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

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...

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Autores principales: Zhang, Hai-Feng, Xie, Jia-Rong, Tang, Ming, Lai, Ying-Cheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AIP Publishing LLC 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7112481/
https://www.ncbi.nlm.nih.gov/pubmed/25554026
http://dx.doi.org/10.1063/1.4896333
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author Zhang, Hai-Feng
Xie, Jia-Rong
Tang, Ming
Lai, Ying-Cheng
author_facet Zhang, Hai-Feng
Xie, Jia-Rong
Tang, Ming
Lai, Ying-Cheng
author_sort Zhang, Hai-Feng
collection PubMed
description 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.
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spelling pubmed-71124812020-04-02 Suppression of epidemic spreading in complex networks by local information based behavioral responses Zhang, Hai-Feng Xie, Jia-Rong Tang, Ming Lai, Ying-Cheng Chaos Regular Articles 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. AIP Publishing LLC 2014-12 2014-10-20 /pmc/articles/PMC7112481/ /pubmed/25554026 http://dx.doi.org/10.1063/1.4896333 Text en © 2014 AIP Publishing LLC 1054-1500/2014/24(4)/043106/7/$30.00 All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).
spellingShingle Regular Articles
Zhang, Hai-Feng
Xie, Jia-Rong
Tang, Ming
Lai, Ying-Cheng
Suppression of epidemic spreading in complex networks by local information based behavioral responses
title Suppression of epidemic spreading in complex networks by local information based behavioral responses
title_full Suppression of epidemic spreading in complex networks by local information based behavioral responses
title_fullStr Suppression of epidemic spreading in complex networks by local information based behavioral responses
title_full_unstemmed Suppression of epidemic spreading in complex networks by local information based behavioral responses
title_short Suppression of epidemic spreading in complex networks by local information based behavioral responses
title_sort suppression of epidemic spreading in complex networks by local information based behavioral responses
topic Regular Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7112481/
https://www.ncbi.nlm.nih.gov/pubmed/25554026
http://dx.doi.org/10.1063/1.4896333
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