Suppression of epidemic spreading in complex networks by local information based behavioral responses

Hai-Feng Zhang, Jia-Rong Xie, Ming Tang, Ying-Cheng Lai

Research output: Contribution to journalArticle

71 Citations (Scopus)
4 Downloads (Pure)

Abstract

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 susceptibleinfected-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. VC 2014 AIP Publishing LLC.
Original languageEnglish
Article number043106
JournalChaos
Volume24
DOIs
Publication statusPublished - Oct 2014

Fingerprint

Epidemic Spreading
Complex networks
Complex Networks
retarding
Numerical Verification
Recovery
hubs
Dynamical Model
Vertex of a graph
recovery
thresholds

Cite this

Suppression of epidemic spreading in complex networks by local information based behavioral responses. / Zhang, Hai-Feng; Xie, Jia-Rong; Tang, Ming; Lai, Ying-Cheng.

In: Chaos, Vol. 24, 043106, 10.2014.

Research output: Contribution to journalArticle

Zhang, Hai-Feng ; Xie, Jia-Rong ; Tang, Ming ; Lai, Ying-Cheng. / Suppression of epidemic spreading in complex networks by local information based behavioral responses. In: Chaos. 2014 ; Vol. 24.
@article{3578eb4b829144afafa2d28e5ffaaedb,
title = "Suppression of epidemic spreading in complex networks by local information based behavioral responses",
abstract = "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 susceptibleinfected-susceptible and susceptible-infected-recovery dynamical models for epidemic spreading, we derive a theoretical formula for the epidemic threshold and provide numerical verification. Ouranalysis 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. VC 2014 AIP Publishing LLC.",
author = "Hai-Feng Zhang and Jia-Rong Xie and Ming Tang and Ying-Cheng Lai",
note = "This work was funded by the National Natural Science Foundation of China (Grant Nos. 61473001, 11105025, and 11331009) and the Doctoral Research Foundation of Anhui University (Grant No. 02303319). Y.C.L. was supported by AFOSR under Grant No. FA9550-10-1-0083.",
year = "2014",
month = "10",
doi = "10.1063/1.4896333",
language = "English",
volume = "24",
journal = "Chaos",
issn = "1054-1500",
publisher = "American Institute of Physics",

}

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

N1 - This work was funded by the National Natural Science Foundation of China (Grant Nos. 61473001, 11105025, and 11331009) and the Doctoral Research Foundation of Anhui University (Grant No. 02303319). Y.C.L. was supported by AFOSR under Grant No. FA9550-10-1-0083.

PY - 2014/10

Y1 - 2014/10

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 susceptibleinfected-susceptible and susceptible-infected-recovery dynamical models for epidemic spreading, we derive a theoretical formula for the epidemic threshold and provide numerical verification. Ouranalysis 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. VC 2014 AIP Publishing LLC.

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 susceptibleinfected-susceptible and susceptible-infected-recovery dynamical models for epidemic spreading, we derive a theoretical formula for the epidemic threshold and provide numerical verification. Ouranalysis 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. VC 2014 AIP Publishing LLC.

U2 - 10.1063/1.4896333

DO - 10.1063/1.4896333

M3 - Article

VL - 24

JO - Chaos

JF - Chaos

SN - 1054-1500

M1 - 043106

ER -