Dynamic Network Analysis of COVID-19 with a Latent Pandemic Space Model.
Amanda M Y Chu, Thomas W C Chan, Mike K P So, Wing-Keung Wong
Author Information
Amanda M Y Chu: Department of Social Sciences, The Education University of Hong Kong, Tai Po, Hong Kong. ORCID
Thomas W C Chan: Department of Information Systems, Business Statistics and Operations Management, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong. ORCID
Mike K P So: Department of Information Systems, Business Statistics and Operations Management, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong. ORCID
Wing-Keung Wong: Department of Finance, Fintech & Blockchain Research Center, and Big Data Research Center, Asia University, Taichung 41354, Taiwan. ORCID
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.
