Epidemic dynamics and endemic states in complex networks.

By Romualdo Pastor-Satorras, Alessandro Vespignani. Physical review. E, Statistical, nonlinear, and soft matter physics, 2001

1,583 citations recorded by the source corpus.

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.

Subfields: Public Health, Environmental and Occupational Health, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/55bca8f1a505b558d8f5605a53328e68a5c0d375

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In Public Health, Environmental and Occupational Health

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Citations in the corpus

Contact processes on random graphs with power law degree distributions have critical value 0 (2009, methodology): “Pastor-Satorras and Vespignani (2001a, 2001b, 2002) have made an extensive study of this model using mean-field methods.”
Diffusion of Behavior and Equilibrium Properties in Network Games (2007, background): “If instead the distribution of costs is convex, then the adoption will continue to gain speed rather than lose speed.(21)”
Evolution of networks (2001, methodology): “Nevertheless, the main parameter of interest describing the spread of the disease is the same, that is, λ, and main results for the SIR model on networks [208] are similar to those for the SIS model [206,207,209], so here we discuss the simpler case.”
Epidemic spreading in real networks: an eigenvalue viewpoint (2003, background): “IIS-9817496, IIS-9988876, IIS0083148, IIS-0113089, IIS-0209107 IIS-0205224 by the Pennsylvania Infrastructure Technology Alliance (PITA) Grant No. 22-901-0001, and by the Defense Advanced Research Projects Agency under Contract No.”
Epidemic modeling in metapopulation systems with heterogeneous coupling pattern: theory and simulations. (2007, background): “…P ( k ), de-ﬁned as the probability that any given node has degree k , and have been found to have a large impact on epidemic phenomena on complex contact patterns (Anderson & May, 1992; Pastor-Satorras & Vespignani, 2001 a , b ; Moreno et al. , 2002; Lloyd & May, 2001; Barth´elemy et al. , 2005).”
Analysis and Control of Epidemics: A Survey of Spreading Processes on Complex Networks (2015, neutral): “[57] R.”
Discrete-time Markov chain approach to contact-based disease spreading in complex networks (2009, background): “Physicist’s approaches to problems in epidemiology involve statistical physics, the theory of phase transitions and critical phenomena [7], which have been extremely helpful to grasp the macroscopic behavior of epidemic outbreaks [8–16].”
Statistical physics of vaccination (2016, methodology): “It was the first approach to the study of the SIS model in complex networks [171].”
Mixing patterns in networks. (2002, background): “Consider for example, the spread of diseases on networks, which has been the subject of much attention in the recent networks literature [64, 65, 66, 67, 68, 69, 70, 71].”
Analyzing and modeling real-world phenomena with complex networks: a survey of applications (2007, background): “The lack of epidemic threshold in social networks with scale-free topology is worrying because for any transmission rate the disease can propagate to all nodes of the network [541, 546, 547, 548].”
The Structure and Function of Complex Networks (2003, methodology): “The SIS model cannot be solved exactly on a network as the SIR model can, but a detailed mean-field treatment has been given by Pastor-Satorras and Vespignani [332, 333] for SIS epidemics on the configuration model.”