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Contact tracing is a crucial tool in infection prevention and control (IPC), which aims to identify outbreaks and prevent onward transmission. What constitutes a contact is typically based on strict binary criteria (i.e., being at a location at the same time). Missing data, indirect contacts and background sources can however substantially alter contact-tracing investigations. Here, we present StEP, a Spatial-temporal Epidemiological Proximity model that accounts for imperfect data by introducing a network-based notion of contact based on spatial-temporal proximity derived from background flows of patient movement. We showcase StEP by analysing outbreaks of multidrug-resistant bacteria and COVID-19 within a large hospital Trust in London (UK).StEP utilises spatial-temporal patient trajectories and the background hospital movement flows to recover enhanced contact networks. Firstly, we study a well-characterised outbreak of carbapenemase-producing Enterobacteriaceae (CPE) involving 116 hospitalised patients where genetic sequencing is used to learn model parameters. Secondly, our trained model is deployed in an unsupervised manner on three unseen outbreaks involving 867 patients of related CPE-types. Thirdly, we test application to an altogether novel pathogen by analysing a hospital outbreak of COVID-19 among 90 hospital patients, and demonstrate the power of StEP when characterising newly emerging diseases, even when there is a lack of sequencing data. In addition to recovering core contact structures, StEP identifies missing contacts that link seemingly unconnected infection clusters, revealing a larger extent of transmission than conventional methods. Via genomic analyses we confirm that the additional contacts detected through StEP lead to improved alignment to the plasmid phylogeny (the major outbreak driving force). Hence the StEP contact network is most aligned to the transmission structure. By considering spatial-temporal information in a continuous manner, StEP tackles several challenges associated with traditional contact-tracing. StEP allows both direct and indirect contacts as possible routes of disease transmission and is tuneable to a pathogen's epidemiological characteristics. Such flexible use of heterogeneous data in uncertain situations can significantly enhance IPC. [ABSTRACT FROM AUTHOR]

Background: Understanding nosocomial acquisition, outbreaks, and transmission chains in real time will be fundamental to ensuring infection-prevention measures are effective in controlling coronavirus disease 2019 (COVID-19) in healthcare. We report the design and implementation of a hospital-onset COVID-19 infection (HOCI) surveillance system for an acute healthcare setting to target prevention interventions., Methods: The study took place in a large teaching hospital group in London, United Kingdom. All patients tested for SARS-CoV-2 between 4 March and 14 April 2020 were included. Utilizing data routinely collected through electronic healthcare systems we developed a novel surveillance system for determining and reporting HOCI incidence and providing real-time network analysis. We provided daily reports on incidence and trends over time to support HOCI investigation and generated geotemporal reports using network analysis to interrogate admission pathways for common epidemiological links to infer transmission chains. By working with stakeholders the reports were co-designed for end users., Results: Real-time surveillance reports revealed changing rates of HOCI throughout the course of the COVID-19 epidemic, key wards fueling probable transmission events, HOCIs overrepresented in particular specialties managing high-risk patients, the importance of integrating analysis of individual prior pathways, and the value of co-design in producing data visualization. Our surveillance system can effectively support national surveillance., Conclusions: Through early analysis of the novel surveillance system we have provided a description of HOCI rates and trends over time using real-time shifting denominator data. We demonstrate the importance of including the analysis of patient pathways and networks in characterizing risk of transmission and targeting infection-control interventions., (© The Author(s) 2020. Published by Oxford University Press for the Infectious Diseases Society of America.)

Background: Early and accurate treatment of infections due to carbapenem-resistant organisms is facilitated by rapid diagnostics, but rare resistance mechanisms can compromise detection. One year after a Guiana Extended-Spectrum (GES)-5 carbapenemase-positive Klebsiella oxytoca infection was identified by whole-genome sequencing (WGS; later found to be part of a cluster of 3 cases), a cluster of 11 patients with GES-5-positive K. oxytoca was identified over 18 weeks in the same hospital., Methods: Bacteria were identified by matrix-assisted laser desorption/ionization-time of flight mass spectrometry, antimicrobial susceptibility testing followed European Committee on Antimicrobial Susceptibility Testing guidelines. Ertapenem-resistant isolates were referred to Public Health England for characterization using polymerase chain reaction (PCR) detection of GES, pulsed-field gel electrophoresis (PFGE), and WGS for the second cluster., Results: The identification of the first GES-5 K. oxytoca isolate was delayed, being identified by WGS. Implementation of a GES-gene PCR informed the occurrence of the second cluster in real time. In contrast to PFGE, WGS phylogenetic analysis refuted an epidemiological link between the 2 clusters; it also suggested a cascade of patient-to-patient transmission in the later cluster. A novel GES-5-encoding plasmid was present in K. oxytoca, Escherichia coli, and Enterobacter cloacae isolates from unlinked patients within the same hospital group and in human and wastewater isolates from 3 hospitals elsewhere in the United Kingdom., Conclusions: Genomic sequencing revolutionized the epidemiological understanding of the clusters; it also underlined the risk of covert plasmid propagation in healthcare settings and revealed the national distribution of the resistance-encoding plasmid. Sequencing results also informed and led to the ongoing use of enhanced diagnostic tests for detecting carbapenemases locally and nationally., (© The Author(s) 2019. Published by Oxford University Press for the Infectious Diseases Society of America.)

A series of extensively drug-resistant isolates of Pseudomonas aeruginosa from two outbreaks in UK hospitals were characterized by whole genome sequencing (WGS). Although these isolates were resistant to antibiotics other than colistin, we confirmed that they are still sensitive to disinfectants. The sequencing confirmed that isolates in the larger outbreak were serotype O12, and also revealed that they belonged to sequence type ST111, which is a major epidemic strain of P. aeruginosa throughout Europe. As this is the first reported sequence of an ST111 strain, the genome was examined in depth, focusing particularly on antibiotic resistance and potential virulence genes, and on the reported regions of genome plasticity. High degrees of sequence similarity were discovered between outbreak isolates collected from recently infected patients, isolates from sinks, an isolate from the sewer, and a historical isolate, suggesting that the ST111 strain has been endemic in the hospital for many years. The ability to translate easily from outbreak investigation to detailed genome biology by use of the same data demonstrates the flexibility of WGS application in a clinical setting., (© 2014 The Authors Clinical Microbiology and Infection © 2014 European Society of Clinical Microbiology and Infectious Diseases.)