Epidemic spreading in weighted networks: An edge-based mean-field solution

Yang, Zimo; Zhou, Tao

doi:10.1103/physreve.85.056106

Cited by 80 publications

(53 citation statements)

References 47 publications

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“…At this point, the weighted SIS model degenerates to the unweighted one, or it is equivalent to the most homogeneous case with all weights are the same. Therefore, the results are in accordance with the known conclusion [31] that in the absence of correlation between structure and weight, the most homogeneous weight distribution leads to the widest epidemic spreading.…”

Section: Discussionsupporting

confidence: 90%

“…Yan et al [30] investigated the epidemic spreading in weighted scale-free networks and the simulation results indicated that the more homogeneous weight distribution of the network is, the more quickly epidemic spreads on it. This finding was further demonstrated by an edge-based mean-field solution [31]. Chu et al [32] showed that weight distribution has strong impacts on both epidemic threshold and prevalence.…”

Section: Introductionmentioning

confidence: 83%

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Strong ties promote the epidemic prevalence in susceptible–infected–susceptible spreading dynamics

Cui

Yang

Zhou

2016

Physica A: Statistical Mechanics and its Applications

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Understanding spreading dynamics will benefit society as a whole in better preventing and controlling diseases, as well as facilitating the socially responsible information while depressing destructive rumors. In network-based spreading dynamics, edges with different weights may play far different roles: a friend from afar usually brings novel stories, and an intimate relationship is highly risky for a flu epidemic. In this article, we propose a weighted susceptible-infected-susceptible model on complex networks, where the weight of an edge is defined by the topological proximity of the two associated nodes. Each infected individual is allowed to select limited number of neighbors to contact, and a tunable parameter is introduced to control the preference to contact through high-weight or low-weight edges. Experimental results on six real networks show that the epidemic prevalence can be largely promoted when strong ties are favored in the spreading process. By comparing with two statistical null models respectively with randomized topology and randomly redistributed weights, we show that the distribution pattern of weights, rather than the topology, mainly contributes to the experimental observations. Further analysis suggests that the weight-weight correlation strongly affects the results: high-weight edges are more significant in keeping high epidemic prevalence when the weight-weight correlation is present.

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Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 83%

Strong ties promote the epidemic prevalence in susceptible–infected–susceptible spreading dynamics

Cui

Yang

Zhou

2016

Physica A: Statistical Mechanics and its Applications

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“…Scores of researchers have proven that the strong heterogeneity of degree distribution can reduce or even vanish the epidemic threshold under some certain conditions [e.g., on the scale-free networks of degree distribution p(k) ∼ k −γD with degree exponent γ D ≤ 3 in thermodynamic limit] [15,16]. On weighted networks, some researches have shown that the inhomogeneity of weight distribution can also significantly affect the epidemic dynamics, such as the epidemic threshold and epidemic prevalence [17][18][19][20][21][22][23][24]. For instance, Zhou et al suggested that increasing the dispersion of weight distribution can reduce the velocity of epidemic spreading as well as the epidemic prevalence [17,18].…”

Section: Introductionmentioning

confidence: 99%

Epidemic spreading on complex networks with general degree and weight distributions

et al. 2014

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The spread of disease on complex networks has attracted widely attention in the physics community. Recent works have demonstrated that heterogeneous degree and weight distributions have a significant influence on the epidemic dynamics. In this study, a novel edge-weight based compartmental approach is developed to estimate the epidemic threshold and epidemic size (final infected density) on networks with general degree and weight distributions, and a remarkable agreement with numerics is obtained. Even in complex network with the strong heterogeneous degree and weight distributions, this approach is worked. We then propose an edge-weight based removal strategy with different biases, and find that such a strategy can effectively control the spread of epidemic when the highly weighted edges are preferentially removed, especially when the weight distribution of a network is extremely heterogenous. The theoretical results from the suggested method can accurately predict the above removal effectiveness.

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“…Yang et al have studied the effect of connection patterns on the outbreak of epidemic. They have found that a heterogeneous network structure accelerates the spread of infectious disease [38,39,40,41]. K. P. Chan et al have investigated the effect of aging and links removal on epidemic spread in scalefree networks.…”

Section: Introductionmentioning

confidence: 99%

Coupled effects of local movement and global interaction on contagion

Liu

Chen

et al. 2015

Physica A: Statistical Mechanics and its Applications

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By incorporating segregated spatial domain and individual-based linkage into the SIS (susceptible-infected-susceptible) model, we propose a generalized epidemic model which can change from the territorial epidemic model to the networked epidemic model. The role of the individual-based linkage between different spatial domains is investigated. As we adjust the timescale parameter τ from 0 to unity, which represents the degree of activation of the individual-based linkage, three regions are found. Within the region of 0 < τ < 0.02, the epidemic is determined by local movement and is sensitive to the timescale τ . Within the region of 0.02 < τ < 0.5, the epidemic is insensitive to the timescale τ . Within the region of 0.5 < τ < 1, the outbreak of the epidemic is determined by the structure of the individualbased linkage. As we keep an eye on the first region, the role of activating the individual-based linkage in the present model is similar to the role of the shortcuts in the two-dimensional small world network. Only activating a small number of the individual-based linkage can prompt the outbreak of the epidemic globally. The role of narrowing segregated spatial domain and reducing mobility in epidemic control is checked. These two measures are found to be conducive to curbing the spread of infectious disease only when the global interaction is suppressed. A log-log relation between the change in the number of infected individuals and the timescale τ is found.By calculating the epidemic threshold and the mean first encounter time, we heuristically analyze the microscopic characteristics of the propagation of the epidemic in the present model.

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Epidemic spreading in weighted networks: An edge-based mean-field solution

Cited by 80 publications

References 47 publications

Strong ties promote the epidemic prevalence in susceptible–infected–susceptible spreading dynamics

Strong ties promote the epidemic prevalence in susceptible–infected–susceptible spreading dynamics

Epidemic spreading on complex networks with general degree and weight distributions

Coupled effects of local movement and global interaction on contagion

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