文献检索文档翻译深度研究
Suppr Zotero 插件Zotero 插件
邀请有礼套餐&价格历史记录

新学期，新优惠

限时优惠:9月1日-9月22日

30天高级会员仅需29元

1天体验卡首发特惠仅需5.99元

了解详情
不再提醒
插件&应用
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
高级版
套餐订阅购买积分包
AI 工具
文献检索文档翻译深度研究
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2025

利用社交网络分析方法识别全球 COVID-19 时空传播模式：在线仪表板开发。

Using Social Network Analysis to Identify Spatiotemporal Spread Patterns of COVID-19 around the World: Online Dashboard Development.

机构信息

Department of Gastrointestinal Hepatobiliary, Chi Mei Jiali Hospital, Tainan 700, Taiwan.

Department of Medical Research, Chi-Mei Hospital, Tainan 700, Taiwan.

出版信息

Int J Environ Res Public Health. 2021 Mar 3;18(5):2461. doi: 10.3390/ijerph18052461.

DOI:10.3390/ijerph18052461
PMID:33802247
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7967593/
Abstract

The COVID-19 pandemic has spread widely around the world. Many mathematical models have been proposed to investigate the inflection point (IP) and the spread pattern of COVID-19. However, no researchers have applied social network analysis (SNA) to cluster their characteristics. We aimed to illustrate the use of SNA to identify the spread clusters of COVID-19. Cumulative numbers of infected cases (CNICs) in countries/regions were downloaded from GitHub. The CNIC patterns were extracted from SNA based on CNICs between countries/regions. The item response model (IRT) was applied to create a general predictive model for each country/region. The IP days were obtained from the IRT model. The location parameters in continents, China, and the United States were compared. The results showed that (1) three clusters (255, n = 51, 130, and 74 in patterns from Eastern Asia and Europe to America) were separated using SNA, (2) China had a shorter mean IP and smaller mean location parameter than other counterparts, and (3) an online dashboard was used to display the clusters along with IP days for each country/region. Spatiotemporal spread patterns can be clustered using SNA and correlation coefficients (CCs). A dashboard with spread clusters and IP days is recommended to epidemiologists and researchers and is not limited to the COVID-19 pandemic.

摘要

新冠疫情在全球范围内广泛传播。许多数学模型已经被提出，以研究新冠疫情的拐点（IP）和传播模式。然而，没有研究人员应用社会网络分析（SNA）对其特征进行聚类。我们旨在说明如何应用 SNA 来识别新冠疫情的传播集群。从 GitHub 下载了国家/地区的累计感染病例数（CNIC）。基于国家/地区之间的 CNIC，从 SNA 中提取了 CNIC 模式。应用项目反应模型（IRT）为每个国家/地区创建了一个通用预测模型。从 IRT 模型中获得了 IP 日。比较了各大洲、中国和美国的位置参数。结果表明：(1) 使用 SNA 可以分离出三个集群（255、n = 51、130 和 74，模式从东亚和欧洲到美洲）；(2) 中国的平均 IP 日数较短，位置参数较小；(3) 利用在线仪表板可以显示每个国家/地区的集群及其 IP 日数。使用 SNA 和相关系数（CC）可以对时空传播模式进行聚类。建议为流行病学家和研究人员提供带有传播集群和 IP 日数的仪表板，且不限于新冠疫情。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b95/7967593/43720f3296f4/ijerph-18-02461-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b95/7967593/1959be0f284e/ijerph-18-02461-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b95/7967593/2994c3d18e07/ijerph-18-02461-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b95/7967593/90a87bc60e39/ijerph-18-02461-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b95/7967593/912ee00eba15/ijerph-18-02461-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b95/7967593/5baf8a70c51d/ijerph-18-02461-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b95/7967593/147417037729/ijerph-18-02461-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b95/7967593/32feacdd9745/ijerph-18-02461-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b95/7967593/43720f3296f4/ijerph-18-02461-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b95/7967593/1959be0f284e/ijerph-18-02461-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b95/7967593/2994c3d18e07/ijerph-18-02461-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b95/7967593/90a87bc60e39/ijerph-18-02461-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b95/7967593/912ee00eba15/ijerph-18-02461-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b95/7967593/5baf8a70c51d/ijerph-18-02461-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b95/7967593/147417037729/ijerph-18-02461-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b95/7967593/32feacdd9745/ijerph-18-02461-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b95/7967593/43720f3296f4/ijerph-18-02461-g008.jpg

相似文献

[1]
Using Social Network Analysis to Identify Spatiotemporal Spread Patterns of COVID-19 around the World: Online Dashboard Development.

Int J Environ Res Public Health. 2021-3-3

[2]
An online time-to-event dashboard comparing the effective control of COVID-19 among continents using the inflection point on an ogive curve: Observational study.

