4.7 Article

In Search of Patient Zero: Visual Analytics of Pathogen Transmission Pathways in Hospitals

Journal

Publisher

IEEE COMPUTER SOC
DOI: 10.1109/TVCG.2020.3030437

Keywords

dynamic networks; visualization applications; health; medicine; outbreak; Klebsiella; infection control

Funding

  1. German Federal Ministry of Education and Research (BMBF) within the Medical Informatics Initiative [01ZZ1802B/HiGHmed]

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The study introduces a novel visual analytics approach to support the analysis of transmission pathways during hospital pathogen outbreaks, demonstrating significant benefits in terms of efficiency, extended analysis intervals, and broader coverage of hospital wards. Feedback from twenty-five experts from seven German hospitals confirms that the solution brings substantial advantages in analyzing outbreaks.
Pathogen outbreaks (i.e., outbreaks of bacteria and viruses) in hospitals can cause high mortality rates and increase costs for hospitals significantly. An outbreak is generally noticed when the number of infected patients rises above an endemic level or the usual prevalence of a pathogen in a defined population. Reconstructing transmission pathways back to the source of an outbreak - the patient zero or index patient - requires the analysis of microbiological data and patient contacts. This is often manually completed by infection control experts. We present a novel visual analytics approach to support the analysis of transmission pathways, patient contacts, the progression of the outbreak, and patient timelines during hospitalization. Infection control experts applied our solution to a real outbreak of Klebsiella pneumoniae in a large German hospital. Using our system, our experts were able to scale the analysis of transmission pathways to longer time intervals (i.e., several years of data instead of days) and across a larger number of wards. Also, the system is able to reduce the analysis time from days to hours. In our final study, feedback from twenty-five experts from seven German hospitals provides evidence that our solution brings significant benefits for analyzing outbreaks.

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