Wu Junqi, Niu Zhibin, Liu Xiufeng
College of Intelligence and Computing, Tianjin University, Tianjin, China.
Department of Technology, Management and Economics, Technical University of Denmark, Lyngby, Denmark.
Health Syst (Basingstoke). 2024 Jan 25;13(3):229-245. doi: 10.1080/20476965.2024.2308286. eCollection 2024.
Epidemics present significant challenges for public health policy globally, but current tools for visualizing and analyzing epidemic spread are limited, especially at a large scale. This paper presents a novel visual analysis approach for exploring and comparing pandemic patterns in spatial and temporal dimensions across various regions. The method incorporates a potential flow technique to model the spatiotemporal dynamics of epidemics and a visual exploration tool, EPViz, for interactive data analysis. Utilizing COVID-19 data from Illinois and Pennsylvania in the United States, the paper evaluates the method and tool's effectiveness. These states were chosen for their differing epidemic scenarios and policies. Additionally, interviews with public health policy experts were conducted to gather feedback on the approach and EPViz's effectiveness, design, and usability. The findings indicate that this new approach and tool enhance expert understanding, support decision-making, and can inform effective strategies for epidemic prevention and control.
流行病给全球公共卫生政策带来了重大挑战,但目前用于可视化和分析疫情传播的工具有限,尤其是在大规模层面。本文提出了一种新颖的视觉分析方法,用于探索和比较不同地区在空间和时间维度上的大流行模式。该方法采用了一种潜在流技术来模拟流行病的时空动态,并使用了一个视觉探索工具EPViz进行交互式数据分析。利用美国伊利诺伊州和宾夕法尼亚州的新冠肺炎数据,本文评估了该方法和工具的有效性。选择这两个州是因为它们不同的疫情情况和政策。此外,还对公共卫生政策专家进行了访谈,以收集他们对该方法以及EPViz的有效性、设计和可用性的反馈。研究结果表明,这种新方法和工具增强了专家的理解,支持决策制定,并可为有效的疫情防控策略提供参考。