Interplay between SIR-based disease spreading and awareness diffusion on multiplex networks

In this paper, we propose a coupled multiplex network framework to model the epidemic spreading and its corresponding information diffusion among a population. In the model, as far as the information perception on the epidemics is concerned, the individuals can be divided into two classes, namely aw...

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Bibliographic Details
Published in:Journal of parallel and distributed computing 2018-05, Vol.115, p.20-28
Main Authors: Zheng, Chunyuan, Xia, Chengyi, Guo, Quantong, Dehmer, Matthias
Format: Article
Language:English
Subjects:
Epidemic spreading
Information diffusion
Multiplex network
Transmission dynamics
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Summary:In this paper, we propose a coupled multiplex network framework to model the epidemic spreading and its corresponding information diffusion among a population. In the model, as far as the information perception on the epidemics is concerned, the individuals can be divided into two classes, namely aware or unaware ones; Meanwhile, the awareness diffusion is depicted by utilizing the traditional contact process. From the perspective of infectious disease spreading, the contagion dynamics among nodes can be characterized with the classic SIR (susceptible–infective–recovered) model. Based on the microscopic Markov chain approach, we build the probability tree to describe the switching process between different states, and then intensively perform the theoretical analysis for the state transition. In particular, we analytically derive the epidemic threshold regarding the disease propagation, which is correlated with the multiplex network topology and the coupling relationship between two transmission dynamics. After being compared with extensive numerical Monte Carlo (MC) simulations, it is clearly found that the achieved analytical results concur with the MC simulations. Current results will be beneficial to substantially enhance the predictability of the epidemic outbreaks within many realistic dissemination cases. •We propose a coupled multiplex network to model the epidemic dynamics.•The microscopic Markov chain approach is used to derive the epidemic threshold.•We build the probability tree to characterize the state transition process.•Extensive simulation experiments validate the effectiveness of our model.
ISSN:0743-7315
1096-0848
DOI:10.1016/j.jpdc.2018.01.001