{"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.
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.