genome sequencing is being introduced in England to support national surveillance of key hospital-acquired pathogens, starting with Clostridiodes difficile, to facilitate novel strain identification and enable timely interventions. However sequencing capacity is limited. Symptomatic and asymptomatic patients attending multiple hospitals can act as inter-facility transmission vectors; we therefore identified the empirical network of shared patients from analysing national admission data and simulated spread of a hypothetical novel strain. Algorithmically optimising detection, incorporating logistical constraints, we identified sentinel sites which detected a novel strain 27% faster than random sentinel selection, whilst sequencing <15% cases. Sensitivity and scenario analyses using a range of plausible pathogen characteristics and historical networks confirmed epidemiologically- and longitudinally-robust sentinel selection and performance. The new surveillance system, established from our findings, benefits from stress-tested sentinel set selection to deliver rapid, efficient identification of novel strains within real world constraints, to inform control interventions, and provides a roadmap for future hospital-acquired pathogen surveillance.