{"title":"Spreading of Epidemics on Scale-Free Networks with Traffic Flow","authors":"Ya-Qi Wang, Guoping Jiang","doi":"10.1109/IWCFTA.2010.44","DOIUrl":null,"url":null,"abstract":"In this paper, based on the mean-field theory, we propose a new susceptible-infected-removed (SIR) model to study epidemic spreading on scale-free networks with traffic flow. Theoretical analysis shows that as the network traffic flow increases, the epidemic prevalence is obviously enhanced, and the epidemic threshold is reduced. We also find that the epidemic threshold is related to the ratio between the first and second moments of the network’s algorithm betweenness distribution. Moreover, the heterogeneity levels of the networks weaken the epidemic spreading. We confirm all results by sufficient numerical simulations.","PeriodicalId":157339,"journal":{"name":"2010 International Workshop on Chaos-Fractal Theories and Applications","volume":"81 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Workshop on Chaos-Fractal Theories and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IWCFTA.2010.44","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
In this paper, based on the mean-field theory, we propose a new susceptible-infected-removed (SIR) model to study epidemic spreading on scale-free networks with traffic flow. Theoretical analysis shows that as the network traffic flow increases, the epidemic prevalence is obviously enhanced, and the epidemic threshold is reduced. We also find that the epidemic threshold is related to the ratio between the first and second moments of the network’s algorithm betweenness distribution. Moreover, the heterogeneity levels of the networks weaken the epidemic spreading. We confirm all results by sufficient numerical simulations.