• 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

孔雀:一个基于地图的多类型传染病爆发信息系统。

PEACOCK: A Map-Based Multitype Infectious Disease Outbreak Information System.

作者信息

Jang Beakcheol, Lee Miran, Kim Jong Wook

机构信息

Department of Computer ScienceSangmyung UniversitySeoul03016South Korea.

出版信息

IEEE Access. 2019 Jun 21;7:82956-82969. doi: 10.1109/ACCESS.2019.2924189. eCollection 2019.

DOI:10.1109/ACCESS.2019.2924189
PMID:32391237
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7176039/
Abstract

A map-based infectious disease outbreak information system, called PEACOCK, that provides three types of necessary infectious disease outbreak information is presented. The system first collects the infectious disease outbreak statistics from the government agencies and displays the number of infected people and infection indices on the map. Then, it crawls online news articles for each infectious disease and displays the number of mentions of each disease on the map. Users can also search for news articles regarding the disease. Finally, it retrieves the portal search query data and plots the graphs of the trends. It divides the risk into three levels (i.e., normal, caution, and danger) and visualizes them using different colors on the map. Users can access infectious disease outbreak information accurately and quickly using the system. As the system visualizes the information using both a map and various types of graphs, users can check the information at a glance. This system is in live at http://www.epidemic.co.kr/map.

摘要

本文介绍了一种基于地图的传染病爆发信息系统,名为PEACOCK,它提供三种必要的传染病爆发信息。该系统首先从政府机构收集传染病爆发统计数据,并在地图上显示感染人数和感染指数。然后,它抓取每种传染病的在线新闻文章,并在地图上显示每种疾病的提及次数。用户还可以搜索有关该疾病的新闻文章。最后,它检索门户搜索查询数据并绘制趋势图。它将风险分为三个级别(即正常、谨慎和危险),并在地图上使用不同颜色将其可视化。用户可以使用该系统准确、快速地获取传染病爆发信息。由于该系统使用地图和各种类型的图表对信息进行可视化,用户可以一目了然地查看信息。该系统可在http://www.epidemic.co.kr/map上实时访问。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c3b/7176039/af5cf85be52f/kim13-2924189.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c3b/7176039/e9374116a1e1/kim1-2924189.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c3b/7176039/1a28d58d2096/kim2-2924189.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c3b/7176039/de400854e45f/kim3-2924189.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c3b/7176039/e21300b7b9fd/kim4-2924189.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c3b/7176039/f691a4795c40/kim5-2924189.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c3b/7176039/212aedfdf3c2/kim6-2924189.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c3b/7176039/fcdacbd9a262/kim7-2924189.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c3b/7176039/586b43aef885/kim8-2924189.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c3b/7176039/e41daee2dd52/kim9abcd-2924189.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c3b/7176039/d2b7dc2bdb33/kim10-2924189.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c3b/7176039/1f0bef4bb835/kim11-2924189.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c3b/7176039/a44287a583dd/kim12-2924189.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c3b/7176039/af5cf85be52f/kim13-2924189.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c3b/7176039/e9374116a1e1/kim1-2924189.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c3b/7176039/1a28d58d2096/kim2-2924189.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c3b/7176039/de400854e45f/kim3-2924189.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c3b/7176039/e21300b7b9fd/kim4-2924189.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c3b/7176039/f691a4795c40/kim5-2924189.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c3b/7176039/212aedfdf3c2/kim6-2924189.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c3b/7176039/fcdacbd9a262/kim7-2924189.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c3b/7176039/586b43aef885/kim8-2924189.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c3b/7176039/e41daee2dd52/kim9abcd-2924189.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c3b/7176039/d2b7dc2bdb33/kim10-2924189.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c3b/7176039/1f0bef4bb835/kim11-2924189.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c3b/7176039/a44287a583dd/kim12-2924189.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9c3b/7176039/af5cf85be52f/kim13-2924189.jpg

