CoWiz: Interactive Covid-19 Visualization Based On Multilayer Network Analysis

被引:1
|
作者
Samant, Kunal [1 ]
Memeti, Endrit
Santra, Abhishek
Karim, Enamul
Chakravarthy, Sharma
机构
[1] Univ Texas Arlington, IT Lab, Arlington, TX 76019 USA
基金
美国国家科学基金会;
关键词
Community Detection; Modeling Using MLNs; Composition using Decoupling Approach; Covid-19 data Analysis;
D O I
10.1109/ICDE51399.2021.00299
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Covid Wizard or CoWiz is a Covid-19 visualization dashboard based on Multilayer Network (MLN) analysis underneath(1). Online dashboards typically plot/visualize statistical information gleaned from raw data, such as daily cases, deaths, recoveries, tests, etc. However, for a better understanding, we need aggregate analysis (e.g., community, centrality) and its visualization which is the purpose of CoWiz. As an example, grouping counties across a country/region based on similarity of increase/decrease in cases, deaths, hospitalizations over intervals is not possible without aggregate analysis. This is where CoWiz utilizes community and other concepts over MLNs that are inferred from Covid and other relevant data sets for visualization. This demo presents a flexible, interactive dashboard which is capable of visualizing various aspects of Covid-19 data, including composition of Covid data with demographics (population density, education level, average earning, vehicle movements, and change in purchase patterns) at the granularity of county for USA. This paper elaborates on the types of analysis, underlying model, and how a flexible visualization dashboard has been developed using open source software and data sets. As new data becomes available, they can be incorporated into the visualization with no manual intervention.
引用
收藏
页码:2665 / 2668
页数:4
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