Optimizing Sustainable Mobility Interventions for Efficient Epidemic Containment

IF 6.7 2区 计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY IEEE Transactions on Network Science and Engineering Pub Date : 2024-12-30 DOI:10.1109/TNSE.2024.3519670
Yanggang Cheng;Shibo He;Cunqi Shao;Chao Li;Jiming Chen
{"title":"Optimizing Sustainable Mobility Interventions for Efficient Epidemic Containment","authors":"Yanggang Cheng;Shibo He;Cunqi Shao;Chao Li;Jiming Chen","doi":"10.1109/TNSE.2024.3519670","DOIUrl":null,"url":null,"abstract":"Learning from the lessons of the COVID-19 pandemic, nations are increasingly recognizing the imperative to develop sustainable mobility interventions that effectively balance epidemic control and economic stability. In response, we study a novel network immunity problem: the formulation of precise capacity limitation measures for each point of interest (POI) node within the urban mobility network. The aim is to maximize epidemic containment under the fixed resource budget for mobility intervention. To achieve this, we establish a metapopulation model on urban inter-POI networks. Our proposed model accurately fits real epidemic trajectories, demonstrating resilience to significant shifts in human movement patterns pre- and post-epidemic. Leveraging this model, we derive the generalized basic reproduction number and reframe the original problem as one that minimizes <inline-formula><tex-math>$R_{0}$</tex-math></inline-formula> under budgetary constraints. We devise a greedy capacity reduction algorithm to approximately solve these problems. Subsequently, we conduct extensive experiments on large-scale urban networks that connect 4,335 residential communities to 14,936 POIs with 5.7 million daily edges. Compared to baseline methods, our algorithm consistently achieves higher efficiency and accuracy in reducing <inline-formula><tex-math>$R_{0}$</tex-math></inline-formula> and maximizing epidemic containment. Notably, it can effectively minimize the risk of epidemic spread within the city without imposing significant constraints on overall urban mobility.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 2","pages":"823-837"},"PeriodicalIF":6.7000,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Network Science and Engineering","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10817775/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

Learning from the lessons of the COVID-19 pandemic, nations are increasingly recognizing the imperative to develop sustainable mobility interventions that effectively balance epidemic control and economic stability. In response, we study a novel network immunity problem: the formulation of precise capacity limitation measures for each point of interest (POI) node within the urban mobility network. The aim is to maximize epidemic containment under the fixed resource budget for mobility intervention. To achieve this, we establish a metapopulation model on urban inter-POI networks. Our proposed model accurately fits real epidemic trajectories, demonstrating resilience to significant shifts in human movement patterns pre- and post-epidemic. Leveraging this model, we derive the generalized basic reproduction number and reframe the original problem as one that minimizes $R_{0}$ under budgetary constraints. We devise a greedy capacity reduction algorithm to approximately solve these problems. Subsequently, we conduct extensive experiments on large-scale urban networks that connect 4,335 residential communities to 14,936 POIs with 5.7 million daily edges. Compared to baseline methods, our algorithm consistently achieves higher efficiency and accuracy in reducing $R_{0}$ and maximizing epidemic containment. Notably, it can effectively minimize the risk of epidemic spread within the city without imposing significant constraints on overall urban mobility.
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
求助全文
约1分钟内获得全文 去求助
来源期刊
IEEE Transactions on Network Science and Engineering
IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering
CiteScore
12.60
自引率
9.10%
发文量
393
期刊介绍: The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.
期刊最新文献
Table of Contents Degradation Estimation for Distributed Nonlinear Systems: A PDF-Consensus Particle Filtering Method A Hybrid Semi-Asynchronous Federated Learning and Split Learning Strategy in Edge Networks A Hybrid Multi-Agent System Approach for Distributed Composite Convex Optimization Under Unbalanced Directed Graphs Weighted Average Consensus Algorithms in Distributed and Federated Learning
×
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