Dynamic network analysis of COVID-19 with a latent pandemic space model

Man Ying Amanda CHU, Thomas W. C. CHAN, Mike K. P. SO, Wing-Keung WONG

Research output: Contribution to journalArticlespeer-review

Abstract

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. Copyright © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Original languageEnglish
Article number3195
JournalInternational Journal of Environmental Research and Public Health
Volume18
Issue number6
DOIs
Publication statusPublished - 02 Mar 2021

Citation

Chu, A. M. Y., Chan, T. W. C., So, M. K. P., & Wong, W.-K. (2021). Dynamic network analysis of COVID-19 with a latent pandemic space model. International Journal of Environmental Research and Public Health, 18(6). Retrieved from https://doi.org/10.3390/ijerph18063195

Keywords

  • Coronavirus
  • Network modeling
  • Pandemic nowcasting
  • Pandemic risk visualization
  • Pandemic network analysis
  • Pandemic space

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