Wunderlich, Marcel and Block, Isabelle and Landesberger, Tatiana von and Petzold, Markus and Marschollek, Michael and 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 |
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Erschienen: | 2019 |
Creators: | Wunderlich, Marcel and Block, Isabelle and Landesberger, Tatiana von and Petzold, Markus and Marschollek, Michael and 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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Suche nach Titel in: | TUfind oder in Google |
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