Department of Computer Science, Virginia Tech, Blacksburg, Virginia, USA.
Network Dynamics and Simulation Science Laboratory, Biocomplexity Institute of Virginia Tech, Blacksburg, Virginia, USA.
BMC Bioinformatics. 2018 Nov 22;19(1):449. doi: 10.1186/s12859-018-2439-0.
Visualization plays an important role in epidemic time series analysis and forecasting. Viewing time series data plotted on a graph can help researchers identify anomalies and unexpected trends that could be overlooked if the data were reviewed in tabular form; these details can influence a researcher's recommended course of action or choice of simulation models. However, there are challenges in reviewing data sets from multiple data sources - data can be aggregated in different ways (e.g., incidence vs. cumulative), measure different criteria (e.g., infection counts, hospitalizations, and deaths), or represent different geographical scales (e.g., nation, HHS Regions, or states), which can make a direct comparison between time series difficult. In the face of an emerging epidemic, the ability to visualize time series from various sources and organizations and to reconcile these datasets based on different criteria could be key in developing accurate forecasts and identifying effective interventions. Many tools have been developed for visualizing temporal data; however, none yet supports all the functionality needed for easy collaborative visualization and analysis of epidemic data.
In this paper, we present EpiViewer, a time series exploration dashboard where users can upload epidemiological time series data from a variety of sources and compare, organize, and track how data evolves as an epidemic progresses. EpiViewer provides an easy-to-use web interface for visualizing temporal datasets either as line charts or bar charts. The application provides enhanced features for visual analysis, such as hierarchical categorization, zooming, and filtering, to enable detailed inspection and comparison of multiple time series on a single canvas. Finally, EpiViewer provides several built-in statistical Epi-features to help users interpret the epidemiological curves.
EpiViewer is a single page web application that provides a framework for exploring, comparing, and organizing temporal datasets. It offers a variety of features for convenient filtering and analysis of epicurves based on meta-attribute tagging. EpiViewer also provides a platform for sharing data between groups for better comparison and analysis. Our user study demonstrated that EpiViewer is easy to use and fills a particular niche in the toolspace for visualization and exploration of epidemiological data.
可视化在传染病时间序列分析和预测中起着重要作用。将时间序列数据绘制在图表上可以帮助研究人员识别异常和意外趋势，如果仅以表格形式查看数据，这些细节可能会被忽略；这些细节可能会影响研究人员建议的行动方案或模拟模型的选择。然而，从多个数据源查看数据集存在挑战 - 数据可以以不同的方式聚合（例如，发病率与累计），衡量不同的标准（例如，感染计数、住院和死亡），或代表不同的地理尺度（例如，国家、HHS 地区或州），这使得直接比较时间序列变得困难。在出现新传染病时，能够可视化来自各种来源和组织的时间序列，并根据不同的标准调和这些数据集，这可能是制定准确预测和确定有效干预措施的关键。已经开发了许多用于可视化时间数据的工具；然而，没有一个工具支持轻松协作可视化和分析传染病数据所需的所有功能。
在本文中，我们介绍了 EpiViewer，这是一个时间序列探索仪表板，用户可以从各种来源上传传染病时间序列数据，并比较、组织和跟踪数据随着传染病的进展如何演变。EpiViewer 为可视化时间数据集提供了一个易于使用的 Web 界面，可以以线图或条形图的形式显示。该应用程序提供了用于视觉分析的增强功能，例如层次分类、缩放和过滤，以实现对单个画布上多个时间序列的详细检查和比较。最后，EpiViewer 提供了几个内置的统计 Epi 功能，以帮助用户解释流行病学曲线。
EpiViewer 是一个单页 Web 应用程序，为探索、比较和组织时间数据集提供了一个框架。它提供了各种功能，可方便地根据元属性标记对 epi 曲线进行过滤和分析。EpiViewer 还为群组之间的数据共享提供了一个平台，以进行更好的比较和分析。我们的用户研究表明，EpiViewer 易于使用，并且在可视化和探索流行病学数据的工具空间中填补了一个特定的空白。
