• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
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
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
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 @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

基于新冠疫情时空信息知识图谱的疫情态势交互分析

Interactive Analysis of Epidemic Situations Based on a Spatiotemporal Information Knowledge Graph of COVID-19.

作者信息

Jiang Bingchuan, You Xiong, Li Ke, Li Tingting, Zhou Xiaojun, Tan Liheng

机构信息

Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University Zhengzhou 450052 China.

出版信息

IEEE Access. 2020 Oct 26;10:46782-46795. doi: 10.1109/ACCESS.2020.3033997. eCollection 2022.

DOI:10.1109/ACCESS.2020.3033997
PMID:35937640
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9280860/
Abstract

In view of the lack of data association in spatiotemporal information analysis and the lack of spatiotemporal situation analysis in knowledge graphs, this article combines the semantic web of the geographic knowledge graph with the visual analysis model of spatial information and puts forward the comprehensive utilization of the related technologies of the geographic knowledge graph and big data visual analysis. Then, it realizes the situational analysis of COVID-19 (Coronavirus Disease 2019) and the exploration of patient relationships through interactive collaborative analysis. The main contributions of the paper are as follows. (1) Based on the characteristics of the geographic knowledge graph, a patient entity model and an entity relationship type and knowledge representation method are proposed, and a knowledge graph of the spatiotemporal information of COVID-19 is constructed. (2) To analyse the COVID-19 patients' situations and explore their relationships, an analytical framework is designed. The framework, combining the semantic web of the geographic knowledge graph and the visual analysis model of geographic information, allows one to analyse the semantic web by using the node attribute similarity calculation, key stage mining, community prediction and other methods. (3)An efficient epidemic prevention and anti-epidemic method is proposed which is of referential significance. It is based on experiments and the collaborative analysis of the semantic web and spatial information, allowing for real-time situational understanding, the discovery of patients' relationships, the analysis of the spatiotemporal distribution of patients, super spreader mining, key node analysis, and the prevention and control of high-risk groups.

摘要

鉴于在时空信息分析中缺乏数据关联，以及知识图谱中缺乏时空态势分析，本文将地理知识图谱的语义网与空间信息视觉分析模型相结合，提出综合利用地理知识图谱和大数据视觉分析的相关技术。然后，通过交互式协作分析实现了对2019冠状病毒病（COVID-19）的态势分析和患者关系探索。本文的主要贡献如下：（1）基于地理知识图谱的特点，提出了患者实体模型、实体关系类型及知识表示方法，构建了COVID-19时空信息知识图谱。（2）为分析COVID-19患者的态势并探索其关系，设计了一个分析框架。该框架结合了地理知识图谱的语义网和地理信息视觉分析模型，允许通过节点属性相似度计算、关键阶段挖掘、社区预测等方法对语义网进行分析。（3）提出了一种高效的防疫抗疫方法，具有参考意义。它基于实验以及语义网与空间信息的协同分析，能够实现实时态势理解、患者关系发现、患者时空分布分析、超级传播者挖掘、关键节点分析以及高危人群防控。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f25c/9280860/3afa0583f0d5/jiang13-3033997.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f25c/9280860/5ebe5c5c0a8a/jiang1-3033997.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f25c/9280860/2b0fee442f66/jiang2-3033997.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f25c/9280860/23ed1e6abf24/jiang3-3033997.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f25c/9280860/3c8472ce8ddd/jiang4-3033997.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f25c/9280860/b8a47af85b61/jiang5-3033997.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f25c/9280860/a8c172c72c4b/jiang6-3033997.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f25c/9280860/3aa85c65e883/jiang7-3033997.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f25c/9280860/6a5f8147306e/jiang8-3033997.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f25c/9280860/cd67bf15f913/jiang9-3033997.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f25c/9280860/fc82814eb210/jiang10-3033997.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f25c/9280860/e5c4c93e8e2c/jiang11abc-3033997.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f25c/9280860/83c882d57937/jiang12-3033997.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f25c/9280860/3afa0583f0d5/jiang13-3033997.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f25c/9280860/5ebe5c5c0a8a/jiang1-3033997.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f25c/9280860/2b0fee442f66/jiang2-3033997.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f25c/9280860/23ed1e6abf24/jiang3-3033997.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f25c/9280860/3c8472ce8ddd/jiang4-3033997.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f25c/9280860/b8a47af85b61/jiang5-3033997.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f25c/9280860/a8c172c72c4b/jiang6-3033997.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f25c/9280860/3aa85c65e883/jiang7-3033997.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f25c/9280860/6a5f8147306e/jiang8-3033997.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f25c/9280860/cd67bf15f913/jiang9-3033997.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f25c/9280860/fc82814eb210/jiang10-3033997.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f25c/9280860/e5c4c93e8e2c/jiang11abc-3033997.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f25c/9280860/83c882d57937/jiang12-3033997.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f25c/9280860/3afa0583f0d5/jiang13-3033997.jpg