相似文献

1
PEACOCK: A Map-Based Multitype Infectious Disease Outbreak Information System.孔雀:一个基于地图的多类型传染病爆发信息系统。
IEEE Access. 2019 Jun 21;7:82956-82969. doi: 10.1109/ACCESS.2019.2924189. eCollection 2019.
2
DOVE: An Infectious Disease Outbreak Statistics Visualization System.DOVE:一种传染病爆发统计可视化系统。
IEEE Access. 2018 Aug 24;6:47206-47216. doi: 10.1109/ACCESS.2018.2867030. eCollection 2018.
3
Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas.在流行地区,服用抗叶酸抗疟药物的人群中,叶酸补充剂与疟疾易感性和严重程度的关系。
Cochrane Database Syst Rev. 2022 Feb 1;2(2022):CD014217. doi: 10.1002/14651858.CD014217.
4
Online Information Exchange and Anxiety Spread in the Early Stage of the Novel Coronavirus (COVID-19) Outbreak in South Korea: Structural Topic Model and Network Analysis.韩国新型冠状病毒(COVID-19)疫情早期的在线信息交流与焦虑传播:结构主题模型与网络分析
J Med Internet Res. 2020 Jun 2;22(6):e19455. doi: 10.2196/19455.
5
Deep similarity analysis and forecasting of actual outbreak of major infectious diseases using Internet-Sourced data.利用互联网数据进行主要传染病实际疫情的深度相似性分析和预测。
J Biomed Inform. 2022 Sep;133:104148. doi: 10.1016/j.jbi.2022.104148. Epub 2022 Jul 22.
6
The Global Infectious Diseases Epidemic Information Monitoring System: Development and Usability Study of an Effective Tool for Travel Health Management in China.全球传染病疫情监测系统:中国旅行健康管理有效工具的开发和可用性研究。
JMIR Public Health Surveill. 2021 Feb 16;7(2):e24204. doi: 10.2196/24204.
7
Search engines, news wires and digital epidemiology: Presumptions and facts.搜索引擎、新闻专线和数字流行病学:假设与事实。
Int J Med Inform. 2018 Jul;115:53-63. doi: 10.1016/j.ijmedinf.2018.03.017. Epub 2018 Apr 12.
8
请你提供一下具体的原文内容呀,这样我才能准确地翻译为中文。
9
Enhancing COVID-19 Epidemic Forecasting Accuracy by Combining Real-time and Historical Data From Multiple Internet-Based Sources: Analysis of Social Media Data, Online News Articles, and Search Queries.利用多个基于互联网的来源的实时和历史数据提高 COVID-19 疫情预测准确性:社交媒体数据、在线新闻文章和搜索查询分析。
JMIR Public Health Surveill. 2022 Jun 16;8(6):e35266. doi: 10.2196/35266.
10
Google Trends-based non-English language query data and epidemic diseases: a cross-sectional study of the popular search behaviour in Taiwan.基于 Google Trends 的非英语语言查询数据与传染病:台湾热门搜索行为的横断面研究。
BMJ Open. 2020 Jul 5;10(7):e034156. doi: 10.1136/bmjopen-2019-034156.

引用本文的文献

1
Investigating the spatiotemporal characteristics and medical response during the initial COVID-19 epidemic in six Chinese cities.调查六个中国城市 COVID-19 疫情初期的时空特征和医疗应对措施。
Sci Rep. 2024 Mar 25;14(1):7065. doi: 10.1038/s41598-024-56077-3.
2
A Detection-Service-Mobile Three-Terminal Software Platform for Point-of-Care Infectious Disease Detection System.一种用于即时检测传染病的检测服务移动三端软件平台。
Biosensors (Basel). 2022 Aug 25;12(9):684. doi: 10.3390/bios12090684.
3
Data-driven dynamic clustering framework for mitigating the adverse economic impact of Covid-19 lockdown practices.

本文引用的文献

1
DOVE: An Infectious Disease Outbreak Statistics Visualization System.DOVE:一种传染病爆发统计可视化系统。
IEEE Access. 2018 Aug 24;6:47206-47216. doi: 10.1109/ACCESS.2018.2867030. eCollection 2018.
2
DiTeX: Disease-related topic extraction system through internet-based sources.基于互联网资源的疾病相关主题提取系统(DiTeX)。
PLoS One. 2018 Aug 3;13(8):e0201933. doi: 10.1371/journal.pone.0201933. eCollection 2018.
3
Big Data and the Global Public Health Intelligence Network (GPHIN).大数据与全球公共卫生情报网络(GPHIN)。
用于减轻新冠疫情封锁措施不利经济影响的数据驱动动态聚类框架
Sustain Cities Soc. 2020 Nov;62:102372. doi: 10.1016/j.scs.2020.102372. Epub 2020 Jul 3.
4
Cluster-Based Analysis of Infectious Disease Occurrences Using Tensor Decomposition: A Case Study of South Korea.基于张量分解的传染病发生聚类分析:以韩国为例。
Int J Environ Res Public Health. 2020 Jul 6;17(13):4872. doi: 10.3390/ijerph17134872.
Can Commun Dis Rep. 2015 Sep 3;41(9):209-214. doi: 10.14745/ccdr.v41i09a02.
4
Big Data for Infectious Disease Surveillance and Modeling.用于传染病监测与建模的大数据
J Infect Dis. 2016 Dec 1;214(suppl_4):S375-S379. doi: 10.1093/infdis/jiw400.
5
Use of a Digital Health Application for Influenza Surveillance in China.数字健康应用在中国流感监测中的应用。
Am J Public Health. 2017 Jul;107(7):1130-1136. doi: 10.2105/AJPH.2017.303767. Epub 2017 May 18.
6
Web-based infectious disease surveillance systems and public health perspectives: a systematic review.基于网络的传染病监测系统与公共卫生视角:一项系统综述
BMC Public Health. 2016 Dec 8;16(1):1238. doi: 10.1186/s12889-016-3893-0.
7
Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico.评估传染病预测的性能:墨西哥气候驱动型和季节性登革热预测的比较。
Sci Rep. 2016 Sep 26;6:33707. doi: 10.1038/srep33707.
8
Internet-based media coverage on dengue in Sri Lanka between 2007 and 2015.2007年至2015年间斯里兰卡登革热的网络媒体报道。
Glob Health Action. 2016 May 12;9:31620. doi: 10.3402/gha.v9.31620. eCollection 2016.
9
Using clinicians' search query data to monitor influenza epidemics.利用临床医生的搜索查询数据监测流感疫情。
Clin Infect Dis. 2014 Nov 15;59(10):1446-50. doi: 10.1093/cid/ciu647. Epub 2014 Aug 12.
10
Internet-based surveillance systems for monitoring emerging infectious diseases.基于互联网的传染病监测系统。
Lancet Infect Dis. 2014 Feb;14(2):160-8. doi: 10.1016/S1473-3099(13)70244-5. Epub 2013 Nov 28.