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Reaction to the COVID-19 pandemic in Seoul with biostatistics

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dc.contributor.authorJung, Seungpil-
dc.contributor.authorHwang, Seung-Sik-
dc.contributor.authorKim, Kyoung-Nam-
dc.contributor.authorLee, Woojoo-
dc.date.accessioned2023-08-16T07:45:42Z-
dc.date.available2023-08-16T07:45:42Z-
dc.date.created2023-07-21-
dc.date.issued2022-09-
dc.identifier.issn2468-0427-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/189108-
dc.description.abstractThis paper discusses our collaboration work with government officers in the health department of Seoul during the COVID-19 pandemic. First, we focus on short-term forecasting for the number of new confirmed cases and severe cases. Second, we focus on understanding how much of the current infections has been affected by external influx from neighborhood areas or internal transmission within the area. This understanding may be important because it is linked to the government policy determining non-pharmaceutical interventions. To obtain the decomposition of the effect, districts of Seoul should be considered simultaneously, and multivariate time series models are used. Third, we focus on predicting the number of new weekly confirmed cases for each district in Seoul. This detailed prediction may be important to the government policy on resource allocation. We consider an ensemble method to overcome poor prediction performance of simple models. This paper presents the methodological details and analysis results of the study.-
dc.language영어-
dc.language.isoen-
dc.publisherKeAi Communications Co.-
dc.titleReaction to the COVID-19 pandemic in Seoul with biostatistics-
dc.typeArticle-
dc.contributor.affiliatedAuthorKim, Kyoung-Nam-
dc.identifier.doi10.1016/j.idm.2022.06.009-
dc.identifier.scopusid2-s2.0-85134193013-
dc.identifier.wosid000834167900001-
dc.identifier.bibliographicCitationInfectious Disease Modelling, v.7, no.3, pp.419 - 429-
dc.relation.isPartOfInfectious Disease Modelling-
dc.citation.titleInfectious Disease Modelling-
dc.citation.volume7-
dc.citation.number3-
dc.citation.startPage419-
dc.citation.endPage429-
dc.type.rimsART-
dc.type.docType정기학술지(Article(Perspective Article포함))-
dc.description.journalClass1-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaMathematical & Computational Biology-
dc.relation.journalResearchAreaInfectious Diseases-
dc.relation.journalWebOfScienceCategoryMathematical & Computational Biology-
dc.relation.journalWebOfScienceCategoryInfectious Diseases-
dc.subject.keywordPlusarticle-
dc.subject.keywordPlusArticle-
dc.subject.keywordPlusbiostatistics-
dc.subject.keywordPluscompartment model-
dc.subject.keywordPluscoronavirus disease 2019-
dc.subject.keywordPlusdecomposition-
dc.subject.keywordPlusdisease surveillance-
dc.subject.keywordPlusepidemic-
dc.subject.keywordPlusepidemiological model-
dc.subject.keywordPlusforecasting-
dc.subject.keywordPlusgovernment-
dc.subject.keywordPlushuman-
dc.subject.keywordPlusintensive care unit-
dc.subject.keywordPlusmathematical model-
dc.subject.keywordPlusneighborhood-
dc.subject.keywordPluspandemic-
dc.subject.keywordPlusprediction-
dc.subject.keywordPlusquality of life-
dc.subject.keywordPlusresource allocation-
dc.subject.keywordPlusrisk factor-
dc.subject.keywordPlustime series analysis-
dc.subject.keywordAuthorCount time series model-
dc.subject.keywordAuthorCOVID-19-
dc.subject.keywordAuthorEndemic-epidemic model-
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S2468042722000513?via%3Dihub-
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