2 results
Integration of genomic and clinical data augments surveillance of healthcare-acquired infections
- Doyle V. Ward, Andrew G. Hoss, Raivo Kolde, Helen C. van Aggelen, Joshua Loving, Stephen A. Smith, Deborah A. Mack, Raja Kathirvel, Jeffery A. Halperin, Douglas J. Buell, Brian E. Wong, Judy L. Ashworth, Mary M. Fortunato-Habib, Liyi Xu, Bruce A. Barton, Peter Lazar, Juan J. Carmona, Jomol Mathew, Ivan S. Salgo, Brian D. Gross, Richard T. Ellison III
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 40 / Issue 6 / June 2019
- Published online by Cambridge University Press:
- 23 April 2019, pp. 649-655
- Print publication:
- June 2019
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- Article
- Export citation
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Background:
Determining infectious cross-transmission events in healthcare settings involves manual surveillance of case clusters by infection control personnel, followed by strain typing of clinical/environmental isolates suspected in said clusters. Recent advances in genomic sequencing and cloud computing now allow for the rapid molecular typing of infecting isolates.
Objective:To facilitate rapid recognition of transmission clusters, we aimed to assess infection control surveillance using whole-genome sequencing (WGS) of microbial pathogens to identify cross-transmission events for epidemiologic review.
Methods:Clinical isolates of Staphylococcus aureus, Enterococcus faecium, Pseudomonas aeruginosa, and Klebsiella pneumoniae were obtained prospectively at an academic medical center, from September 1, 2016, to September 30, 2017. Isolate genomes were sequenced, followed by single-nucleotide variant analysis; a cloud-computing platform was used for whole-genome sequence analysis and cluster identification.
Results:Most strains of the 4 studied pathogens were unrelated, and 34 potential transmission clusters were present. The characteristics of the potential clusters were complex and likely not identifiable by traditional surveillance alone. Notably, only 1 cluster had been suspected by routine manual surveillance.
Conclusions:Our work supports the assertion that integration of genomic and clinical epidemiologic data can augment infection control surveillance for both the identification of cross-transmission events and the inclusion of missed and exclusion of misidentified outbreaks (ie, false alarms). The integration of clinical data is essential to prioritize suspect clusters for investigation, and for existing infections, a timely review of both the clinical and WGS results can hold promise to reduce HAIs. A richer understanding of cross-transmission events within healthcare settings will require the expansion of current surveillance approaches.
10 - Data-Intensive Visual Analysis for Cyber-Security
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- By William A. Pike, Pacific Northwest National Laboratory, Daniel M. Best, Pacific Northwest National Laboratory, Douglas V. Love, Pacific Northwest National Laboratory, Shawn J. Bohn, Pacific Northwest National Laboratory
- Edited by Ian Gorton, Deborah K. Gracio
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- Book:
- Data-Intensive Computing
- Published online:
- 05 December 2012
- Print publication:
- 29 October 2012, pp 258-286
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- Chapter
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Summary
Introduction
Protecting communications networks against attacks where the aim is to steal information, disrupt order, or harm critical infrastructure can require the collection and analysis of staggering amounts of data. The ability to detect and respond to threats quickly is a paramount concern across sectors, and especially for critical government, utility, and financial networks. Yet detecting emerging or incipient threats in immense volumes of network traffic requires new computational and analytic approaches. Network security increasingly requires cooperation between human analysts able to spot suspicious events through means such as data visualization and automated systems that process streaming network data in near real-time to triage events so that human analysts are best able to focus their work.
This chapter presents a pair of network traffic analysis tools coupled to a computational architecture that enables the high-throughput, real-time visual analysis of network activity. The streaming data pipeline towhich these tools are connected is designed to be easily extensible, allowing newtools to subscribe to data and add their own in-stream analytics. The visual analysis tools themselves – Correlation Layers for Information Query and Exploration (CLIQUE) and Traffic Circle – provide complementary views of network activity designed to support the timely discovery of potential threats in volumes of network data that exceed what is traditionally visualized. CLIQUE uses a behavioral modeling approach that learns the expected activity of actors (such as IP addresses or users) and collections of actors on a network, and compares current activity to this learned model to detect behavior-based anomalies.