Coupling dynamics of epidemic spreading and information diffusion on complex networks

Zhan, Xiu-Xiu; Liu, Chuang; Zhou, Ge; Zhang, Zi-Ke; Sun, Gui‐Quan; Zhu, Jonathan J. H.; Jin, Zhen

doi:10.1016/j.amc.2018.03.050

Cited by 146 publications

(101 citation statements)

References 63 publications

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“…Zhan et al [250] analyzed the coevolution of information and two representative diseases (i.e., H7N9 and Dengue fever). They collected daily data about the two diseases from the Chinese Center for Disease Control and Prevention, and crawled for information about the diseases from Sina Weibo.…”

Section: Empirical Analysesmentioning

confidence: 99%

Coevolution spreading in complex networks

et al. 2019

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a b s t r a c tThe propagations of diseases, behaviors and information in real systems are rarely independent of each other, but they are coevolving with strong interactions. To uncover the dynamical mechanisms, the evolving spatiotemporal patterns and critical phenomena of networked coevolution spreading are extremely important, which provide theoretical foundations for us to control epidemic spreading, predict collective behaviors in social systems, and so on. The coevolution spreading dynamics in complex networks has thus attracted much attention in many disciplines. In this review, we introduce recent progress in the study of coevolution spreading dynamics, emphasizing the contributions from the perspectives of statistical mechanics and network science. The theoretical methods, critical phenomena, phase transitions, interacting mechanisms, and effects of network topology for four representative types of coevolution spreading mechanisms, including the coevolution of biological contagions, social contagions, epidemic-awareness, and epidemic-resources, are presented in detail, and the challenges in this field as well as open issues for future studies are also discussed.

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Section: Empirical Analysesmentioning

confidence: 99%

Coevolution spreading in complex networks

et al. 2019

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“…The method used for generating the importance is random forests where the importance of a feature increases whenever a split in the tree using that feature minimizes the prediction error [43]. 15 Complexity…”

Section: Conflicts Of Interestmentioning

confidence: 99%

“…These inactivity cascades are mainly constructed from the structure of the modeled network, where the network structure has already shown to be crucial in understanding the dynamics of any process that takes place on top of a network such as the structure of the World Wide Web networks [8,9] and social network analysis [10][11][12][13]. Moreover, network structure is correlated in many studies to understanding the dynamics of the processes over networks such as epidemic dynamics [14,15], knowledge spread [16], and knowledge transfer [17]. The information produced and evolved on the Stack Exchange website as an information exchange platform makes this work also connected to the information dynamics area [15,18,19], where we are concerned in the decay of the information production process on the Stack Exchange website as a medium of knowledge production and sharing.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, network structure is correlated in many studies to understanding the dynamics of the processes over networks such as epidemic dynamics [14,15], knowledge spread [16], and knowledge transfer [17]. The information produced and evolved on the Stack Exchange website as an information exchange platform makes this work also connected to the information dynamics area [15,18,19], where we are concerned in the decay of the information production process on the Stack Exchange website as a medium of knowledge production and sharing.…”

Section: Introductionmentioning

confidence: 99%

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Postmortem Analysis of Decayed Online Social Communities: Cascade Pattern Analysis and Prediction

Abufouda

2018

Complexity

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Recently, many online social networks, such as MySpace, Orkut, and Friendster, have faced inactivity decay of their members, which contributed to the collapse of these networks. The reasons, mechanics, and prevention mechanisms of such inactivity decay are not fully understood. In this work, we analyze decayed and alive subwebsites from the Stack Exchange platform. The analysis mainly focuses on the inactivity cascades that occur among the members of these communities. We provide measures to understand the decay process and statistical analysis to extract the patterns that accompany the inactivity decay. Additionally, we predict cascade size and cascade virality using machine learning. The results of this work include a statistically significant difference of the decay patterns between the decayed and the alive subwebsites. These patterns are mainly cascade size, cascade virality, cascade duration, and cascade similarity. Additionally, the contributed prediction framework showed satisfactorily prediction results compared to a baseline predictor. Supported by empirical evidence, the main findings of this work are (1) there are significantly different decay patterns in the alive and the decayed subwebsites of the Stack Exchange; (2) the cascade's node degrees contribute more to the decay process than the cascade's virality, which indicates that the expert members of the Stack Exchange subwebsites were mainly responsible for the activity or inactivity of the Stack Exchange subwebsites; (3) the Statistics subwebsite is going through decay dynamics that may lead to it becoming fully-decayed; (4) the decay process is not governed by only one network measure, it is better described using multiple measures; (5) decayed subwebsites were originally less resilient to inactivity decay, unlike the alive subwebsites; and (6) network's structure in the early stages of its evolution dictates the activity/inactivity characteristics of the network.

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“…Mathematical model is an important tool to better understand epidemiological patterns and disease control. Since Kermack and McKendrick [1] published their insights on the underlying mechanisms of transmission and control of infectious diseases, various epidemic models (for example, deterministic models [2][3][4] , spatially explicit models [5][6][7] , models on complex network [8][9][10] , models based on real data [11][12][13] and stochastic models [15,16,22] ) have been extensively established and investigated, which have led to great progress in the prevention and control of diseases. In the study of infectious disease dynamics, incidence Fig.…”

Section: Introductionmentioning

confidence: 99%

A stochastic SIRS epidemic model with non-monotone incidence rate under regime-switching

Lan

Lin

Wei

et al. 2019

Journal of the Franklin Institute

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In this paper, we propose and discuss a stochastic SIRS epidemic model with non-monotone incidence rate under regime-switching. First of all, we show that there is a unique positive solution, which is a prerequisite for analyzing the long-term behavior of the stochastic model. Then, a threshold dynamic determined by the basic reproduction number R s 0 is established: the disease can be eradicated almost surely if R s 0 < 1 and under mild extra conditions, whereas if R s 0 > 1 , the densities of the distributions of the solution can converge in L 1 to an invariant density by using the Markov semigroups theory. Finally, based on realistic parameters obtained from previous literatures, numerical simulations have been performed to verify our analytical results.

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Coupling dynamics of epidemic spreading and information diffusion on complex networks

Cited by 146 publications

References 63 publications

Coevolution spreading in complex networks

Coevolution spreading in complex networks

Postmortem Analysis of Decayed Online Social Communities: Cascade Pattern Analysis and Prediction

A stochastic SIRS epidemic model with non-monotone incidence rate under regime-switching

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