A visual modeling method for spatiotemporal and multidimensional features in epidemiological analysis: Applied COVID-19 aggregated datasets

IF 17.3 3区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING Computational Visual Media Pub Date : 2023-11-30 DOI:10.1007/s41095-023-0353-5
Yu Dong, Christy Jie Liang, Yi Chen, Jie Hua
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Abstract

The visual modeling method enables flexible interactions with rich graphical depictions of data and supports the exploration of the complexities of epidemiological analysis. However, most epidemiology visualizations do not support the combined analysis of objective factors that might influence the transmission situation, resulting in a lack of quantitative and qualitative evidence. To address this issue, we developed a portrait-based visual modeling method called +msRNAer. This method considers the spatiotemporal features of virus transmission patterns and multidimensional features of objective risk factors in communities, enabling portrait-based exploration and comparison in epidemiological analysis. We applied +msRNAer to aggregate COVID-19-related datasets in New South Wales, Australia, combining COVID-19 case number trends, geo-information, intervention events, and expert-supervised risk factors extracted from local government area-based censuses. We perfected the +msRNAer workflow with collaborative views and evaluated its feasibility, effectiveness, and usefulness through one user study and three subject-driven case studies. Positive feedback from experts indicates that +msRNAer provides a general understanding for analyzing comprehension that not only compares relationships between cases in time-varying and risk factors through portraits but also supports navigation in fundamental geographical, timeline, and other factor comparisons. By adopting interactions, experts discovered functional and practical implications for potential patterns of long-standing community factors regarding the vulnerability faced by the pandemic. Experts confirmed that +msRNAer is expected to deliver visual modeling benefits with spatiotemporal and multidimensional features in other epidemiological analysis scenarios.

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流行病学分析中时空和多维特征的可视化建模方法:应用COVID-19汇总数据集
可视化建模方法能够与丰富的数据图形描述进行灵活互动,并支持探索复杂的流行病学分析。然而,大多数流行病学可视化方法不支持对可能影响传播情况的客观因素进行综合分析,从而导致缺乏定量和定性证据。为了解决这个问题,我们开发了一种基于肖像的可视化建模方法,名为 +msRNAer。该方法考虑了病毒传播模式的时空特征和社区中客观风险因素的多维特征,可在流行病学分析中进行基于肖像的探索和比较。我们将 +msRNAer 应用于澳大利亚新南威尔士州 COVID-19 相关数据集的汇总,结合了 COVID-19 病例数趋势、地理信息、干预事件以及从地方政府地区人口普查中提取的专家监督风险因素。我们利用协作视图完善了 +msRNAer 工作流程,并通过一项用户研究和三项主题驱动的案例研究评估了其可行性、有效性和实用性。专家们的积极反馈表明,+msRNAer 为分析理解提供了一种一般理解,不仅通过肖像比较了病例之间在时间变化和风险因素方面的关系,而且还支持在基本地理、时间线和其他因素比较方面的导航。通过采用交互方式,专家们发现了长期存在的社区因素对大流行病所面临的脆弱性的潜在模式的功能和实际影响。专家们确认,+msRNAer 预计将在其他流行病学分析方案中提供具有时空和多维特征的可视化建模优势。
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来源期刊
Computational Visual Media
Computational Visual Media Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
16.90
自引率
5.80%
发文量
243
审稿时长
6 weeks
期刊介绍: Computational Visual Media is a peer-reviewed open access journal. It publishes original high-quality research papers and significant review articles on novel ideas, methods, and systems relevant to visual media. Computational Visual Media publishes articles that focus on, but are not limited to, the following areas: • Editing and composition of visual media • Geometric computing for images and video • Geometry modeling and processing • Machine learning for visual media • Physically based animation • Realistic rendering • Recognition and understanding of visual media • Visual computing for robotics • Visualization and visual analytics Other interdisciplinary research into visual media that combines aspects of computer graphics, computer vision, image and video processing, geometric computing, and machine learning is also within the journal''s scope. This is an open access journal, published quarterly by Tsinghua University Press and Springer. The open access fees (article-processing charges) are fully sponsored by Tsinghua University, China. Authors can publish in the journal without any additional charges.
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