Visual Analytics for Explaining and Analysing Contact Tracing Networks

用于解释和分析接触者追踪网络的可视化分析

• 批准号: EP/V033670/1
• 负责人: Daniel Archambault
• 金额: $ 37.22万
• 依托单位: Swansea University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FV033670%2F1
• 关键词: Visual; Analytics; Explaining; Analysing; Contact

Contact tracing networks carry invaluable information for researchers to understand the spread of the virus, for policy-makers to control the COVID-19 outbreak, and for the government and the media in informing the public in rich ways. However, current data science tools fall short for the exploratory and explanatory analysis of the temporal, spatial and social aspects of these networks, and little is known on how most effectively the results of such analyses can be communicated broadly. This lack of a toolbox leads to organisations wasting resources on developing partial solutions designed without broad stakeholder engagement. To this end, this project aims to follow a user-centred approach to develop visual analytics methods for the analysis of large collections of contact tracing networks along with techniques for the communication of analysis results in transparent, comprehensive, yet engaging ways. Contact networks come with noteworthy technical and ethical challenges: inherent uncertainties due to the variation in their generation mechanisms, e.g., apps, hospital records, by volunteers; and high volumes of complex and sensitive information represented as event-based interactions with spatio-temporal facets. This project responds to these challenges through two deliverables comprising visualisation methods working simultaneously at group and individual levels while communicating the general trends in the spread:1. Visualisations aimed at experts for understanding collections of contact networks to inform public health policies and make in-depth investigations without compromising individuals' privacy.2. Visualisations for communicating analysis results with the general public for information and evidencing policy recommendations with representations having a purely explanatory emphasis.

接触者追踪网络为研究人员了解病毒的传播、政策制定者控制COVID-19疫情以及政府和媒体以丰富的方式向公众提供信息提供了宝贵的信息。然而，目前的数据科学工具不足以对这些网络的时间、空间和社会方面进行探索性和解释性分析，而且对于如何最有效地广泛传播此类分析的结果知之甚少。这种工具箱的缺乏导致组织浪费资源开发没有广泛利益相关者参与的部分解决方案。为此，该项目旨在遵循以用户为中心的方法，开发用于分析大量接触者追踪网络的可视化分析方法，同时沿着以透明、全面但引人入胜的方式交流分析结果的技术。接触网络带来了值得注意的技术和伦理挑战：由于其生成机制的变化而产生的固有不确定性，例如，志愿者的应用程序、医院记录;以及大量复杂和敏感的信息，这些信息被表示为与时空方面的基于事件的交互。该项目通过两个可交付成果来应对这些挑战，其中包括在群体和个人层面同时工作的可视化方法，同时传达传播的总体趋势：1.可视化的目的是让专家了解接触网络的集合，以告知公共卫生政策，并在不损害个人隐私的情况下进行深入调查。可视化，用于与公众交流分析结果，以获取信息，并证明政策建议，其中陈述具有纯粹的解释性重点。

项目成果

Towards explainable community finding.

• DOI: 10.1007/s41109-022-00515-6
• 发表时间: 2022
• 期刊: Applied network science
• 影响因子: 2.2

Edge-Path Bundling: A Less Ambiguous Edge Bundling Approach

边缘路径捆绑：一种不太模糊的边缘捆绑方法

• DOI: 10.48550/arxiv.2108.05467
• 发表时间: 2021
• 作者: Wallinger M

From Asymptomatics to Zombies: Visualization-Based Education of Disease Modeling for Children

• DOI: 10.1145/3544548.3581573
• 发表时间: 2023-04
• 期刊: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
• 作者: Graham Mcneill;Max Sondag;Stewart Powell;P. Asplin;C. Turkay;Faron Moller;D. Archambault

Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations.

流行病学建模的可视化：挑战，解决方案，反思和建议。

• DOI: 10.1098/rsta.2021.0299
• 发表时间: 2022-10-03
• 期刊: PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
• 影响因子: 5
• 作者: Dykes, Jason;Abdul-Rahman, Alfie;Archambault, Daniel;Bach, Benjamin;Borgo, Rita;Chen, Min;Enright, Jessica;Fang, Hui;Firat, Elif E.;Freeman, Euan;Gonen, Tuna;Harris, Claire;Jianu, Radu;John, Nigel W.;Khan, Saiful;Lahiff, Andrew;Laramee, Robert S.;Matthews, Louise;Mohr, Sibylle;Nguyen, Phong H.;Rahat, Alma A. M.;Reeve, Richard;Ritsos, Panagiotis D.;Roberts, Jonathan C.;Slingsby, Aidan;Swallow, Ben;Torsney-Weir, Thomas;Turkay, Cagatay;Turner, Robert;Vidal, Franck P.;Wang, Qiru;Wood, Jo;Xu, Kai
• 通讯作者: Xu, Kai

Visual Analytics of Contact Tracing Policy Simulations During an Emergency Response

• DOI: 10.1111/cgf.14520
• 发表时间: 2022-06
• 期刊: Computer Graphics Forum
• 影响因子: 2.5
• 作者: Max Sondag;C. Turkay;Kai Xu;L. Matthews;S. Mohr;D. Archambault