Epidemic spreading with awareness diffusion on activity-driven networks

Hu, Ping; Ding, Li; An, Xuming

doi:10.1103/physreve.98.062322

Cited by 34 publications

(16 citation statements)

References 39 publications

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“…e connection correlation describes a preference for nodes to interact with others in terms of their class. It is similar to the degree-degree correlation in the static network [8] and the activity-activity correlation in AD networks [24]. Here, we refer to it as the class-class correlation since both the type and the activity rate of a node are considered in each class.…”

Section: Topological Properties Of the Instantaneous Networkmentioning

confidence: 91%

“…where ⌈x⌉ denotes the minimum integer that is no less than x, P a (k 1 ) denotes the probability that the node i is connected by k 1 active nodes in G t , and P g (k − k 1 ) denotes the probability of k − k 1 interactions between the node i and other neighbor nodes. e expression of P a (k 1 ) is the same as that for AD networks [24], which is given by…”

Section: Topological Properties Of the Instantaneous Networkmentioning

confidence: 99%

“…e activity probability distribution is chosen as F(a) ∝ a − 2 . For simplicity, we assume that there are n different activity rates in the network and they are uniformly sampled in (0, 1] [24]. e sampling interval is 1/N, and the minimum activity rate in the network is denoted as a 1 .…”

Section: Topological Properties Of the Instantaneous Networkmentioning

confidence: 99%

“…In this section, we perform numerical simulations to investigate the epidemic spreading process on CAD networks. For simplicity, we consider five activity rates of the nodes, i.e., a ∈ 0.5, 0.6, 0.7, 0.8, 0.9 { } [24], and the activity rate distribution obeys F(a) ∝ a − 2 . Accordingly, the proportions of nodes with the activity rate 0.5, 0.6, 0.7, 0.8, and 0.9 are 0.344, 0.239, 0.176, 0.135, and 0.106, respectively.…”

Section: Numerical Simulationsmentioning

confidence: 99%

“…e simple formulation of AD networks and its extensions is amenable to analytical treatment and thus facilitates the study of contagion processes on time-varying networks. Examples include epidemic spreading processes with different network structures [19,20], information diffusion processes with complex adoption models [21,22], and the interplay between the epidemic spreading and awareness diffusion with different risk perception mechanisms [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

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Contagion Processes on Time‐Varying Networks with Homophily‐Driven Group Interactions

Ding

2019

Complexity

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The complicated interaction patterns among heterogeneous individuals have a profound impact on the contagion process in the networks. In recent years, there has been increasing evidence for the emergence of many-body interactions between two or more nodes in a wide range of biological and social networks. To encode these multinode interactions explicitly, the simplicial complex is now a popular alternative to simple networks. Meanwhile, the time-varying network has been acknowledged as a key ingredient of the contagion process. In this paper, we consider the connectivity pattern of networks affected by the homophily effect associated with individual attributes and investigate the impact of homophily-driven group interactions on the contagion process in temporal networks. The simplicial complex modeling framework is adopted to capture stochastic interactions between passively selected nodes in the paradigm of activity-driven networks. We study the evolution of infection and the epidemic threshold of the contagion process by both analytical and numerical methods. Our results on statistical topological properties of instantaneous network may shed light on accurately characterizing the evolution curve of infection. Furthermore, we show the impact of the homophily-driven interaction pattern on the epidemic threshold, which generalizes the existing results on both the paradigmatic activity-driven network and the simplicial activity-driven network.

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Section: Topological Properties Of the Instantaneous Networkmentioning

confidence: 91%

Section: Topological Properties Of the Instantaneous Networkmentioning

confidence: 99%

Section: Topological Properties Of the Instantaneous Networkmentioning

confidence: 99%

Section: Numerical Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Contagion Processes on Time‐Varying Networks with Homophily‐Driven Group Interactions

Ding

2019

Complexity

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Epidemics Spread Over Networks: Influence of Infrastructure and Opinions

She

Gracy

Sundaram

et al. 2023

Cyber–Physical–Human Systems

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Co-evolution spreading of multiple information and epidemics on two-layered networks under the influence of mass media

Wang

Xia

2020

Nonlinear Dyn

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During epidemic outbreaks, there are various types of information about epidemic prevention disseminated simultaneously among the population. Meanwhile, the mass media also scrambles to report the information related to the epidemic. Inspired by these phenomena, we devise a model to discuss the dynamical characteristics of the co-evolution spreading of multiple information and epidemic under the influence of mass media. We construct the co-evolution model under the framework of two-layered networks and gain the dynamical equations and epidemic critical point with the help of the micro-Markov chain approach. The expression of epidemic critical point show that the positive and negative information have a direct impact on the epidemic critical point. Moreover, the mass media can indirectly affect the epidemic size and epidemic critical point through their interference with the dissemination of epidemic-relevant information. Though extensive numerical experiments, we examine the accuracy of the dynamical equations and expression of the epidemic critical point, showing that the dynamical characteristics of co-evolution spreading can be well described by the dynamic equations and the epidemic critical point is able to be accurately calculated by the derived expression. The experimental results demonstrate that accelerating positive information dissemination and enhancing the propaganda intensity of mass media can efficaciously restrain the epidemic spreading. Interestingly, the way to accelerate the dissemination of negative information can also alleviate the epidemic to a certain extent when the positive information hardly spreads. Current results can provide some useful clues for epidemic prevention and control on the basis of epidemic-relevant information dissemination.

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Epidemic spreading with awareness diffusion on activity-driven networks

Cited by 34 publications

References 39 publications

Contagion Processes on Time‐Varying Networks with Homophily‐Driven Group Interactions

Contagion Processes on Time‐Varying Networks with Homophily‐Driven Group Interactions

Epidemics Spread Over Networks: Influence of Infrastructure and Opinions

Co-evolution spreading of multiple information and epidemics on two-layered networks under the influence of mass media

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