Suppressing traffic-driven epidemic spreading by use of the efficient routing protocol

Yang, Huijie; Wu, Zhi-Xi

doi:10.1088/1742-5468/2014/03/p03018

Cited by 23 publications

(9 citation statements)

References 46 publications

(53 reference statements)

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“…For traffic flow dynamics on complex networks, an essential ingredient is the routing strategy. In the model, one can demonstrate that epidemic spreading can be modulated or controlled through a proper choice of the local routing strategy [27] or global routing protocol [28]. For a fixed routing strategy, the network structure can also affect the spreading dynamics.…”

Section: Introductionmentioning

confidence: 99%

Traffic-driven epidemic spreading in correlated networks

2015

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In spite of the extensive previous efforts on traffic dynamics and epidemic spreading in complex networks, the problem of traffic-driven epidemic spreading on correlated networks has not been addressed. Interestingly, we find that the epidemic threshold, a fundamental quantity underlying the spreading dynamics, exhibits a nonmonotonic behavior in that it can be minimized for some critical value of the assortativity coefficient, a parameter characterizing the network correlation. To understand this phenomenon, we use the degree-based mean-field theory to calculate the traffic-driven epidemic threshold for correlated networks. The theory predicts that the threshold is inversely proportional to the packet-generation rate and the largest eigenvalue of the betweenness matrix. We obtain consistency between theory and numerics. Our results may provide insights into the important problem of controlling and/or harnessing real-world epidemic spreading dynamics driven by traffic flows.

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Section: Introductionmentioning

confidence: 99%

Traffic-driven epidemic spreading in correlated networks

2015

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“…They found that the epidemic threshold in the SIS model decreases as flow increases, and emergence of traffic congestion slows down the spread of epidemics. Then, Yang et al [26,27] further studied the relation between traffic dynamics and the SIS epidemic model, and found that the epidemic can be controlled by fine tuning the local or global routing schemes. Furthermore, they obtained that the epidemic threshold can be enhanced by cutting some specific edges in the network [28].…”

Section: Epidemiological Dynamicsmentioning

confidence: 99%

Understanding the Propagation and Control Strategies of Congestion in Urban Rail Transit Based on Epidemiological Dynamics Model

2019

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With the construction of the urban rail transit (URT) network, the explosion of passenger volume is more rapid than the increased capacity of the newly built infrastructure, which results in serious passenger flow congestion (PLC). Understanding the propagation process of PLC is the key to formulate sustainable policies for reducing congestion and optimizing management. This study proposes a susceptible-infected-recovered (SIR) model based on the theories of epidemiological dynamics and complex network to analyze the PLC propagation. We simulate the PLC propagation under various situations, and analyze the sensitivity of PLC propagation to model parameters. Finally, the control strategies of restricting PLC propagation are introduced from two aspects, namely, supply control and demand control. The results indicate that both of the two control strategies contribute to relieving congestion pressure. The propagating scope of PLC is more sensitive when taking mild supply control, whereas, the demand control strategy shows some advantages in flexibly implementing and dealing with serious congestion. These results are of important guidance for URT agencies to understand the mechanism of PLC propagation and formulate appropriate congestion control strategies.

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“…In the area of communication networks, [56] modelled epidemic dynamics by considering data packets as the infection agents traveling between random source-destination pairs. Taking [56] as the modelling basis, [57] and [58] have studied the question on how to control epidemic spreading by link removals and via different routing schemes respectively. These works assumed stochastic interactions between node pairs.…”

Section: Background Basics and Related Workmentioning

confidence: 99%

Modelling Spreading Process Induced by Agent Mobility in Complex Networks

Chai

2018

IEEE Trans. Netw. Sci. Eng.

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Abstract-Most conventional epidemic models assume contact-based contagion process. We depart from this assumption and study epidemic spreading process in networks caused by agents acting as carrier of infection. These agents traverse from origins to destinations following specific paths in a network and in the process, infecting the sites they travel across. We focus our work on the Susceptible-Infected-Removed (SIR) epidemic model and use continuous-time Markov chain analysis to model the impact of such agent mobility induced contagion mechanics by taking into account the state transitions of each node individually, as oppose to most conventional epidemic approaches which usually consider the mean aggregated behavior of all nodes. Our approach makes one mean field approximation to reduce complexity from exponential to polynomial. We study both network-wide properties such as epidemic threshold as well as individual node vulnerability under such agent assisted infection spreading process. Furthermore, we provide a first order approximation on the agents' vulnerability since infection is bi-directional. We compare our analysis of spreading process induced by agent mobility against contact-based epidemic model via a case study on London Underground network, the second busiest metro system in Europe, with real dataset recording commuters' activities in the system. We highlight the key differences in the spreading patterns between the contact-based vs. agent assisted spreading models. Specifically, we show that our model predicts greater spreading radius than conventional contact-based model due to agents' movements. Another interesting finding is that, in contrast to contact-based model where nodes located more centrally in a network are proportionally more prone to infection, our model shows no such strict correlation as in our model, nodes may not be highly susceptible even located at the heart of the network and vice versa.

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Suppressing traffic-driven epidemic spreading by use of the efficient routing protocol

Cited by 23 publications

References 46 publications

Traffic-driven epidemic spreading in correlated networks

Traffic-driven epidemic spreading in correlated networks

Understanding the Propagation and Control Strategies of Congestion in Urban Rail Transit Based on Epidemiological Dynamics Model

Modelling Spreading Process Induced by Agent Mobility in Complex Networks

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