Epidemic dynamics on scale-free networks with piecewise linear infectivity and immunization

Fu, Xinchu; Small, Michael; Walker, David M.; Zhang, Haifeng

doi:10.1103/physreve.77.036113

Cited by 215 publications

(144 citation statements)

References 23 publications

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“…In reference [38], the infectivity is assumed as a constant A, which means each infected individual will generate A contacts at each time step. Recently, Fu et al proposed a piecewise linear infectivity [39], which means: if the degree k of a node is small, its infectivity is αk; otherwise its infectivity is A as a saturated value when k is beyond a constant A/α. Both the constant or the piecewise linear method, the heterogeneous infectivity of nodes with different degrees is not considered as adequately as possible in scale-free networks, that is to say there may be some nodes with different degrees which have the same infectivity, and there will be a large number of such nodes if the constant A is assigned irrelevantly or the size of underlying networks is infinite.…”

Section: Introductionmentioning

confidence: 99%

Epidemic spreading with nonlinear infectivity in weighted scale-free networks

Chu

Zhang

Guan

et al. 2011

Physica A: Statistical Mechanics and its Applications

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Abstract. In this paper, we investigate the epidemic spreading for SIR model in weighted scale-free networks with nonlinear infectivity, where the transmission rate in our analytical model is weighted. Concretely, we introduce the infectivity exponent α and the weight exponent β into the analytical SIR model, then examine the combination effects of α and β on the epidemic threshold and phase transition. We show that one can adjust the values of α and β to rebuild the epidemic threshold to a finite value, and it is observed that the steady epidemic prevalence R grows in an exponential form in the early stage, then follows hierarchical dynamics. Furthermore, we find α is more sensitive than β in the transformation of the epidemic threshold and epidemic prevalence, which might deliver some useful information or new insights in the epidemic spreading and the correlative immunization schemes.

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Section: Introductionmentioning

confidence: 99%

Epidemic spreading with nonlinear infectivity in weighted scale-free networks

Chu

Zhang

Guan

et al. 2011

Physica A: Statistical Mechanics and its Applications

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“…Recent estimates suggest around 390 million annual cases globally and rising; with 20 nearly 96 million of these exhibiting clinical symptoms [3]. Similarly, chikungunya and 21 zika are emerging diseases that are rapidly disseminating in Latin American countries, 22 raising health concerns and placing a heavy burden upon their national health 23 institutes [4][5][6].…”

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confidence: 99%

“…With it, we are able to track the bites females take on humans; which allows 63 us to reconstruct the vectorial-contact networks and to borrow tools from graph theory 64 to perform structural analyses. This, to the best of our knowledge, is on the vanguard 65 of epidemiological analysis of vector-borne diseases (although it has successfully been 66 used before for direct-contact pathogen transmission [18][19][20][21][22]). …”

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confidence: 99%

Effects of Spatial Heterogeneity on Transmission Potential in Vectorial-Contact Networks: A Comparison of Three Aedes aegypti Control Strategies

Marshall

et al. 2017

Preprint

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Dengue, chikungunya and zika are all transmitted by the Aedes aegypti mosquito. Despite the strong influence of host spatial distribution and movement patterns on the ability of mosquito vectors to transmit pathogens, there is little understanding how these complex interactions modify the spread of disease in spatially heterogeneous populations. In light of present fears of a worldwide zika epidemic, and failures to eradicate dengue and chikungunya; there is a pressing need to get a better picture of how high-resolution details such as human movement in a small landscape, modify the patterns of transmission of these diseases and how different mosquito-control interventions could be affected by these movements.In this work we use a computational agent-based model (ABM) to simulate mosquito-human interactions in two different levels of spatial heterogeneity, with human movement, and in the presence of three mosquito-control interventions (spatial spraying, the release of Wolbachia-infected mosquitoes and release of insects with dominant lethal gene). To analyse the results from each of these experiments we examined mosquito population dynamics and host to host contact networks that emerged from the distribution of consecutive bites across humans. We then compared results across experiments to understand the differential effectiveness of different interventions in both the presence and absence of spatial heterogeneities, and analysed network measures of epidemiological relevance (degree probability distributions, mean path length, network density and small-worldness).From our experiments we conclude that spatial heterogeneity greatly influences how a pathogen may spread in a host population when mediated by a mosquito vector, and that these important heterogeneities also strongly affect effectiveness of interventions. Finally, we demonstrate that these host to host vectorial-contact networks can provide operationally important information to inform selection of optimal vector-control strategies. Author SummaryMosquito-borne diseases' transmission patterns arise from the complex interactions 1 between hosts and vector. Because these interactions are influenced by host and vector 2 behaviour, spatial constraints, and other factors they are amongst the most difficult to 3 understand. In this work, we use our computational agent-based model: SoNA3BS; to 4 1 . we proceeded to show that even when mosquito population sizes are almost equal in 11 both spatial settings, the resulting vectorial-contact networks are radically different. CC-BY-NC-ND4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/210450 doi: bioRxiv preprint first posted online Oct. 28, 2017; SoNA3BS: Social Network Aedes aegypti Agent- 12This has profound implications in our understanding of how mosquito-borne diseases 13 spread in human populations and is relevant to the effective use of resources allocated 14 to stop these pat...

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“…Research on social networks has received remarkable attention in the past decade, the diffusion process on the online social network became an ongoing research topic [1][2][3][4][5][6][7]. Due to similar patterns in the spread of epidemics and social contagion processes, most research adopts the same theoretical principles for epidemics in describing the information diffusion.…”

Section: Introductionmentioning

confidence: 99%

Asymptotic Behavior of Global Positive Solution to an Information Diffusion Model with Random Perturbation in Social Network

Yuan¹,

Hu²

2018

dtcse

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Abstract. The paper explore an information diffusion models with random perturbation in social network. First, we show the models exit the unique global positive solution. By the construction of the Lyapunov function, we give the positive solution is stochastically asymptotically stable in the large around disease-free equilibrium, i.e. the conditions of the information diffusion will die out, investigate the stochastic asymptotic behavior of the positive solution around endemic equilibrium of the deterministic models, obtain the stochastic asymptotic stability condition, i.e. the conditions of the information diffusion will be persistent in social networks.

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Epidemic dynamics on scale-free networks with piecewise linear infectivity and immunization

Cited by 215 publications

References 23 publications

Epidemic spreading with nonlinear infectivity in weighted scale-free networks

Epidemic spreading with nonlinear infectivity in weighted scale-free networks

Effects of Spatial Heterogeneity on Transmission Potential in Vectorial-Contact Networks: A Comparison of Three Aedes aegypti Control Strategies

Asymptotic Behavior of Global Positive Solution to an Information Diffusion Model with Random Perturbation in Social Network

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