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Visual Analysis of Probabilistic Infection Contagion in Hospitals

Wunderlich, Marcel ; Block, Isabelle ; Landesberger von Antburg, Tatiana ; Petzold, Markus ; Marschollek, Michael ; Scheithauer, Simone (2019):
Visual Analysis of Probabilistic Infection Contagion in Hospitals.
pp. 143-150, 24. Vision, Modeling, and Visualization (VMV), Rostock, 30.09.-02.10.19, DOI: 10.2312/vmv.20191328,
[Conference or Workshop Item]

Abstract

Clinicians and hygienists need to know how an infection of one patient could be transmitted among other patients in the hospital (e.g., to prevent outbreaks). They need to analyze how many and which patients will possibly be infected, how fast the infection could spread, and which contacts are likely to transfer the infections within the hospital. Currently, infection contagion is modeled and visualized for populations only on an aggregate level, without identification and exploration of possible infection between individuals. We present a novel visual analytics approach that simulates the contagion in a contact graph of patients in a hospital. We propose a clustering approach to identify probable contagion scenarios in the simulation ensemble. Furthermore, our novel visual design for detailed assessment of transmission shows the temporal development of contagion per patient in one view. We demonstrate the capability of our approach to a real-world use case in a German hospital.

Item Type: Conference or Workshop Item
Erschienen: 2019
Creators: Wunderlich, Marcel ; Block, Isabelle ; Landesberger von Antburg, Tatiana ; Petzold, Markus ; Marschollek, Michael ; Scheithauer, Simone
Title: Visual Analysis of Probabilistic Infection Contagion in Hospitals
Language: English
Abstract:

Clinicians and hygienists need to know how an infection of one patient could be transmitted among other patients in the hospital (e.g., to prevent outbreaks). They need to analyze how many and which patients will possibly be infected, how fast the infection could spread, and which contacts are likely to transfer the infections within the hospital. Currently, infection contagion is modeled and visualized for populations only on an aggregate level, without identification and exploration of possible infection between individuals. We present a novel visual analytics approach that simulates the contagion in a contact graph of patients in a hospital. We propose a clustering approach to identify probable contagion scenarios in the simulation ensemble. Furthermore, our novel visual design for detailed assessment of transmission shows the temporal development of contagion per patient in one view. We demonstrate the capability of our approach to a real-world use case in a German hospital.

Uncontrolled Keywords: Visual analytics Hospital applications Contagion effects Dynamic graph layouts Clustering
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: 24. Vision, Modeling, and Visualization (VMV)
Event Location: Rostock
Event Dates: 30.09.-02.10.19
Date Deposited: 14 Apr 2020 06:46
DOI: 10.2312/vmv.20191328
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