相似文献

1
Interactive Analysis of Epidemic Situations Based on a Spatiotemporal Information Knowledge Graph of COVID-19.基于新冠疫情时空信息知识图谱的疫情态势交互分析
IEEE Access. 2020 Oct 26;10:46782-46795. doi: 10.1109/ACCESS.2020.3033997. eCollection 2022.
2
Construct a Knowledge Graph for China Coronavirus (COVID-19) Patient Information Tracking.构建中国新冠病毒（COVID-19）患者信息追踪知识图谱。
Risk Manag Healthc Policy. 2021 Oct 21;14:4321-4337. doi: 10.2147/RMHP.S309732. eCollection 2021.
3
Expediting knowledge acquisition by a web framework for Knowledge Graph Exploration and Visualization (KGEV): case studies on COVID-19 and Human Phenotype Ontology.利用知识图谱探索和可视化（KGEV）的网络框架加速知识获取：COVID-19 和人类表型本体论的案例研究。
BMC Med Inform Decis Mak. 2022 Jun 2;22(Suppl 2):147. doi: 10.1186/s12911-022-01848-z.
4
Path-based knowledge reasoning with textual semantic information for medical knowledge graph completion.基于路径的知识推理与文本语义信息融合的医疗知识图谱补全方法
BMC Med Inform Decis Mak. 2021 Nov 29;21(Suppl 9):335. doi: 10.1186/s12911-021-01622-7.
5
edge2vec: Representation learning using edge semantics for biomedical knowledge discovery.边向量模型：利用边语义的表示学习方法进行生物医学知识发现。
BMC Bioinformatics. 2019 Jun 10;20(1):306. doi: 10.1186/s12859-019-2914-2.
6
Design of Knowledge Graph Retrieval System for Legal and Regulatory Framework of Multilevel Latent Semantic Indexing.多层面潜在语义索引法律和监管框架的知识图检索系统设计。
Comput Intell Neurosci. 2022 Jul 19;2022:6781043. doi: 10.1155/2022/6781043. eCollection 2022.
7
GFCNet: Utilizing graph feature collection networks for coronavirus knowledge graph embeddings.GFCNet：利用图特征收集网络进行冠状病毒知识图谱嵌入
Inf Sci (N Y). 2022 Aug;608:1557-1571. doi: 10.1016/j.ins.2022.07.031. Epub 2022 Jul 14.
8
Complex Reporting of the COVID-19 Epidemic in the Czech Republic: Use of an Interactive Web-Based App in Practice.捷克共和国COVID-19疫情的复杂报告：交互式网络应用程序在实际中的应用。
J Med Internet Res. 2020 May 27;22(5):e19367. doi: 10.2196/19367.
9
Semantic Flow Graph: A Framework for Discovering Object Relationships in Flow Fields.语义流图：一种在流场中发现对象关系的框架。
IEEE Trans Vis Comput Graph. 2018 Dec;24(12):3200-3213. doi: 10.1109/TVCG.2017.2773071. Epub 2017 Nov 22.
10
TS-Extractor: large graph exploration via subgraph extraction based on topological and semantic information.TS-Extractor：基于拓扑和语义信息通过子图提取进行大型图探索
J Vis (Tokyo). 2021;24(1):173-190. doi: 10.1007/s12650-020-00699-y. Epub 2020 Sep 22.

