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Using Social Network Analysis to Identify Spatiotemporal Spread Patterns of COVID-19 around the World: Online Dashboard Development

Author

Listed:
  • Kyent-Yon Yie

    (Department of Gastrointestinal Hepatobiliary, Chi Mei Jiali Hospital, Tainan 700, Taiwan)

  • Tsair-Wei Chien

    (Department of Medical Research, Chi-Mei Hospital, Tainan 700, Taiwan)

  • Yu-Tsen Yeh

    (Medical School, St. George’s University of London, London SW17 0RE, UK)

  • Willy Chou

    (Department of Physical Medicine and Rehabilitation, Chi Mei Medical Center, Tainan 700, Taiwan)

  • Shih-Bin Su

    (Department of Occupational Medicine, Chi Mei Medical Center, Tainan 700, Taiwan)

Abstract

The COVID-19 pandemic has spread widely around the world. Many mathematical models have been proposed to investigate the inflection point (IP) and the spread pattern of COVID-19. However, no researchers have applied social network analysis (SNA) to cluster their characteristics. We aimed to illustrate the use of SNA to identify the spread clusters of COVID-19. Cumulative numbers of infected cases (CNICs) in countries/regions were downloaded from GitHub. The CNIC patterns were extracted from SNA based on CNICs between countries/regions. The item response model (IRT) was applied to create a general predictive model for each country/region. The IP days were obtained from the IRT model. The location parameters in continents, China, and the United States were compared. The results showed that (1) three clusters (255, n = 51, 130, and 74 in patterns from Eastern Asia and Europe to America) were separated using SNA, (2) China had a shorter mean IP and smaller mean location parameter than other counterparts, and (3) an online dashboard was used to display the clusters along with IP days for each country/region. Spatiotemporal spread patterns can be clustered using SNA and correlation coefficients (CCs). A dashboard with spread clusters and IP days is recommended to epidemiologists and researchers and is not limited to the COVID-19 pandemic.

Suggested Citation

  • Kyent-Yon Yie & Tsair-Wei Chien & Yu-Tsen Yeh & Willy Chou & Shih-Bin Su, 2021. "Using Social Network Analysis to Identify Spatiotemporal Spread Patterns of COVID-19 around the World: Online Dashboard Development," IJERPH, MDPI, vol. 18(5), pages 1-15, March.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:5:p:2461-:d:509239
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    References listed on IDEAS

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    1. Lin-Yen Wang & Tsair-Wei Chien & Willy Chou, 2021. "Using the IPcase Index with Inflection Points and the Corresponding Case Numbers to Identify the Impact Hit by COVID-19 in China: An Observation Study," IJERPH, MDPI, vol. 18(4), pages 1-16, February.
    2. Huiqin Chen & Lei Chen, 2017. "Support Vector Machine Classification of Drunk Driving Behaviour," IJERPH, MDPI, vol. 14(1), pages 1-14, January.
    3. Jinling Quan, 2019. "Multi-Temporal Effects of Urban Forms and Functions on Urban Heat Islands Based on Local Climate Zone Classification," IJERPH, MDPI, vol. 16(12), pages 1-35, June.
    4. JinSoo Park & Sungroul Kim, 2020. "Machine Learning-Based Activity Pattern Classification Using Personal PM 2.5 Exposure Information," IJERPH, MDPI, vol. 17(18), pages 1-11, September.
    5. Thomas Warm, 1989. "Weighted likelihood estimation of ability in item response theory," Psychometrika, Springer;The Psychometric Society, vol. 54(3), pages 427-450, September.
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    2. Po-Hsin Chou & Jui-Chung John Lin & Tsair-Wei Chien, 2023. "Using text mining and forest plots to identify similarities and differences between two spine-related journals based on medical subject headings (MeSH terms) and author-specified keywords in 100 top-c," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 1-17, January.
    3. Zhangbo Yang & Jiahao Zhang & Shanxing Gao & Hui Wang, 2022. "Complex Contact Network of Patients at the Beginning of an Epidemic Outbreak: An Analysis Based on 1218 COVID-19 Cases in China," IJERPH, MDPI, vol. 19(2), pages 1-17, January.
    4. Ekaterina Ignatenko & Manuel Ribeiro & Mónica D. Oliveira, 2022. "Informing the Design of Data Visualization Tools to Monitor the COVID-19 Pandemic in Portugal: A Web-Delphi Participatory Approach," IJERPH, MDPI, vol. 19(17), pages 1-18, September.

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