Traffic-driven epidemic spreading in finite-size scale-free networks

The study of complex networks sheds light on the relation between the structure and function of complex systems. One remarkable result is the absence of an epidemic threshold in infinite-size, scale-free networks, which implies that any infection will perpetually propagate regardless of the spreadin...

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2009-10, Vol.106 (40), p.16897-16902
Main Authors: Meloni, Sandro, Arenas, Alex, Moreno, Yamir
Format: Article
Language:English
Subjects:
Communicable Diseases - epidemiology
Complex networks
Complex systems
Computer Communication Networks
Computer Security
Computer Simulation
Disease models
Disease outbreaks
Disease Outbreaks - statistics & numerical data
Disease transmission
Disease Transmission, Infectious - statistics & numerical data
Epidemics
Epidemiologic Methods
Epidemiology
Humans
Incidence
Infections
Infectious diseases
Internet
Models, Theoretical
Numerical analysis
Physical Sciences
Simulation
Studies
Traffic
Traffic congestion
Traffic flow
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Summary:The study of complex networks sheds light on the relation between the structure and function of complex systems. One remarkable result is the absence of an epidemic threshold in infinite-size, scale-free networks, which implies that any infection will perpetually propagate regardless of the spreading rate. The vast majority of current theoretical approaches assumes that infections are transmitted as a reaction process from nodes to all neighbors. Here we adopt a different perspective and show that the epidemic incidence is shaped by traffic-flow conditions. Specifically, we consider the scenario in which epidemic pathways are defined and driven by flows. Through extensive numerical simulations and theoretical predictions, it is shown that the value of the epidemic threshold in scale-free networks depends directly on flow conditions, in particular on the first and second moments of the betweenness distribution given a routing protocol. We consider the scenarios in which the delivery capability of the nodes is bounded or unbounded. In both cases, the threshold values depend on the traffic and decrease as flow increases. Bounded delivery provokes the emergence of congestion, slowing down the spreading of the disease and setting a limit for the epidemic incidence. Our results provide a general conceptual framework for the understanding of spreading processes on complex networks.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.0907121106