Optimal resource diffusion for suppressing disease spreading in multiplex networks
Authored by Wei Wang, Xiaolong Chen, Shimin Cai, H Eugene Stanley, Lidia A Braunstein
Date Published: 2018
DOI: 10.1088/1742-5468/aabfcc
Sponsors:
Chinese National Natural Science Foundation
United States Defense Threat Reduction Agency (DTRA)
United States Department of Energy (DOE)
United States National Science Foundation (NSF)
Platforms:
No platforms listed
Model Documentation:
Other Narrative
Mathematical description
Model Code URLs:
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Abstract
Resource diffusion is a ubiquitous phenomenon, but how it impacts
epidemic spreading has received little study. We propose a model that
couples epidemic spreading and resource diffusion in multiplex networks.
The spread of disease in a physical contact layer and the recovery of
the infected nodes are both strongly dependent upon resources supplied
by their counterparts in the social layer. The generation and diffusion
of resources in the social layer are in turn strongly dependent upon the
state of the nodes in the physical contact layer. Resources diffuse
preferentially or randomly in this model. To quantify the degree of
preferential diffusion, a bias parameter that controls the resource
diffusion is proposed. We conduct extensive simulations and find that
the preferential resource diffusion can change phase transition type of
the fraction of infected nodes. When the degree of interlayer
correlation is below a critical value, increasing the bias parameter
changes the phase transition from double continuous to single
continuous. When the degree of interlayer correlation is above a
critical value, the phase transition changes from multiple continuous to
first discontinuous and then to hybrid. We find hysteresis loops in the
phase transition. We also find that there is an optimal resource
strategy at each fixed degree of interlayer correlation under which the
threshold reaches a maximum and the disease can be maximally suppressed.
In addition, the optimal controlling parameter increases as the degree
of inter-layer correlation increases.
Tags
Agent-based models
Complex networks
Scale-Free Networks
networks
network dynamics
random graphs
percolation
Epidemic
epidemic modelling
Impact