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      Variability of contact process in complex networks

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      1 , 1,2 , 1 , 1
      Chaos
      American Institute of Physics

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          Abstract

          We study numerically how the structures of distinct networks influence the epidemic dynamics in contact process. We first find that the variability difference between homogeneous and heterogeneous networks is very narrow, although the heterogeneous structures can induce the lighter prevalence. Contrary to non-community networks, strong community structures can cause the secondary outbreak of prevalence and two peaks of variability appeared. Especially in the local community, the extraordinarily large variability in early stage of the outbreak makes the prediction of epidemic spreading hard. Importantly, the bridgeness plays a significant role in the predictability, meaning the further distance of the initial seed to the bridgeness, the less accurate the predictability is. Also, we investigate the effect of different disease reaction mechanisms on variability, and find that the different reaction mechanisms will result in the distinct variabilities at the end of epidemic spreading.

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          Most cited references35

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          The role of the airline transportation network in the prediction and predictability of global epidemics

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            Epidemic dynamics and endemic states in complex networks.

            We study by analytical methods and large scale simulations a dynamical model for the spreading of epidemics in complex networks. In networks with exponentially bounded connectivity we recover the usual epidemic behavior with a threshold defining a critical point below that the infection prevalence is null. On the contrary, on a wide range of scale-free networks we observe the absence of an epidemic threshold and its associated critical behavior. This implies that scale-free networks are prone to the spreading and the persistence of infections whatever spreading rate the epidemic agents might possess. These results can help understanding computer virus epidemics and other spreading phenomena on communication and social networks.
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              Dynamical patterns of epidemic outbreaks in complex heterogeneous networks.

              We present a thorough inspection of the dynamical behavior of epidemic phenomena in populations with complex and heterogeneous connectivity patterns. We show that the growth of the epidemic prevalence is virtually instantaneous in all networks characterized by diverging degree fluctuations, independently of the structure of the connectivity correlation functions characterizing the population network. By means of analytical and numerical results, we show that the outbreak time evolution follows a precise hierarchical dynamics. Once reached the most highly connected hubs, the infection pervades the network in a progressive cascade across smaller degree classes. Finally, we show the influence of the initial conditions and the relevance of statistical results in single case studies concerning heterogeneous networks. The emerging theoretical framework appears of general interest in view of the recently observed abundance of natural networks with complex topological features and might provide useful insights for the development of adaptive strategies aimed at epidemic containment.
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                Author and article information

                Journal
                Chaos
                Chaos
                CHAOEH
                Chaos
                American Institute of Physics
                1054-1500
                1089-7682
                December 2011
                07 December 2011
                07 December 2011
                : 21
                : 4
                Affiliations
                [1 ]Web Sciences Center, University of Electronic Science and Technology of China , Chengdu 610054, People’s Republic of China
                [2 ]2Computer Experimental Teaching Center, University of Electronic Science and Technology of China , Chengdu 610054, People’s Republic of China
                Author notes
                Article
                032104CHA 1.3664403 11258R
                10.1063/1.3664403
                7112449
                22225367
                863f4964-ae1e-48f7-a6a5-41e27dc54e0c
                Copyright © 2011 American Institute of Physics

                1054-1500/2011/21(4)/043130/6/ $30.00

                All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/ ).

                Page count
                Pages: 6
                Funding
                Award ID: 90924011
                Award ID: 20110491705
                Award ID: 2010HH0002
                Categories
                Regular Articles

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