On the dynamical interplay between awareness and epidemic spreading in multiplex networks

By Clara Granell, Sergio Gómez, Alex Arenas. Physical Review Letters, 2013

847 citations recorded by the source corpus.

We present the analysis of the interrelation between two processes accounting for the spreading of an epidemic, and the information awareness to prevent its infection, on top of multiplex networks. This scenario is representative of an epidemic process spreading on a network of persistent real contacts, and a cyclic information awareness process diffusing in the network of virtual social contacts between the same individuals. The topology corresponds to a multiplex network where two diffusive processes are interacting affecting each other. The analysis using a microscopic Markov chain approach reveals the phase diagram of the incidence of the epidemics and allows us to capture the evolution of the epidemic threshold depending on the topological structure of the multiplex and the interrelation with the awareness process. Interestingly, the critical point for the onset of the epidemics has a critical value (metacritical point) defined by the awareness dynamics and the topology of the virtual network, from which the onset increases and the epidemics incidence decreases.

Subfields: Experimental and Cognitive Psychology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/864c3689b1559762575f90d709e7f88901b7432e

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Citations in the corpus

Complex network approaches to nonlinear time series analysis (2019, background): “Multiplex and multilayer networks explicitly incorporate multiple levels of social interactions and have been successfully applied to the study of disease spreading and diffusion dynamics [87,88] or the evolution of cooperation in the presence of social dilemmas [89].”
The echo chamber effect on social media (2021, methodology): “Classical epidemic models such as the susceptible–infected–recovered (SIR) model (44) have been used to study the diffusion of information, such as rumors or news (45–47).”
The structure and dynamics of multilayer networks (2014, background): “In this way, disease spreading has been recently coupled to social dynamics [256], in order to take into account how the social awareness about the spreading of a disease can influence the impact of the latter.”
Effects of temporal correlations in social multiplex networks (2016, positive): “Finally, we explore the impact of temporal correlations on the dynamics of coupled epidemic/awareness processes unfolding on different layers 25 , showing that they can either slow down or speed up the epidemic spread, depending on the region of the parameter space defining the model.”
Data Based Identification and Prediction of Nonlinear and Complex Dynamical Systems (2016, background): “Indeed, deterministic and stochastic state changes can account for a variety of emergent phenomena, such as the outbreak of epidemic spreading [432], cooperation among selfish individuals [433], oscillations in biological systems [434], power blackout [435], financial crisis [436], and phase transit”
Mathematical Formulation of Multilayer Networks (2013, background): “References [21,22,27,28] recently introduced the concepts of supra-adjacency (i.”
Multilayer networks (2013, background): “Reference [144] studied interacting SIS dynamics on multiplex networks in which one layer has literal SIS dynamics on real social contacts and the other layer has SIS-like awareness dynamics (of information about”
Coupled disease–behavior dynamics on complex networks: A review (2015, background): “recently incorporated information awareness into a disease model embedded in a multiplex network [110], where the physical contact layer supports epidemic process and the virtual contact layer supports awareness diffusion.”
Ranking in interconnected multilayer networks reveals versatile nodes (2013, background): “It is important to discuss the difference between the topological structure which represents the core of this study, namely interconnected multilayer networks [4, 5, 6, 7, 8, 9, 10], and other multilayer structures which have been named multiplexes in the past and have been the subject of recent stu”