Subgroup analysis of an epidemic response network of organizations: 2015 MERS outbreak in Korea
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Yushim | - |
dc.contributor.author | Kim, Jihong | - |
dc.contributor.author | Oh, Seong Soo | - |
dc.contributor.author | Kim, Sang Wook | - |
dc.contributor.author | Ku, Minyoung | - |
dc.date.accessioned | 2022-07-09T14:02:26Z | - |
dc.date.available | 2022-07-09T14:02:26Z | - |
dc.date.created | 2021-05-13 | - |
dc.date.issued | 2019-06 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/147635 | - |
dc.description.abstract | This paper analyzes subgroups in an epidemic response network to gain decision-making insights. We collected relational data among organizations in news articles during the 2015 Middle East Respiratory Syndrome (MERS) Coronavirus outbreak in South Korea. The MERS response network consisted of a total of 998 organizations and 1,968 edges. We identified and examined 28 subgroups. We found that the subgroup structure can be explained by three factors: activeness in the response, geographical location, and organizational function. Two core subgroups that actively responded to the outbreak consisted of heterogeneous organizations at multiple governmental levels and in multiple sectors. This implies that subgroups of heterogeneous organizations are worthy of greater attention than are homogeneous subgroups in the epidemic response network. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Association for Computing Machinery | - |
dc.title | Subgroup analysis of an epidemic response network of organizations: 2015 MERS outbreak in Korea | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Oh, Seong Soo | - |
dc.contributor.affiliatedAuthor | Kim, Sang Wook | - |
dc.identifier.doi | 10.1145/3325112.3325260 | - |
dc.identifier.scopusid | 2-s2.0-85068595248 | - |
dc.identifier.wosid | 000555903400022 | - |
dc.identifier.bibliographicCitation | ACM International Conference Proceeding Series, pp.177 - 185 | - |
dc.relation.isPartOf | ACM International Conference Proceeding Series | - |
dc.citation.title | ACM International Conference Proceeding Series | - |
dc.citation.startPage | 177 | - |
dc.citation.endPage | 185 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Public Administration | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Public Administration | - |
dc.subject.keywordPlus | Artificial intelligence | - |
dc.subject.keywordPlus | Decision making | - |
dc.subject.keywordPlus | Coronaviruses | - |
dc.subject.keywordPlus | Geographical locations | - |
dc.subject.keywordPlus | MERS | - |
dc.subject.keywordPlus | News articles | - |
dc.subject.keywordPlus | Organizational functions | - |
dc.subject.keywordPlus | Relational data | - |
dc.subject.keywordPlus | Subgroup Analysis | - |
dc.subject.keywordPlus | Subgroup structure | - |
dc.subject.keywordPlus | Epidemiology | - |
dc.subject.keywordAuthor | MERS | - |
dc.subject.keywordAuthor | Response Network | - |
dc.subject.keywordAuthor | Subgroup Analysis | - |
dc.identifier.url | https://dl.acm.org/doi/10.1145/3325112.3325260 | - |
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