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Visual Analytics of Contact Tracing Policy Simulations During an Emergency Response

Max Sondag Sondag, C. Turkay Orcid Logo, K. Xu Orcid Logo, L. Matthews Orcid Logo, S. Mohr Orcid Logo, Daniel Archambault Orcid Logo

Computer Graphics Forum, Volume: 41, Issue: 3, Pages: 29 - 41

Swansea University Authors: Max Sondag Sondag, Daniel Archambault Orcid Logo

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DOI (Published version): 10.1111/cgf.14520

Abstract

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...

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Published in: Computer Graphics Forum
ISSN: 0167-7055 1467-8659
Published: EuroVis 2022 Wiley 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa59803
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spelling 2022-10-31T14:28:26.5135834 v2 59803 2022-04-12 Visual Analytics of Contact Tracing Policy Simulations During an Emergency Response cddcb8ff6471133067229223edabfe98 Max Sondag Sondag Max Sondag Sondag true false 8fa6987716a22304ef04d3c3d50ef266 0000-0003-4978-8479 Daniel Archambault Daniel Archambault true false 2022-04-12 SCS 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. Journal Article Computer Graphics Forum 41 3 29 41 Wiley EuroVis 2022 0167-7055 1467-8659 CCS concepts, applied computing, health informatics, human-centered computing, visual analytics 1 6 2022 2022-06-01 10.1111/cgf.14520 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University SU Library paid the OA fee (TA Institutional Deal) This work was funded by the UKRI EPSRC grants EP/V033670/1 and EP/V054236/1. 2022-10-31T14:28:26.5135834 2022-04-12T14:28:50.3939120 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Max Sondag Sondag 1 C. Turkay 0000-0001-6788-251x 2 K. Xu 0000-0003-2242-5440 3 L. Matthews 0000-0003-4420-8367 4 S. Mohr 0000-0002-9089-6327 5 Daniel Archambault 0000-0003-4978-8479 6 59803__24901__a609d03133e740ad9553271fede247ac.pdf 59803.VOR.pdf 2022-08-09T16:30:54.8183289 Output 681845 application/pdf Version of Record true © 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. true eng http://creativecommons.org/licenses/by/4.0/
title Visual Analytics of Contact Tracing Policy Simulations During an Emergency Response
spellingShingle Visual Analytics of Contact Tracing Policy Simulations During an Emergency Response
Max Sondag Sondag
Daniel Archambault
title_short Visual Analytics of Contact Tracing Policy Simulations During an Emergency Response
title_full Visual Analytics of Contact Tracing Policy Simulations During an Emergency Response
title_fullStr Visual Analytics of Contact Tracing Policy Simulations During an Emergency Response
title_full_unstemmed Visual Analytics of Contact Tracing Policy Simulations During an Emergency Response
title_sort Visual Analytics of Contact Tracing Policy Simulations During an Emergency Response
author_id_str_mv cddcb8ff6471133067229223edabfe98
8fa6987716a22304ef04d3c3d50ef266
author_id_fullname_str_mv cddcb8ff6471133067229223edabfe98_***_Max Sondag Sondag
8fa6987716a22304ef04d3c3d50ef266_***_Daniel Archambault
author Max Sondag Sondag
Daniel Archambault
author2 Max Sondag Sondag
C. Turkay
K. Xu
L. Matthews
S. Mohr
Daniel Archambault
format Journal article
container_title Computer Graphics Forum
container_volume 41
container_issue 3
container_start_page 29
publishDate 2022
institution Swansea University
issn 0167-7055
1467-8659
doi_str_mv 10.1111/cgf.14520
publisher Wiley
college_str Faculty of Science and Engineering
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hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
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description 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.
published_date 2022-06-01T04:17:22Z
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