Identifying vital edges in Chinese air route network via memetic algorithm

Wei, Du; Liang, Boyuan; Yan, Gang; Lordan, Oriol; Cao, Xianbin

doi:10.1016/j.cja.2016.12.001

Cited by 58 publications

(22 citation statements)

References 34 publications

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“…Based on this principle, it is possible to devise alternative vaccination strategies which take advantage of the heterogenous connectivity patterns in order to achieve a high level of tolerance towards infections [275]. Accordingly, other centrality indexes, such as betweenness, closeness, eigenvalue, or PageRank, can be used to design targeted vaccination strategies [289,294,295,297,298,298,[301][302][303][304][305][306][307][308][309][310][311]. In what follows, we will briefly survey these centrality indexes and their applications in targeted vaccination.…”

Section: Centrality-based Targeted Vaccination: Definitions and Simulmentioning

confidence: 99%

Statistical physics of vaccination

et al. 2016

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Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination-one of the most important preventive measures of modern times-is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research.

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Section: Centrality-based Targeted Vaccination: Definitions and Simulmentioning

confidence: 99%

Statistical physics of vaccination

et al. 2016

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“…From the perspective of complex network theory, the topology of the system can be expressed as a dynamical network of mobile agents [20][21][22][23][24]. Every agent is regarded as a node of the network and a wireless link is established between two agents if their geographical distance is less than the contact radius r. Owing to the movement of agents, the network's structure changes with time.…”

Section: Resultsmentioning

confidence: 99%

Suppressing epidemic spreading by risk-averse migration in dynamical networks

Yang¹,

Tang²,

Wang³

2018

Physica A: Statistical Mechanics and its Applications

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In this paper, we study the interplay between individual behaviors and epidemic spreading in a dynamical network. We distribute agents on a square-shaped region with periodic boundary conditions. Every agent is regarded as a node of the network and a wireless link is established between two agents if their geographical distance is less than a certain radius. At each time, every agent assesses the epidemic situation and make decisions on whether it should stay in or leave its current place. An agent will leave its current place with a speed if the number of infected neighbors reaches or exceeds a critical value E. Owing to the movement of agents, the network's structure is dynamical. Interestingly, we find that there exists an optimal value of E leading to the maximum epidemic threshold. This means that epidemic spreading can be effectively controlled by risk-averse migration. Besides, we find that the epidemic threshold increases as the recovering rate increases, decreases as the contact radius increases, and is maximized by an optimal moving speed. Our findings offer a deeper understanding of epidemic spreading in dynamical networks.

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“…For example, Du analyzed the network robustness before and after removing certain links, and found the set of vital links[21, 22]. It proves that this analysis method is effective.…”

Section: Comparison and Analysis Of Network Efficiency Evaluation Metmentioning

confidence: 99%

Comparative analysis of quantitative efficiency evaluation methods for transportation networks

Qin

Hong

2017

PLoS ONE

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An effective evaluation of transportation network efficiency could offer guidance for the optimal control of urban traffic. Based on the introduction and related mathematical analysis of three quantitative evaluation methods for transportation network efficiency, this paper compares the information measured by them, including network structure, traffic demand, travel choice behavior and other factors which affect network efficiency. Accordingly, the applicability of various evaluation methods is discussed. Through analyzing different transportation network examples it is obtained that Q-H method could reflect the influence of network structure, traffic demand and user route choice behavior on transportation network efficiency well. In addition, the transportation network efficiency measured by this method and Braess’s Paradox can be explained with each other, which indicates a better evaluation of the real operation condition of transportation network. Through the analysis of the network efficiency calculated by Q-H method, it can also be drawn that a specific appropriate demand is existed to a given transportation network. Meanwhile, under the fixed demand, both the critical network structure that guarantees the stability and the basic operation of the network and a specific network structure contributing to the largest value of the transportation network efficiency can be identified.

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Identifying vital edges in Chinese air route network via memetic algorithm

Cited by 58 publications

References 34 publications

Statistical physics of vaccination

Statistical physics of vaccination

Suppressing epidemic spreading by risk-averse migration in dynamical networks

Comparative analysis of quantitative efficiency evaluation methods for transportation networks

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