Visual Analytics of Contact Tracing Policy Simulations During an Emergency Response
Visual Analytics of Contact Tracing Policy Simulations During an Emergency Response
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DOI：
10.1111/cgf.14520
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发表时间：
2022-06
影响因子：
2.5
通讯作者：
Max Sondag;C. Turkay;Kai Xu;L. Matthews;S. Mohr;D. Archambault
中科院分区：
文献类型：
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作者：
Max Sondag;C. Turkay;Kai Xu;L. Matthews;S. Mohr;D. Archambault
流行病学家使用基于个体的模型来（a）模拟疾病在动态接触网络上的传播，（B）研究控制疫情的策略。这些模型模拟生成了复杂的“感染地图”，包括随时间变化的传播树和传播模式。对产出的传统统计分析只能提供有限的解释。本文提出了一种新的可视化分析方法，用于检查感染地图沿着及其相关的元数据，在不断变化的应急响应情况下，合作开发了16个月。我们引入了代表树的概念，它总结了随时间变化的感染图的许多组成部分，同时保留了每个传播树的流行病学特征。我们还提出了交互式可视化技术，用于快速评估不同的控制策略。通过一系列案例研究和流行病学家的定性评估，我们展示了我们的可视化如何帮助改进流行病学模型的开发，并帮助解释复杂的传播模式。
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.