Coupled Dynamic Model of Resource Diffusion and Epidemic Spreading in Time‐Varying Multiplex Networks

Huang, Ping; Chen, Xiaolong; Tang, Ming; Cai, Shi-Min

doi:10.1155/2021/6629105

Cited by 13 publications

(4 citation statements)

References 49 publications

(57 reference statements)

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“…In recent years, an increasing focus within disciplines such as statistical physics and computer science has been directed toward the examination of evolving network models [24]. Addressing this context, Huang et al formulated a coupled resource-epidemic dynamic model on the temporal network and analyzed the asymmetric interactions between the epidemic and the resource [25]. Guo et al further enriched the understanding of temporal networks by introducing a two-layer network framework with partial mappings [26].…”

Section: Open Accessmentioning

confidence: 99%

Epidemic spreading with an awareness-based adaptive mechanism in temporal multiplex networks

Xiong,

Zhou

2023

Front. Phys.

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Social interaction, which is intricate and time-varying, has emerged as a pivotal consideration in epidemic spreading. In this paper, we devise a UAU–SIS model for simulating awareness diffusion and epidemic spreading on temporal multiplex networks. Drawing inspiration from individuals’ self-protection behaviors, a novel adaptive update mechanism is developed. To provide a more precise representation of the collective social interactions encompassing individuals, we introduce the higher-order network structure encompassing temporal variability. Building upon the established framework of the microscopic Markov chain approach (MMCA) for static networks, we extend its applicability to the condition of temporal networks and derive the threshold within the coupled dynamics. Our extensive simulations illuminate the dual role of awareness in epidemic mitigation. Beyond solely diminishing infection probabilities through self-protective measures, individual awareness additionally facilitates to change the network structure to separate them from the infected. By elucidating these fundamental characteristics, our research contributes to advancing more effective strategies for epidemic mitigation and containment.

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Section: Open Accessmentioning

confidence: 99%

Epidemic spreading with an awareness-based adaptive mechanism in temporal multiplex networks

Xiong,

Zhou

2023

Front. Phys.

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“…Based on the assumption of constant thresholds, because of variations in the average degree, saddle-node bifurcation occurs, leading to a continuous increase and subsequent discontinuous decrease in the final adoption size with increasing network average degree. Research has found that factors such as the initial number of seeds [16,17], clustering coefficient [18], multilayer networks [19], network temporal dynamics [20] and timevarying [21] significantly affect information propagation in the threshold model.…”

Section: Introductionmentioning

confidence: 99%

The information propagation mechanism of individual heterogeneous adoption behavior under the heterogeneous network

Cui,

Zhu

2024

Front. Phys.

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To explore heterogeneous behavior diffusion in the same population under a heterogeneous network, this study establishes a dual-layer heterogeneous network model to simulate the spreading patterns of hesitant individuals and regular individuals in different networks. It analyzes the influence of to investigate heterogeneous behavior diffusion within the same population in a heterogeneous network, this paper establishes a dual-layer heterogeneous network model to simulate the spreading patterns of hesitant individuals and regular individuals in different networks. It analyzes the influence of individuals’ hesitation states and different spreading patterns in heterogeneous networks on the information diffusion mechanism. In the propagation of this model, when either layer of the dual-layer network becomes the dominant spreading layer, second-order continuous spreading is observed. However, when the regular adoption behavior serves as the dominant spreading layer, its spreading threshold occurs earlier than the spreading threshold when hesitant adoption behavior is the dominant spreading layer. When there is no dominant spreading layer, first-order discontinuous spreading is observed, and the spreading threshold occurs later than the threshold in the presence of a dominant spreading layer. Additionally, the study discovers the existence of cross-phase transitions during the spreading process. The results of theoretical analysis align with the simulation results.

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“…A multitude of researchers have employed a dual-layer network structure, to establish coupled information–epidemic propagation models, aiming to expound upon the reciprocal relationship between the spread of epidemic-related information and the proliferation of the epidemic itself [ 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ]. Within this framework, the lower-layer network represents the epidemic spreading stratum, with nodes symbolizing individuals, and edges signifying physical contact relationships in reality.…”

Section: Introductionmentioning

confidence: 99%

Coupled Information–Epidemic Spreading Dynamics with Selective Mass Media

Xian

Yang

2023

Entropy

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As a pandemic emerges, information on epidemic prevention disseminates among the populace, and the propagation of that information interacts with the proliferation of the disease. Mass media serve a pivotal function in facilitating the dissemination of epidemic-related information. Investigating coupled information–epidemic dynamics, while accounting for the promotional effect of mass media in information dissemination, is of significant practical relevance. Nonetheless, in the extant research, scholars predominantly employ an assumption that mass media broadcast to all individuals equally within the network: this assumption overlooks the practical constraint imposed by the substantial social resources required to accomplish such comprehensive promotion. In response, this study introduces a coupled information–epidemic spreading model with mass media that can selectively target and disseminate information to a specific proportion of high-degree nodes. We employed a microscopic Markov chain methodology to scrutinize our model, and we examined the influence of the various model parameters on the dynamic process. The findings of this study reveal that mass media broadcasts directed towards high-degree nodes within the information spreading layer can substantially reduce the infection density of the epidemic, and raise the spreading threshold of the epidemic. Additionally, as the mass media broadcast proportion increases, the suppression effect on the disease becomes stronger. Moreover, with a constant broadcast proportion, the suppression effect of mass media promotion on epidemic spreading within the model is more pronounced in a multiplex network with a negative interlayer degree correlation, compared to scenarios with positive or absent interlayer degree correlation.

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Coupled Dynamic Model of Resource Diffusion and Epidemic Spreading in Time‐Varying Multiplex Networks

Cited by 13 publications

References 49 publications

Epidemic spreading with an awareness-based adaptive mechanism in temporal multiplex networks

Epidemic spreading with an awareness-based adaptive mechanism in temporal multiplex networks

The information propagation mechanism of individual heterogeneous adoption behavior under the heterogeneous network

Coupled Information–Epidemic Spreading Dynamics with Selective Mass Media

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