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

Kyent-Yon Yie, Tsair-Wei Chien, Yu-Tsen Yeh, Willy Chou and Shih-Bin Su
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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

IJERPH, 2021, vol. 18, issue 5, 1-15

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.

Keywords: COVID-19; social network analysis; item response model; correlation coefficient; daily confirmed case; spatiotemporal spread pattern (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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