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Reaction to the COVID-19 pandemic in Seoul with biostatistics

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dc.contributor.authorJung, S-
dc.contributor.authorHwang, SS-
dc.contributor.authorKim, KN-
dc.contributor.authorLee, W-
dc.date.accessioned2023-03-13T03:07:08Z-
dc.date.available2023-03-13T03:07:08Z-
dc.date.issued2022-
dc.identifier.issn2468-2152-
dc.identifier.urihttp://repository.ajou.ac.kr/handle/201003/25029-
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.language.isoen-
dc.titleReaction to the COVID-19 pandemic in Seoul with biostatistics-
dc.typeArticle-
dc.identifier.pmid35822172-
dc.identifier.urlhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9264726-
dc.subject.keywordCount time series model-
dc.subject.keywordCOVID-19-
dc.subject.keywordEndemic-epidemic model-
dc.contributor.affiliatedAuthorKim, KN-
dc.type.localJournal Papers-
dc.identifier.doi10.1016/j.idm.2022.06.009-
dc.citation.titleInfectious Disease Modelling-
dc.citation.volume7-
dc.citation.number3-
dc.citation.date2022-
dc.citation.startPage419-
dc.citation.endPage429-
dc.identifier.bibliographicCitationInfectious Disease Modelling, 7(3). : 419-429, 2022-
dc.identifier.eissn2468-0427-
dc.relation.journalidJ024682152-
Appears in Collections:
Journal Papers > School of Medicine / Graduate School of Medicine > Preventive Medicine & Public Health
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