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Few antimicrobial treatment options are available at this time for treatment of CPE infections, and only a few studies have thus far described the epidemiology of CPE in hospitalised patients. B Methods and materials: b The clinical characteristics of 33 hospitalised patients with CPE, including patient history and factors associated with the development of CPE infections, were examined. B Background: b Infections due to carbapenemase-producing Enterobacteriaceae (CPE) have continually grown as a global public health threat, with significant related mortality rates observed. [Extracted from the article]

Controlling the spread of infectious diseases requires correctly targeting preventative resources. When poorly deployed, these resources are misspent and have an inefficient impact. In a dynamic environment where sources of outbreaks are ever-changing, how to optimally deploy these resources is difficult to identify and can moreover change over time. In addressment, we outline and test network-driven framework that accounts for underlying patient mobility, and outbreak dynamics in hospitals to predict the temporal and spatial arrival of carbapenemase-producing Enterobacteriaceae (CPE) outbreaks. We reconstructed CPE-outbreaks using a novel formulation based on transmission-dynamics, contact-interactions, and microbiology data. For each outbreak, we then examine their spatial evolution and, using background hospital population movement (entire patient population from Imperial College Healthcare NHS Trust between 2018-08-17 and 2022-02-03), we predict the arrival times of new CPE cases across wards. The background mobility-network contained ward-transitions from 181,512 patients (178 wards). Based on the construction, the network comprises a single giant component and acts as a medium for disease transmission. For the results of a fitted regression predicting outbreak arrival, we included locational attributes, in addition to network distances. Overall, we found that effective distance (a graphbased path measure shown previously epidemiologically predictive) contained unique predictive power compared to edge weight. However, the effective distance could be complemented by information regarding patient demographics (Age and Sex) of patient transfers; their predictive power is suggestive of specific sub-population mobility as more important drivers of CPE. We investigated a network-driven framework showing the potential to anticipate arrival-times of hospital CPE outbreaks. In including additional information, we also showed how specific hospital population movements were key drivers of CPE. In furthering our results, we next plan to investigate additional diseases, validate our findings beyond our current dataset, and explore further locational attributes. [ABSTRACT FROM AUTHOR]

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]