Epidemic spreading on uncorrelated heterogenous networks with non-uniform transmission

Wang, Jia-Zeng; Liu, Zengrong; Xu, Jianmin

doi:10.1016/j.physa.2007.04.034

Cited by 46 publications

(18 citation statements)

References 19 publications

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“…These original works stimulate many related studies [7,14]. Wang et al [17] investigated the effect of non-uniform transmission on the infection threshold and found that the topological structure was not the only factor to dominate the propagation behavior on networks. Olinky and Stone [15] designed a kind of connectivity-dependent infection scheme to yield the non-zero critical threshold even in SF networks.…”

mentioning

confidence: 88%

SIS model of epidemic spreading on dynamical networks with community

Xia

Sun

Rao

et al. 2009

Front. Comput. Sci. China

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We present a new epidemic Susceptible-Infected-Susceptible (SIS) model to investigate the spreading behavior on networks with dynamical topology and community structure. Individuals in the model are mobile agents who are allowed to perform the inter-community (i.e., long-range) motion with the probability p. The mean-field theory is utilized to derive the critical threshold (λ C ) of epidemic spreading inside separate communities and the influence of the long-range motion on the epidemic spreading. The results indicate that λ C is only related with the population density within the community, and the long-range motion will make the original disease-free community become the endemic state. Large-scale numerical simulations also demonstrate the theoretical approximations based on our new epidemic model. The current model and analysis will help us to further understand the propagation behavior of real epidemics taking place on social networks.

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confidence: 88%

SIS model of epidemic spreading on dynamical networks with community

Xia

Sun

Rao

et al. 2009

Front. Comput. Sci. China

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“…We found the admission time after infection is approximate to a logarithmic normal distribution [43]- [46]. A logarithmic normal random variable [42] was used to model the admission time of infected agents in (8), shown at the bottom of the page. T Adm (μ, σ) is the admission time after infection, μ and σ are its mean and standard deviation, γ 1 and γ 2 are two uniform random numbers in the range [0, 1].…”

Section: Behavior Modelsmentioning

confidence: 99%

An ACP Approach to Public Health Emergency Management: Using a Campus Outbreak of H1N1 Influenza as a Case Study

Duan

Cao

Wang

et al. 2013

IEEE Trans. Syst. Man Cybern, Syst.

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In order to tackle the infeasibility of building mathematical models and conducting physical experiments for public health emergencies in the real world, we apply the Artificial societies, Computational experiments, and Parallel execution (ACP) approach to public health emergency management. We use the largest collective outbreak of H1N1 influenza at a Chinese university in 2009 as a case study. We build an artificial society to simulate the outbreak at the university. In computational experiments, aiming to obtain comparable results with the real data, we apply the same intervention strategy as that was used during the real outbreak. Then, we compare experiment results with real data to verify our models, including spatial models, population distribution, weighted social networks, contact patterns, students' behaviors, and models of H1N1 influenza disease, in the artificial society. In the phase of parallel execution, alternative intervention strategies are proposed to control the outbreak of H1N1 influenza more effectively. Our models and their application to intervention strategy improvement show that the ACP approach is useful for public health emergency management.Index Terms-Agent-based simulation, artificial societies, computational experiments, emergency management, parallel execution (ACP), public health.

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“…[30], the authors proposed a kind of connectivity-dependent infection scheme, which can yield threshold effects even in scale-free networks where they would otherwise be unexpected. Additional ingredients include saturation effects [31], constant infectivity [32], nonuniform transmission [33], finite populations [34], traffic-driven mechanisms [35], and piece-wise infection probability [36], which have been integrated into the SIS or SIR models.…”

Section: Introductionmentioning

confidence: 99%

Effects of delayed recovery and nonuniform transmission on the spreading of diseases in complex networks

Xia

Wang

Sanz

et al. 2013

Physica A: Statistical Mechanics and its Applications

108

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a b s t r a c tWe investigate the effects of delaying the time to recovery (delayed recovery) and of nonuniform transmission on the propagation of diseases on structured populations. Through a mean-field approximation and large-scale numerical simulations, we find that postponing the transition from the infectious to the recovered states can largely reduce the epidemic threshold, therefore promoting the outbreak of epidemics. On the other hand, if we consider nonuniform transmission among individuals, the epidemic threshold increases, thus inhibiting the spreading process. When both mechanisms are at work, the latter might prevail, hence resulting in an increase of the epidemic threshold with respect to the standard case, in which both ingredients are absent. Our findings are of interest for a better understanding of how diseases propagate on structured populations and to a further design of efficient immunization strategies.

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Epidemic spreading on uncorrelated heterogenous networks with non-uniform transmission

Cited by 46 publications

References 19 publications

SIS model of epidemic spreading on dynamical networks with community

SIS model of epidemic spreading on dynamical networks with community

An ACP Approach to Public Health Emergency Management: Using a Campus Outbreak of H1N1 Influenza as a Case Study

Effects of delayed recovery and nonuniform transmission on the spreading of diseases in complex networks

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