CoWiz: Interactive Covid-19 Visualization Based On Multilayer Network Analysis
CoWiz: Interactive Covid-19 Visualization Based On Multilayer Network Analysis
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DOI：
10.1109/icde51399.2021.00299
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发表时间：
2021-04
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影响因子：
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通讯作者：
Kunal Samant;Endrit Memeti;Abhishek Santra;Enamul Karim;Sharma Chakravarthy
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作者：
Kunal Samant;Endrit Memeti;Abhishek Santra;Enamul Karim;Sharma Chakravarthy
Covid Wizard或CoWiz是一个基于多层网络（MLN）分析的Covid-19可视化仪表板。在线仪表板通常绘制/可视化从原始数据中收集的统计信息，例如每日案例，死亡，恢复，测试等。然而，为了更好地理解，我们需要汇总分析（例如，社区，中心性）及其可视化，这是CoWiz的目的。例如，如果没有汇总分析，就不可能根据病例、死亡、住院时间间隔内的增加/减少的相似性对一个国家/地区的县进行分组。这是CoWiz在MLN上利用社区和其他概念的地方，这些概念是从Covid和其他相关数据集中推断出来的。该演示提供了一个灵活的交互式仪表板，能够可视化Covid-19数据的各个方面，包括Covid数据与人口统计数据的组成（人口密度，教育水平，平均收入，车辆移动和购买模式的变化）在美国县的粒度。本文详细介绍了分析的类型、基础模型以及如何使用开源软件和数据集开发灵活的可视化仪表板。当新数据可用时，它们可以被合并到可视化中，而无需手动干预。
Covid Wizard or CoWiz is a Covid-19 visualization dashboard based on Multilayer Network (MLN) analysis underneath1. 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.