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Dynamic Network Analysis of COVID-19 with a Latent Pandemic Space Model

Author

Listed:
  • Amanda M. Y. Chu

    (Department of Social Sciences, The Education University of Hong Kong, Tai Po, Hong Kong)

  • 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)

  • 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)

  • Wing-Keung Wong

    (Department of Finance, Fintech & Blockchain Research Center, and Big Data Research Center, Asia University, Taichung 41354, Taiwan
    Department of Medical Research, China Medical University Hospital, Taichung 404, Taiwan
    Department of Economics and Finance, The Hang Seng University of Hong Kong, Shatin, Hong Kong)

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.

Suggested Citation

  • Amanda M. Y. Chu & Thomas W. C. Chan & Mike K. P. So & Wing-Keung Wong, 2021. "Dynamic Network Analysis of COVID-19 with a Latent Pandemic Space Model," IJERPH, MDPI, vol. 18(6), pages 1-22, March.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:6:p:3195-:d:520529
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    References listed on IDEAS

    as
    1. Christian M. Hafner, 2020. "The Spread of the Covid-19 Pandemic in Time and Space," IJERPH, MDPI, vol. 17(11), pages 1-13, May.
    2. Hoff P.D. & Raftery A.E. & Handcock M.S., 2002. "Latent Space Approaches to Social Network Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1090-1098, December.
    3. So, Mike K.P. & Chan, Raymond K.S., 2014. "Bayesian analysis of tail asymmetry based on a threshold extreme value model," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 568-587.
    4. Kevin Linka & Mathias Peirlinck & Francisco Sahli Costabal & Ellen Kuhl, 2020. "Outbreak dynamics of COVID-19 in Europe and the effect of travel restrictions," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 23(11), pages 710-717, August.
    5. Wang, Yixin & So, Mike K.P., 2016. "A Bayesian hierarchical model for spatial extremes with multiple durations," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 39-56.
    6. Nakamura, Hiroki & Managi, Shunsuke, 2020. "Airport risk of importation and exportation of the COVID-19 pandemic," Transport Policy, Elsevier, vol. 96(C), pages 40-47.
    7. Sabina Saccomanno & Mauro Bernabei & Fabio Scoppa & Alessio Pirino & Rodolfo Mastrapasqua & Marina Angela Visco, 2020. "Coronavirus Lockdown as a Major Life Stressor: Does It Affect TMD Symptoms?," IJERPH, MDPI, vol. 17(23), pages 1-13, November.
    8. Daniel J McGrail & Jianli Dai & Kathleen M McAndrews & Raghu Kalluri, 2020. "Enacting national social distancing policies corresponds with dramatic reduction in COVID19 infection rates," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-9, July.
    9. Goodell, John W. & Goutte, Stephane, 2021. "Co-movement of COVID-19 and Bitcoin: Evidence from wavelet coherence analysis," Finance Research Letters, Elsevier, vol. 38(C).
    10. Seongryu Bae & Kenji Harada & Ippei Chiba & Keitaro Makino & Osamu Katayama & Sangyoon Lee & Yohei Shinkai & Hiroyuki Shimada, 2020. "A New Social Network Scale for Detecting Depressive Symptoms in Older Japanese Adults," IJERPH, MDPI, vol. 17(23), pages 1-13, November.
    11. Daniel K. Sewell & Yuguo Chen, 2015. "Latent Space Models for Dynamic Networks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1646-1657, December.
    12. Peter D. Hoff, 2005. "Bilinear Mixed-Effects Models for Dyadic Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 286-295, March.
    13. So, Mike K.P. & Yeung, Cherry Y.T., 2014. "Vine-copula GARCH model with dynamic conditional dependence," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 655-671.
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    Cited by:

    1. Sumia Mumtaz & Amanda M. Y. Chu & Saman Attiq & Hassan Jalil Shah & Wing-Keung Wong, 2022. "Habit—Does It Matter? Bringing Habit and Emotion into the Development of Consumer’s Food Waste Reduction Behavior with the Lens of the Theory of Interpersonal Behavior," IJERPH, MDPI, vol. 19(10), pages 1-24, May.
    2. Rastko Jovanović & Miloš Davidović & Ivan Lazović & Maja Jovanović & Milena Jovašević-Stojanović, 2021. "Modelling Voluntary General Population Vaccination Strategies during COVID-19 Outbreak: Influence of Disease Prevalence," IJERPH, MDPI, vol. 18(12), pages 1-18, June.

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