Using Social Network Analysis to Identify Spatiotemporal Spread Patterns of Covid-19 around the World
Online Dashboard Development
Bibliographic Data
| ID | 15461745 |
|---|---|
| Authors | Kyent-Yon Yie (Chi Mei Medical Center), Tsair‐Wei Chien (0000-0003-1329-0679, Chi Mei Medical Center), Yu-Tsen Yeh (0000-0002-6593-9209, St George's, University of London), Willy Chou (0000-0002-1132-9341, Chi Mei Medical Center, corresponding author), Shih‐Bin Su (0000-0001-8348-1433, Chi Mei Medical Center, corresponding author) |
| Year | 2021 |
| Volume | 18 |
| Issue | 5 |
| Pages | 2461-2461 |
| Publication date | 2021-03-03 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph18052461 |
| PMID | 33802247 |
| OpenAlex | W3135878652 |
| Language | EN |
| Citations received | 6 |
| References cited | 35 |
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
China · Cluster (spacecraft · Coronavirus disease 2019 (COVID-19 · Dashboard · Data science · Geography · Pandemic · Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 · Complex Network Analysis Techniques · Computer Science · COVID-19 epidemiological studies · Medicine · SARS-CoV-2 and COVID-19 Research
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| Unique citing works | 6 |
|---|---|
| Citations per year | 1.2 |
| Citation span | 2021 - 2026 (6) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 6 |