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韩国首尔大都市地区的 COVID-19 集群类型及其与社交距离的关系。

Types of COVID-19 clusters and their relationship with social distancing in the Seoul metropolitan area, South Korea.

机构信息

Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, 03080, Seoul, Republic of Korea.

Center for Healthy Society and Education, Seoul National University College of Medicine, Seoul, 03087, Seoul, Republic of Korea.

出版信息

Int J Infect Dis. 2021 May;106:363-369. doi: 10.1016/j.ijid.2021.02.058. Epub 2021 Feb 17.

Abstract

BACKGROUND

The complete contact tracing of coronavirus disease-19 (COVID-19) cases in South Korea allows a unique opportunity to investigate cluster characteristics. This study aimed to investigate all reported COVID-19 clusters in the Seoul metropolitan area from January 23 to September 24, 2020.

METHODS

Publicly available COVID-19 data was collected from the Seoul Metropolitan City and Gyeonggi Province. Community clusters with ≥5 cases were characterized by size and duration, categorized using K-means clustering, and the correlation between the types of clusters and the level of social distancing investigated.

RESULTS

A total of 134 clusters comprised of 4033 cases were identified. The clusters were categorized into small (type I and II), medium (type III), and large (type IV) clusters. A comparable number of daily reported cases in different time periods were composed of different types of clusters. Increased social distancing was related to a shift from large to small-sized clusters.

CONCLUSIONS

Classification of clusters may provide opportunities to understand the pattern of COVID-19 outbreaks better and implement more effective suppression strategies. Social distancing administered by the government may effectively suppress large clusters but may not effectively control small and sporadic clusters.

摘要

背景

韩国对新冠病毒疾病(COVID-19)病例进行了全面的接触者追踪,这为调查聚集性特征提供了一个独特的机会。本研究旨在调查 2020 年 1 月 23 日至 9 月 24 日期间首尔大都市区报告的所有 COVID-19 聚集性病例。

方法

从首尔市和京畿道收集了公开的 COVID-19 数据。≥5 例的社区聚集性病例按规模和持续时间进行特征描述,使用 K-均值聚类进行分类,并调查了聚集类型与社会隔离水平之间的相关性。

结果

共确定了 134 个包含 4033 例病例的聚集性病例。这些聚集性病例被分为小(I 型和 II 型)、中(III 型)和大(IV 型)聚集性病例。不同时期每日报告的病例数量相当,由不同类型的聚集性病例组成。社会隔离程度的增加与从大到小的聚集性病例的转变有关。

结论

对聚集性病例进行分类可能有助于更好地了解 COVID-19 爆发的模式,并实施更有效的抑制策略。政府实施的社会隔离可能有效抑制大聚集性病例,但可能无法有效控制小而零星的聚集性病例。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6f4/7889017/9e5477aced6e/gr1_lrg.jpg

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