Journal article 235 views 8 downloads
Visual Analytics of Contact Tracing Policy Simulations During an Emergency Response
Computer Graphics Forum, Volume: 41, Issue: 3, Pages: 29 - 41
Swansea University Authors: Max Sondag Sondag, Daniel Archambault
PDF | Version of Record
© 2022 The Authors. Computer Graphics Forum published by Eurographics - The European Association for Computer Graphics and John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.Download (665.86KB)
DOI (Published version): 10.1111/cgf.14520
Epidemiologists use individual-based models to (a) simulate disease spread over dynamic contact networks and (b) to investigate strategies to control the outbreak. These model simulations generate complex ‘infection maps’ of time-varying transmission trees and patterns of spread. Conventional statis...
|Published in:||Computer Graphics Forum|
Check full text
No Tags, Be the first to tag this record!
Epidemiologists use individual-based models to (a) simulate disease spread over dynamic contact networks and (b) to investigate strategies to control the outbreak. These model simulations generate complex ‘infection maps’ of time-varying transmission trees and patterns of spread. Conventional statistical analysis of outputs offers only limited interpretation. This paper presents a novel visual analytics approach for the inspection of infection maps along with their associated metadata, developed collaboratively over 16 months in an evolving emergency response situation. We introduce the concept of representative trees that summarize the many components of a time-varying infection map while preserving the epidemiological characteristics of each individual transmission tree. We also present interactive visualization techniques for the quick assessment of different control policies. Through a series of case studies and a qualitative evaluation by epidemiologists, we demonstrate how our visualizations can help improve the development of epidemiological models and help interpret complex transmission patterns.
CCS concepts, applied computing, health informatics, human-centered computing, visual analytics
Faculty of Science and Engineering
This work was funded by the UKRI EPSRC grants EP/V033670/1 and EP/V054236/1.