A social network analysis of the spread of COVID-19 in South Korea and policy implications

Jo, Wonkwang; Chang, Daesung; You, Myoungsoon; Ghim, Ghi Hoon

doi:10.1038/s41598-021-87837-0

Cited by 44 publications

(42 citation statements)

References 16 publications

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“…We calculate the relevant network indicators, which are shown in Table 1 . Furthermore, referring to Eubank et al [ 6 ] and Jo et al [ 49 ], we also simulate the effect of quarantine policy by deleting nodes with certain value of degree centrality.…”

Section: Methodsmentioning

confidence: 99%

“…Jo et al constructed a directed infection network based on data of 3283 cases in the Seoul metropolitan area of Korea from 20 January to 19 July 2020. They calculated indicators such as network out-degree distribution, average path length, and network diameter, pointing out that network structure has an important impact on the transmission processes of COVID-19 and health departments should perform improved investigation and tracking of cases exposure history [ 49 ]. These studies, based on real disease infection data, deepen our understanding of epidemic transmission and help us to implement more effective prevention and control policies.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

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Complex Contact Network of Patients at the Beginning of an Epidemic Outbreak: An Analysis Based on 1218 COVID-19 Cases in China

Yang

Zhang

Gao

et al. 2022

IJERPH

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The spread of viruses essentially occurs through the interaction and contact between people, which is closely related to the network of interpersonal relationships. Based on the epidemiological investigations of 1218 COVID-19 cases in eight areas of China, we use text analysis, social network analysis and visualization methods to construct a dynamic contact network of the epidemic. We analyze the corresponding demographic characteristics, network indicators, and structural characteristics of this network. We found that more than 65% of cases are likely to be infected by a strong relationship, and nearly 40% of cases have family members infected at the same time. The overall connectivity of the contact network is low, but there are still some clustered infections. In terms of the degree distribution, most cases’ degrees are concentrated between 0 and 2, which is relatively low, and only a few ones have a higher degree value. The degree distribution also conforms to the power law distribution, indicating the network is a scale-free network. There are 17 cases with a degree greater than 10, and these cluster infections are usually caused by local transmission. The first implication of this research is we find that the COVID-19 spread is closely related to social structures by applying computational sociological methods for infectious disease studies; the second implication is to confirm that text analysis can quickly visualize the spread trajectory at the beginning of an epidemic.

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Section: Methodsmentioning

confidence: 99%

Section: Theoretical Backgroundmentioning

confidence: 99%

Complex Contact Network of Patients at the Beginning of an Epidemic Outbreak: An Analysis Based on 1218 COVID-19 Cases in China

Yang

Zhang

Gao

et al. 2022

IJERPH

View full text Add to dashboard Cite

show abstract

“…In light of the COVID-19 pandemic, the analysis has been employed either to demonstrate the infection network of the confirmed patients and its structural characteristics [49][50][51] or to explore how key nodes can play an important role in social networks of COVID-19. These studies mainly relied on data from Twitter users and their followers [52] to understand the spatial diffusion of the COVID-19 infection [53] or to visualize the COVID-19 pandemic risk [54].…”

Section: Explanatory Variablesmentioning

confidence: 99%

Density or Connectivity: What Are the Main Causes of the Spatial Proliferation of COVID-19 in Korea?

Hong

Sung

2021

IJERPH

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COVID-19 has sparked a debate on the vulnerability of densely populated cities. Some studies argue that high-density urban centers are more vulnerable to infectious diseases due to a higher chance of infection in crowded urban environments. Other studies, however, argue that connectivity rather than population density plays a more significant role in the spread of COVID-19. While several studies have examined the role of urban density and connectivity in Europe and the U.S., few studies have been conducted in Asian countries. This study aims to investigate the role of urban spatial structure on COVID-19 by comparing different measures of urban density and connectivity during the first eight months of the outbreak in Korea. Two measures of density were derived from the Korean census, and four measures of connectivity were computed using social network analysis of the Origin-Destination data from the 2020 Korea Transport Database. We fitted both OLS and negative binomial models to the number of confirmed COVID-19 patients and its infection rates at the county level, collected individually from regional government websites in Korea. Results show that both density and connectivity play an important role in the proliferation of the COVID-19 outbreak in Korea. However, we found that the connectivity measure, particularly a measure of network centrality, was a better indicator of COVID-19 proliferation than the density measures. Our findings imply that policies that take into account different types of connectivity between cities might be necessary to contain the outbreak in the early phase.

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“…Such apps are an interesting example of the MCS paradigm, as users' devices periodically upload their contacts with proximity-sensing interfaces. The analysis of collected data is extremely useful to re-build the contact network, with higher accuracy than self-reporting tools (e.g., questionnaires or surveys) [52,54]. In this respect, we consider the possibility of employing predicting tools to infer information useful for tracing contacts and to detect communities as a valuable option [55].…”

Section: Contact Tracing and Community Detectionmentioning

confidence: 99%

How Mobility and Sociality Reshape the Context: A Decade of Experience in Mobile CrowdSensing

Girolami

Belli

Chessa

2021

Sensors

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The possibility of understanding the dynamics of human mobility and sociality creates the opportunity to re-design the way data are collected by exploiting the crowd. We survey the last decade of experimentation and research in the field of mobile CrowdSensing, a paradigm centred on users’ devices as the primary source for collecting data from urban areas. To this purpose, we report the methodologies aimed at building information about users’ mobility and sociality in the form of ties among users and communities of users. We present two methodologies to identify communities: spatial and co-location-based. We also discuss some perspectives about the future of mobile CrowdSensing and its impact on four investigation areas: contact tracing, edge-based MCS architectures, digitalization in Industry 5.0 and community detection algorithms.

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A social network analysis of the spread of COVID-19 in South Korea and policy implications

Cited by 44 publications

References 16 publications

Complex Contact Network of Patients at the Beginning of an Epidemic Outbreak: An Analysis Based on 1218 COVID-19 Cases in China

Complex Contact Network of Patients at the Beginning of an Epidemic Outbreak: An Analysis Based on 1218 COVID-19 Cases in China

Density or Connectivity: What Are the Main Causes of the Spatial Proliferation of COVID-19 in Korea?

How Mobility and Sociality Reshape the Context: A Decade of Experience in Mobile CrowdSensing

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