ArticleScientific reports2021
A social network analysis of the spread of COVID-19 in South Korea and policy implications.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- COVID-19 transmission dynamics: age-specific patterns and super spreaders in South Korea.BMC public health · 2026Article
- Analysis of the patient-sharing network in hypertension management: a retrospective study in China.BMJ open · 2025Article
- Amplification of the power of network hubs and degree skewness over infectious disease spread during lulls.PloS one · 2025Article
- Bridging the Gap: Integrating Knowledge from the Study of Social Network Analysis and Infectious Disease Dynamics in Human and Nonhuman Primates.Annual review of anthropology · 2024Article
- International Collaboration in Taiwan Emergency Department Publications: A Social Network Analysis.Journal of acute medicine · 2024Article
- Regional inequalities in excess mortality and its community determinants during the early COVID-19 pandemic in South Korea.Journal of epidemiology and community health · 2024Article
- Uncovering COVID-19 transmission tree: identifying traced and untraced infections in an infection network.Frontiers in public health · 2024Article
- SARS-CoV-2 Infection, Hospitalization, and Mortality in Adults With and Without Cancer.JAMA network open · 2023Article
- COVID-19 infection inference with graph neural networks.Scientific reports · 2023Article
- Heterogeneity is a key factor describing the initial outbreak of COVID-19.Applied mathematical modelling · 2023Article
- Epidemic Diffusion Network of Spain: A Mobility Model to Characterize the Transmission Routes of Disease.International journal of environmental research and public health · 2023Article
- Mathematical Modeling of COVID-19 Transmission and Intervention in South Korea: A Review of Literature.Yonsei medical journal · 2023Review
- A Social Network Analysis Approach to Evaluate the Relationship Between the Mobility Network Metrics and the COVID-19 Outbreak.Health services insights · 2023Article
- Predicting variable-length paths in networked systems using multi-order generative models.Applied network science · 2023Article
- Contact network analysis of COVID-19 Delta variant outbreak in urban China -based on 2,050 confirmed cases in Xi'an, China.BMC public health · 2022Article
- A network analysis and support vector regression approaches for visualising and predicting the COVID-19 outbreak in Malaysia.Healthcare analytics (New York, N.Y.) · 2022Article
- Evaluating the structure-coefficient theorem of evolutionary game theory.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Social Network Analysis on the Mobility of Three Vulnerable Population Subgroups: Domestic Workers, Flight Crews, and Sailors during the COVID-19 Pandemic in Hong Kong.International journal of environmental research and public health · 2022Article
- COVID-19 outbreak response at a nursing hospital in South Korea in the post-vaccination era, including an estimation of the effectiveness of the first shot of the Oxford-AstraZeneca COVID-19 vaccine (ChAdOx1-S).Osong public health and research perspectives · 2022Article
- Evolution of Responses to COVID-19 and Epidemiological Characteristics in South Korea.International journal of environmental research and public health · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study estimates the COVID-19 infection network from actual data and draws on implications for policy and research. Using contact tracing information of 3283 confirmed patients in Seoul metropolitan areas from January 20, 2020 to July 19, 2020, this study created an infection network and analyzed its structural characteristics. The main results are as follows: (i) out-degrees follow an extremely positively skewed distribution; (ii) removing the top nodes on the out-degree significantly decreases the size of the infection network, and (iii) the indicators that express the infectious power of the network change according to governmental measures. Efforts to collect network data and analyze network structures are urgently required for the efficiency of governmental responses to COVID-19. Implications for better use of a metric such as R0 to estimate infection spread are also discussed.
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