TY - JOUR
T1 - Suppression of epidemic spreading in complex networks by local information based behavioral responses
AU - Zhang, Hai Feng
AU - Xie, Jia Rong
AU - Tang, Ming
AU - Lai, Ying Cheng
PY - 2014/12/1
Y1 - 2014/12/1
N2 - The interplay between individual behaviors and epidemic dynamics in complex networks is a topic of recent interest. In particular, individuals can obtain different types of information about the disease and respond by altering their behaviors, and this can affect the spreading dynamics, possibly in a significant way. We propose a model where individuals' behavioral response is based on a generic type of local information, i.e., the number of neighbors that has been infected with the disease. Mathematically, the response can be characterized by a reduction in the transmission rate by a factor that depends on the number of infected neighbors. Utilizing the standard susceptible-infected-susceptible and susceptible-infected-recovery dynamical models for epidemic spreading, we derive a theoretical formula for the epidemic threshold and provide numerical verification. Our analysis lays on a solid quantitative footing the intuition that individual behavioral response can in general suppress epidemic spreading. Furthermore, we find that the hub nodes play the role of "double-edged sword" in that they can either suppress or promote outbreak, depending on their responses to the epidemic, providing additional support for the idea that these nodes are key to controlling epidemic spreading in complex networks.
AB - The interplay between individual behaviors and epidemic dynamics in complex networks is a topic of recent interest. In particular, individuals can obtain different types of information about the disease and respond by altering their behaviors, and this can affect the spreading dynamics, possibly in a significant way. We propose a model where individuals' behavioral response is based on a generic type of local information, i.e., the number of neighbors that has been infected with the disease. Mathematically, the response can be characterized by a reduction in the transmission rate by a factor that depends on the number of infected neighbors. Utilizing the standard susceptible-infected-susceptible and susceptible-infected-recovery dynamical models for epidemic spreading, we derive a theoretical formula for the epidemic threshold and provide numerical verification. Our analysis lays on a solid quantitative footing the intuition that individual behavioral response can in general suppress epidemic spreading. Furthermore, we find that the hub nodes play the role of "double-edged sword" in that they can either suppress or promote outbreak, depending on their responses to the epidemic, providing additional support for the idea that these nodes are key to controlling epidemic spreading in complex networks.
UR - http://www.scopus.com/inward/record.url?scp=84941127005&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84941127005&partnerID=8YFLogxK
U2 - 10.1063/1.4896333
DO - 10.1063/1.4896333
M3 - Article
C2 - 25554026
SN - 1054-1500
VL - 24
SP - 43106
JO - Chaos (Woodbury, N.Y.)
JF - Chaos (Woodbury, N.Y.)
IS - 4
ER -