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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
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa59803
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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 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.
Keywords: CCS concepts, applied computing, health informatics, human-centered computing, visual analytics
College: Faculty of Science and Engineering
Funders: This work was funded by the UKRI EPSRC grants EP/V033670/1 and EP/V054236/1.
Issue: 3
Start Page: 29
End Page: 41