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2020 ; 385
(ä): 125428
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Effects of heterogeneous self-protection awareness on resource-epidemic
coevolution dynamics
#MMPMID32834189
Chen X
; Gong K
; Wang R
; Cai S
; Wang W
Appl Math Comput
2020[Nov]; 385
(ä): 125428
PMID32834189
show ga
Recent studies have demonstrated that the allocation of individual resources has
a significant influence on the dynamics of epidemic spreading. In the real
scenario, individuals have a different level of awareness for self-protection
when facing the outbreak of an epidemic. To investigate the effects of the
heterogeneous self-awareness distribution on the epidemic dynamics, we propose a
resource-epidemic coevolution model in this paper. We first study the effects of
the heterogeneous distributions of node degree and self-awareness on the epidemic
dynamics on artificial networks. Through extensive simulations, we find that the
heterogeneity of self-awareness distribution suppresses the outbreak of an
epidemic, and the heterogeneity of degree distribution enhances the epidemic
spreading. Next, we study how the correlation between node degree and
self-awareness affects the epidemic dynamics. The results reveal that when the
correlation is positive, the heterogeneity of self-awareness restrains the
epidemic spreading. While, when there is a significant negative correlation,
strong heterogeneous or strong homogeneous distribution of the self-awareness is
not conducive for disease suppression. We find an optimal heterogeneity of
self-awareness, at which the disease can be suppressed to the most extent.
Further research shows that the epidemic threshold increases monotonously when
the correlation changes from most negative to most positive, and a critical value
of the correlation coefficient is found. When the coefficient is below the
critical value, an optimal heterogeneity of self-awareness exists; otherwise, the
epidemic threshold decreases monotonously with the decline of the self-awareness
heterogeneity. At last, we verify the results on four typical real-world networks
and find that the results on the real-world networks are consistent with those on
the artificial network.
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