Department of Social Sciences, The Education University of Hong Kong, Tai Po, Hong Kong.
Department of Information Systems, Business Statistics and Operations Management, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong.
Int J Environ Res Public Health. 2021 Mar 19;18(6):3195. doi: 10.3390/ijerph18063195.
In this paper, we propose a latent pandemic space modeling approach for analyzing coronavirus disease 2019 (COVID-19) pandemic data. We developed a pandemic space concept that locates different regions so that their connections can be quantified according to the distances between them. A main feature of the pandemic space is to allow visualization of the pandemic status over time through the connectedness between regions. We applied the latent pandemic space model to dynamic pandemic networks constructed using data of confirmed cases of COVID-19 in 164 countries. We observed the ways in which pandemic risk evolves by tracing changes in the locations of countries within the pandemic space. Empirical results gained through this pandemic space analysis can be used to quantify the effectiveness of lockdowns, travel restrictions, and other measures in regard to reducing transmission risk across countries.
在本文中，我们提出了一种潜在的大流行空间建模方法，用于分析 2019 年冠状病毒病（COVID-19）大流行数据。我们开发了一个大流行空间的概念，它定位了不同的区域，以便可以根据它们之间的距离来量化它们的连接。大流行空间的一个主要特点是允许通过区域之间的连接来可视化随时间推移的大流行状况。我们将潜在的大流行空间模型应用于使用 164 个国家的 COVID-19 确诊病例数据构建的动态大流行网络。我们通过跟踪大流行空间中各国位置的变化来观察大流行风险的演变方式。通过这种大流行空间分析获得的实证结果可用于量化封锁、旅行限制和其他措施在降低国家间传播风险方面的有效性。
