Multiple routes transmitted epidemics on multiplex networks

Zhao, Dawei; Li, Lixiang; Peng, Haipeng; Luo, Qun; Yang, Yixian

doi:10.1016/j.physleta.2014.01.014

Cited by 119 publications

(86 citation statements)

References 26 publications

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“…Most of the works on modeling the dynamics of diffusion over multilayer networks have used epidemic models such as SIS, SIR [203,[232][233][234][235][236][237][238][239][240][241]. In a similar approach with single-layer networks, the dynamics of epidemic spreading according to the SIS and SIR models over multilayer networks are described as a twoor three-state process, respectively.…”

Section: Disease Spreading In Multilayer Networkmentioning

confidence: 99%

“…Therefore, most of the works on spreading processes over multilayer networks [234,239,240,[243][244][245][246][247][248] have extended epidemic-like models by considering different infection rates dependent on the types of the layers. A recent contribution in this context proposed a generalized epidemic mean-field (GEMF) framework, which was capable of modeling epidemic-like spreading processes with more complex states in multiplex network layers (compared to two or three states in the SIS and SIR models).…”

Section: Disease Spreading In Multilayer Networkmentioning

confidence: 99%

“…This research provides vital insight in terms of the efficiency of vaccination under different circumstances, yet empirical networks usually have particular topology properties that require special care. This includes taking into account community structure [170,188,[329][330][331][332], changes in the network structure over time [52,218,[333][334][335][336][337], as well as multilayer properties that are typical for a large plethora of real-world networks [203,231,240,241,[338][339][340][341][342][343][344][345][346][347]. In what follows, we will review vaccination programs that take into account the particular aspects of these properties on various types of networks.…”

Section: Vaccination On Other Types Of Networkmentioning

confidence: 99%

“…Multiplex networks, as a typical kind of multilayer network structure, can be regarded as the combination of several network layers, which contain the same nodes (or share at least some fraction of nodes) yet different intra-layer connections [203,231,240,241,[338][339][340][341][342][343][344][356][357][358]. For each node, we term its counterpart in every layer as a 'replica'.…”

Section: Vaccination On Multiplex Networkmentioning

confidence: 99%

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Statistical physics of vaccination

et al. 2016

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Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination-one of the most important preventive measures of modern times-is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research.

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Section: Disease Spreading In Multilayer Networkmentioning

confidence: 99%

Section: Disease Spreading In Multilayer Networkmentioning

confidence: 99%

Section: Vaccination On Other Types Of Networkmentioning

confidence: 99%

Section: Vaccination On Multiplex Networkmentioning

confidence: 99%

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Statistical physics of vaccination

et al. 2016

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“…Lda [15][16][17][18] is the limited topic distribution of each text in the dataset, each topic is a three layers Bayes mode that is organized in the form of probability. Lda ignores the order of words in the text and the grammatical relations, the text with a dirichelet distribution of the distribution of kdimensional implicit random variables to represent the document, which to simulate the formation process of the document.…”

Section: Topic Similarity Modelmentioning

confidence: 99%

Users Connection across Social Media Sites Based On Users'Relationship Vector

Zhou¹,

Li²,

Wei³

et al. 2017

Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)

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Abstract.The identification and association of multiple identities in different online social networks (osns) is an important problem, and also is the basis for many applications. At present, most of technologies try to solve this problem by matching the username of social networks or calculating the similarity of a pair of users' personal information from different platforms. However, due to the anonymity of social networks, these methods often fail to identify and associate multiple virtual identities. In this paper, we propose a classification method based on machine learning. Our method jointly consider the time, the text and the topic of the similarity to construct the feature vector to characterize the user's relationship. And we use the feature vectors to train the classifier. The model is evaluated on real world dataset, the twitter and sina weibo. The experimental results show that our method is effective.

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