引用本文的文献

1
A data-driven framework to assess population dynamics during novel coronavirus outbreaks: A case study on Xiamen Island, China.基于数据的新型冠状病毒疫情期间人口动态评估框架：以中国厦门岛为例
PLoS One. 2023 Nov 10;18(11):e0293803. doi: 10.1371/journal.pone.0293803. eCollection 2023.
2
CIRO: COVID-19 infection risk ontology.CIRO：COVID-19 感染风险本体。
PLoS One. 2023 Mar 30;18(3):e0282291. doi: 10.1371/journal.pone.0282291. eCollection 2023.
3
Knowledge Graph: Applications in Tracing the Source of Large-Scale Outbreak - Beijing Municipality, China, 2020-2021.

本文引用的文献

1
Clinical Characteristics of Coronavirus Disease 2019 in China.《中国 2019 年冠状病毒病临床特征》
N Engl J Med. 2020 Apr 30;382(18):1708-1720. doi: 10.1056/NEJMoa2002032. Epub 2020 Feb 28.
2
Construction and evaluation of two computational models for predicting the incidence of influenza in Nagasaki Prefecture, Japan.构建和评估两个用于预测日本长崎县流感发病率的计算模型。
Sci Rep. 2017 Aug 3;7(1):7192. doi: 10.1038/s41598-017-07475-3.
3
Spatio-temporal clustering of American Cutaneous Leishmaniasis in a rural municipality of Venezuela.
知识图谱：在追踪大规模疫情源头中的应用——中国北京市，2020 - 2021年
China CDC Wkly. 2023 Jan 27;5(4):90-95. doi: 10.46234/ccdcw2023.017.
4
Tracking and analysis of discourse dynamics and polarity during the early Corona pandemic in Iran.追踪和分析伊朗新冠疫情早期的话语动态和极性。
J Biomed Inform. 2021 Sep;121:103862. doi: 10.1016/j.jbi.2021.103862. Epub 2021 Jul 3.
委内瑞拉一农村自治市的美国皮肤利什曼病时空聚集性
Epidemics. 2013 Mar;5(1):11-9. doi: 10.1016/j.epidem.2012.10.002. Epub 2012 Nov 2.
4
Quantifying the impact of human mobility on malaria.量化人类流动对疟疾的影响。
Science. 2012 Oct 12;338(6104):267-70. doi: 10.1126/science.1223467.
5
Multiscale mobility networks and the spatial spreading of infectious diseases.多尺度移动性网络与传染病的空间传播。
Proc Natl Acad Sci U S A. 2009 Dec 22;106(51):21484-9. doi: 10.1073/pnas.0906910106. Epub 2009 Dec 14.
6
Social network analysis of medication advice-seeking interactions among staff in an Australian hospital.澳大利亚医院员工间药物咨询互动的社交网络分析。
Int J Med Inform. 2010 Jun;79(6):e116-25. doi: 10.1016/j.ijmedinf.2008.08.005. Epub 2008 Nov 12.
7
A new tool for epidemiology: the usefulness of dynamic-agent models in understanding place effects on health.流行病学的一种新工具：动态主体模型在理解地域对健康的影响方面的作用。
Am J Epidemiol. 2008 Jul 1;168(1):1-8. doi: 10.1093/aje/kwn118. Epub 2008 May 13.
8
Finding and evaluating community structure in networks.在网络中寻找并评估社区结构。
Phys Rev E Stat Nonlin Soft Matter Phys. 2004 Feb;69(2 Pt 2):026113. doi: 10.1103/PhysRevE.69.026113. Epub 2004 Feb 26.
9
Epidemiological determinants of spread of causal agent of severe acute respiratory syndrome in Hong Kong.香港严重急性呼吸系统综合症病原体传播的流行病学决定因素。
Lancet. 2003 May 24;361(9371):1761-6. doi: 10.1016/S0140-6736(03)13410-1.