As the COVID-19 pandemic continues to impact the world, data is being gathered and analyzed to better understand the disease. Recognizing the potential for visual analytics technologies to support exploratory analysis and hypothesis generation from longitudinal clinical data, a team of collaborators worked to apply existing event sequence visual analytics technologies to a longitudinal clinical data from a cohort of 998 patients with high rates of COVID-19 infection. This paper describes the initial steps toward this goal, including: (1) the data transformation and processing work required to prepare the data for visual analysis, (2) initial findings and observations, and (3) qualitative feedback and lessons learned which highlight key features as well as limitations to address in future work.

翻译：由于COVID-19大流行继续影响世界,正在收集和分析数据,以更好地了解该疾病;认识到视觉分析技术支持探索性分析和从纵向临床数据生成假设的可能性,一组合作者努力将现有事件序列视觉分析技术应用于998名COVID-19感染率高的病人的纵向临床数据;本文件介绍了实现这一目标的初步步骤,包括:(1) 编制用于视觉分析的数据所需的数据转换和处理工作;(2) 初步结果和观察;(3) 定性反馈和经验教训,其中突出了关键特征以及未来工作中处理的局限性。