The effects of global awareness on the spreading of epidemics in multiplex networks

Zang, Hai-juan

doi:10.1016/j.physa.2017.11.076

Cited by 21 publications

(18 citation statements)

References 34 publications

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“…Furthermore, the effects of the heterogeneity of individuals' responses, and the structures of the virtual and contact networks are investigated, and the existence of two-stage effects on the epidemic threshold is demonstrated [274]. Zang considered that the awareness transmission probability equals the fraction of nodes in the aware state, and found that the epidemic spreading is greatly suppressed [275].…”

Section: Coevolution Of Awareness and Epidemics On Multiplex Networkmentioning

confidence: 99%

Coevolution spreading in complex networks

et al. 2019

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a b s t r a c tThe propagations of diseases, behaviors and information in real systems are rarely independent of each other, but they are coevolving with strong interactions. To uncover the dynamical mechanisms, the evolving spatiotemporal patterns and critical phenomena of networked coevolution spreading are extremely important, which provide theoretical foundations for us to control epidemic spreading, predict collective behaviors in social systems, and so on. The coevolution spreading dynamics in complex networks has thus attracted much attention in many disciplines. In this review, we introduce recent progress in the study of coevolution spreading dynamics, emphasizing the contributions from the perspectives of statistical mechanics and network science. The theoretical methods, critical phenomena, phase transitions, interacting mechanisms, and effects of network topology for four representative types of coevolution spreading mechanisms, including the coevolution of biological contagions, social contagions, epidemic-awareness, and epidemic-resources, are presented in detail, and the challenges in this field as well as open issues for future studies are also discussed.

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Section: Coevolution Of Awareness and Epidemics On Multiplex Networkmentioning

confidence: 99%

Coevolution spreading in complex networks

et al. 2019

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“…[71]. Apart from generalised models they focus on awareness cascades [112] and global awareness [106]. Competition takes place also for information diffusion to prevent an epidemic spreading [107] [90], for opinion formation [113] and the impact of heterogeneity and awareness [93].…”

Section: ) Interaction Between Processesmentioning

confidence: 99%

Interacting Spreading Processes in Multilayer Networks: A Systematic Review

2020

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The world of network science is fascinating and filled with complex phenomena that we aspire to understand. One of them is the dynamics of spreading processes over complex networked structures. Building the knowledge-base in the field where we can face more than one spreading process propagating over a network that has more than one layer is a challenging task, as the complexity comes both from the environment in which the spread happens and from characteristics and interplay of spreads' propagation. As this cross-disciplinary field bringing together computer science, network science, biology and physics has rapidly grown over the last decade, there is a need to comprehensively review the current state-of-the-art and offer to the research community a roadmap that helps to organise the future research in this area. Thus, this survey is a first attempt to present the current landscape of the multi-processes spread over multilayer networks and to suggest the potential ways forward.INDEX TERMS complex networks, information diffusion, multilayer networks, spreading processes

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“…Assume that the local dynamics f belongs to the QUAD function class. When the infection rate λ > 1 (1−α)ρ(A) , which indicates that the disease may spread, then the endemic equilibrium E * is globally asymptotically stable, as well as the behavioral dynamics system (3) will achieve behavioral cluster synchronization for two groups based on the information about individuals' infection under the grouped control strategies (24) if there exists a positive θ > 0 such that:…”

Section: Uninfected Individualsmentioning

confidence: 99%

“…Therefore, we can claim that when λ > 1 (1−α)ρ(A) , resulting in the infectious disease transmission, then the endemic equilibrium is globally asymptotically stable. And under the control strategy (24), only when conditions (26) are satisfied, the behavioral dynamics system (3) reaches behavioral cluster synchronization for two groups based on the disease information. Then the proof is completed.…”

Section: Uninfected Individualsmentioning

confidence: 99%

“…When an epidemic disease spreads in a realistic network, the information about the disease transfer only along the edges, which means that there will no direct transmission in any pairs of individuals without relationship between them. Taking into consideration of the significance about spreading of epidemic diseases on complex networks, many successful works have been devoted by a lot of researchers from mathematical biology field [22]- [24]. The problem about global stability and attractivity of a network-based SIS epidemic model with nonmonotone incidence rate was investigated in [25], and the conditions guaranteeing global asymptotical stability or global attractivity of an endemic equilibrium are also obtained.…”

Section: Introductionmentioning

confidence: 99%

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Feedback pinning control of collective behaviors aroused by epidemic spread on complex networks

Pan

Feng

et al. 2019

Chaos: An Interdisciplinary Journal of Nonlinear Science

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This paper investigates epidemic control behavioral synchronization for a class of complex networks resulting from spread of epidemic diseases via pinning feedback control strategy. Based on the quenched mean field theory, epidemic control synchronization models with inhibition of contact behavior is constructed, combining with the epidemic transmission system and the complex dynamical network carrying extra controllers. By the properties of convex functions and Gerschgorin theorem, the epidemic threshold of the model is obtained, and the global stability of disease-free equilibrium is analyzed. For individual's infected situation, when epidemic spreads, two types of feedback control strategies depended on the diseases' information are designed: the one only adds controllers to infected individuals, the other adds controllers both to infected and susceptible ones. And by using Lyapunov stability theory, under designed controllers, some criteria that guarantee epidemic control synchronization system achieving behavior synchronization are also derived. Several numerical simulations are performed to show the effectiveness of our theoretical results. As far as we know, this is the first work to address the controlling behavioral synchronization induced by epidemic spreading under the pinning feedback mechanism. It is hopeful that we may have more deeper insight into the essence between disease's spreading and collective behavior controlling in complex dynamical networks.

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The effects of global awareness on the spreading of epidemics in multiplex networks

Cited by 21 publications

References 34 publications

Coevolution spreading in complex networks

Coevolution spreading in complex networks

Interacting Spreading Processes in Multilayer Networks: A Systematic Review

Feedback pinning control of collective behaviors aroused by epidemic spread on complex networks

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