Research of Epidemic Dynamics Model Considering Individual Movements and Urban Areas

Boxu Pan, Jie Yang, Yu Wang, Zehao Wang, Yuangeng Zhu, Zhiqiang Zhang
{"title":"Research of Epidemic Dynamics Model Considering Individual Movements and Urban Areas","authors":"Boxu Pan, Jie Yang, Yu Wang, Zehao Wang, Yuangeng Zhu, Zhiqiang Zhang","doi":"10.1109/IC-NIDC54101.2021.9660546","DOIUrl":null,"url":null,"abstract":"With the rapid spread of COVID-19, hundreds of millions of people worldwide have been infected. In order to cope with the epidemic, experts from various countries have carried out a lot of research works. Most of these works chose to use the traditional SEIR model, but the traditional model doesn't consider the individual's movement in the city. Based on the transmission characteristics of COVID-19, this paper optimized the traditional SEIR model by combining the in-depth mining and processed multiple data, such as the real epidemic data published by some official organizations, as well as data with certain credibility obtained from reference papers, journals or newspapers. Compared with the traditional SEIR model, the proposed model takes into account the impact of individuals' movement and the division of urban functional areas. The outcomes can play a certain role in the prediction and analysis of the spread of the epidemic in cities with regular individuals' movements and functions of urban areas.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IC-NIDC54101.2021.9660546","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

With the rapid spread of COVID-19, hundreds of millions of people worldwide have been infected. In order to cope with the epidemic, experts from various countries have carried out a lot of research works. Most of these works chose to use the traditional SEIR model, but the traditional model doesn't consider the individual's movement in the city. Based on the transmission characteristics of COVID-19, this paper optimized the traditional SEIR model by combining the in-depth mining and processed multiple data, such as the real epidemic data published by some official organizations, as well as data with certain credibility obtained from reference papers, journals or newspapers. Compared with the traditional SEIR model, the proposed model takes into account the impact of individuals' movement and the division of urban functional areas. The outcomes can play a certain role in the prediction and analysis of the spread of the epidemic in cities with regular individuals' movements and functions of urban areas.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
考虑个体运动和城市区域的流行病动力学模型研究
随着COVID-19的迅速传播,全球有数亿人受到感染。为了应对这一流行病,各国专家进行了大量的研究工作。这些作品大多选择使用传统的SEIR模型,但是传统模型并没有考虑到个人在城市中的运动。本文根据COVID-19的传播特点,结合对多个数据的深度挖掘和处理,对传统的SEIR模型进行了优化,这些数据包括一些官方机构公布的真实疫情数据,以及从参考论文、期刊或报纸中获得的具有一定可信度的数据。与传统的SEIR模型相比,该模型考虑了个体迁移和城市功能区划分的影响。研究结果可对人群流动规律和城区功能规律的城市疫情传播进行预测和分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Improving Dense FAQ Retrieval with Synthetic Training A Security Integrated Attestation Scheme for Embedded Devices Zero-Shot Voice Cloning Using Variational Embedding with Attention Mechanism Convolutional Neural Network Based Transmit Power Control for D2D Communication in Unlicensed Spectrum WCD: A New Chinese Online Social Media Dataset for Clickbait Analysis and Detection
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1