337 research outputs found
Asymmetrically interacting spreading dynamics on complex layered networks
The spread of disease through a physical-contact network and the spread of
information about the disease on a communication network are two intimately
related dynamical processes. We investigate the asymmetrical interplay between
the two types of spreading dynamics, each occurring on its own layer, by
focusing on the two fundamental quantities underlying any spreading process:
epidemic threshold and the final infection ratio. We find that an epidemic
outbreak on the contact layer can induce an outbreak on the communication
layer, and information spreading can effectively raise the epidemic threshold.
When structural correlation exists between the two layers, the information
threshold remains unchanged but the epidemic threshold can be enhanced, making
the contact layer more resilient to epidemic outbreak. We develop a physical
theory to understand the intricate interplay between the two types of spreading
dynamics.Comment: 29 pages, 14 figure
The physics of spreading processes in multilayer networks
The study of networks plays a crucial role in investigating the structure,
dynamics, and function of a wide variety of complex systems in myriad
disciplines. Despite the success of traditional network analysis, standard
networks provide a limited representation of complex systems, which often
include different types of relationships (i.e., "multiplexity") among their
constituent components and/or multiple interacting subsystems. Such structural
complexity has a significant effect on both dynamics and function. Throwing
away or aggregating available structural information can generate misleading
results and be a major obstacle towards attempts to understand complex systems.
The recent "multilayer" approach for modeling networked systems explicitly
allows the incorporation of multiplexity and other features of realistic
systems. On one hand, it allows one to couple different structural
relationships by encoding them in a convenient mathematical object. On the
other hand, it also allows one to couple different dynamical processes on top
of such interconnected structures. The resulting framework plays a crucial role
in helping achieve a thorough, accurate understanding of complex systems. The
study of multilayer networks has also revealed new physical phenomena that
remain hidden when using ordinary graphs, the traditional network
representation. Here we survey progress towards attaining a deeper
understanding of spreading processes on multilayer networks, and we highlight
some of the physical phenomena related to spreading processes that emerge from
multilayer structure.Comment: 25 pages, 4 figure
Suppressing disease spreading by using information diffusion on multiplex networks
Although there is always an interplay between the dynamics of information
diffusion and disease spreading, the empirical research on the systemic
coevolution mechanisms connecting these two spreading dynamics is still
lacking. Here we investigate the coevolution mechanisms and dynamics between
information and disease spreading by utilizing real data and a proposed
spreading model on multiplex network. Our empirical analysis finds asymmetrical
interactions between the information and disease spreading dynamics. Our
results obtained from both the theoretical framework and extensive stochastic
numerical simulations suggest that an information outbreak can be triggered in
a communication network by its own spreading dynamics or by a disease outbreak
on a contact network, but that the disease threshold is not affected by
information spreading. Our key finding is that there is an optimal information
transmission rate that markedly suppresses the disease spreading. We find that
the time evolution of the dynamics in the proposed model qualitatively agrees
with the real-world spreading processes at the optimal information transmission
rate.Comment: 11 pages, 8 figure
A framework for epidemic spreading in multiplex networks of metapopulations
We propose a theoretical framework for the study of epidemics in structured
metapopulations, with heterogeneous agents, subjected to recurrent mobility
patterns. We propose to represent the heterogeneity in the composition of the
metapopulations as layers in a multiplex network, where nodes would correspond
to geographical areas and layers account for the mobility patterns of agents of
the same class. We analyze both the classical Susceptible-Infected-Susceptible
and the Susceptible-Infected-Removed epidemic models within this framework, and
compare macroscopic and microscopic indicators of the spreading process with
extensive Monte Carlo simulations. Our results are in excellent agreement with
the simulations. We also derive an exact expression of the epidemic threshold
on this general framework revealing a non-trivial dependence on the mobility
parameter. Finally, we use this new formalism to address the spread of diseases
in real cities, specifically in the city of Medellin, Colombia, whose
population is divided into six socio-economic classes, each one identified with
a layer in this multiplex formalism.Comment: 13 pages, 11 figure
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