Medicine (Baltimore). 2021-3-12

[3]
Using the IPcase Index with Inflection Points and the Corresponding Case Numbers to Identify the Impact Hit by COVID-19 in China: An Observation Study.

Int J Environ Res Public Health. 2021-2-18

[4]
Comparison of prediction accuracies between mathematical models to make projections of confirmed cases during the COVID-19 pandamic by country/region.

Medicine (Baltimore). 2021-12-17

[5]
Visualizing the features of inflection point shown on a temporal bar graph using the data of COVID-19 pandemic.

Medicine (Baltimore). 2022-2-4

[6]
Comparison of prediction accuracies between two mathematical models for the assessment of COVID-19 damage at the early stage and throughout 2020.

Medicine (Baltimore). 2022-8-12

[7]
The heterogeneity of the COVID-19 pandemic and national responses: an explanatory mixed-methods study.

BMC Public Health. 2021-5-1

[8]
SARS-CoV-2 Wave Two Surveillance in East Asia and the Pacific: Longitudinal Trend Analysis.

J Med Internet Res. 2021-2-1

[9]
The Epidemiological and Spatiotemporal Characteristics of the 2019 Novel Coronavirus Disease (COVID-19) in Libya.

Front Public Health. 2021

[10]
Using the absolute advantage coefficient (AAC) to measure the strength of damage hit by COVID-19 in India on a growth-share matrix.

Eur J Med Res. 2021-6-24

引用本文的文献

[1]
Characteristics of humoral responses to the first coronavirus disease booster vaccine and breakthrough infection in central China: a multicentre, prospective, longitudinal cohort study.

Front Immunol. 2025-1-7

[2]
Analyzing shifts in age-related macular degeneration research trends since 2014: A bibliometric study with triple-map Sankey diagrams (TMSD).

Medicine (Baltimore). 2024-1-19

[3]
Exploring the top-cited literature in telerehabilitation for joint replacement using the descriptive, diagnostic, predictive, and prescriptive analytics model: A thematic and bibliometric analysis.

Medicine (Baltimore). 2023-12-1

[4]
A modern approach with follower-leading clustering algorithm for visualizing author collaborations and article themes in skin cancer research: A bibliometric analysis.

Medicine (Baltimore). 2023-11-3

[5]
Visualizing burst spots on research for four authors in MDPI journals named to be Citation Laureates 2021 using temporal bar graph.

Medicine (Baltimore). 2023-8-11

[6]
Analysis of citation trends to identify articles on delirium worth reading using DDPP model with temporal heatmaps (THM): A bibliometric analysis.

Medicine (Baltimore). 2023-2-22

[7]
Using chord diagrams to explore article themes in 100 top-cited articles citing Hirsch's h-index since 2005: A bibliometric analysis.

Medicine (Baltimore). 2023-2-22

[8]
A leading bibliometric author does not have a dominant contribution to research based on the CJAL score: Bibliometric analysis.

Medicine (Baltimore). 2023-1-13

[9]
Authors who contributed most to the fields of hemodialysis and peritoneal dialysis since 2011 using the hT-index: Bibliometric analysis.

Medicine (Baltimore). 2022-9-23

[10]
Using the Sankey diagram to visualize article features on the topics of whole-exome sequencing (WES) and whole-genome sequencing (WGS) since 2012: Bibliometric analysis.

Medicine (Baltimore). 2022-9-23

本文引用的文献

[1]
Using the IPcase Index with Inflection Points and the Corresponding Case Numbers to Identify the Impact Hit by COVID-19 in China: An Observation Study.

Int J Environ Res Public Health. 2021-2-18

[2]
Using Kano diagrams to display the most cited article types, affiliated countries, authors and MeSH terms on spinal surgery in recent 12 years.

Eur J Med Res. 2021-2-23

[3]
Horizontal gene transfer and recombination analysis of SARS-CoV-2 genes helps discover its close relatives and shed light on its origin.

BMC Ecol Evol. 2021-1-21

[4]
Spatiotemporal spread pattern of the COVID-19 cases in China.

PLoS One. 2020

[5]
Machine Learning-Based Activity Pattern Classification Using Personal PM Exposure Information.

Int J Environ Res Public Health. 2020-9-9

[6]
Online dashboard and data analysis approach for assessing COVID-19 case and death data.

F1000Res. 2020-6-8

[7]
Effects of control measures on the dynamics of COVID-19 and double-peak behavior in Spain.

Nonlinear Dyn. 2020

[8]
Geographic and Genomic Distribution of SARS-CoV-2 Mutations.

Front Microbiol. 2020-7-22

[9]
A real-time policy dashboard can aid global transparency in the response to coronavirus disease 2019.

Int Health. 2020-9-1

[10]
The computation of case fatality rate for novel coronavirus (COVID-19) based on Bayes theorem: An observational study.

Medicine (Baltimore). 2020-5-22

文献AI研究员

20分钟写一篇综述，助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

推荐工具

医学文档翻译智能文献检索