Novel surveillance design exploiting network analysis for efficient, rapid detection of emerging Clostridioides difficile strains

Whole genome sequencing is being introduced in England to support national surveillance of key hospital-acquired pathogens, starting with Clostridioides 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; here we therefore identify the empirical network of shared patients from analysing national admission data and simulate spread of a hypothetical novel strain. Algorithmically optimising detection, incorporating logistical constraints, we identify sentinel sites which detect 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 confirm 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.