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Epidemic dynamics on information-driven adaptive networks

Research on the interplay between the dynamics on the network and the dynamics of the network has attracted much attention in recent years. In this work, we propose an information-driven adaptive model, where disease and disease information can evolve simultaneously. For the information-driven adapt...

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Autores principales: Zhan, Xiu-Xiu, Liu, Chuang, Sun, Gui-Quan, Zhang, Zi-Ke
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7126912/
https://www.ncbi.nlm.nih.gov/pubmed/32288352
http://dx.doi.org/10.1016/j.chaos.2018.02.010
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author Zhan, Xiu-Xiu
Liu, Chuang
Sun, Gui-Quan
Zhang, Zi-Ke
author_facet Zhan, Xiu-Xiu
Liu, Chuang
Sun, Gui-Quan
Zhang, Zi-Ke
author_sort Zhan, Xiu-Xiu
collection PubMed
description Research on the interplay between the dynamics on the network and the dynamics of the network has attracted much attention in recent years. In this work, we propose an information-driven adaptive model, where disease and disease information can evolve simultaneously. For the information-driven adaptive process, susceptible (infected) individuals who have abilities to recognize the disease would break the links of their infected (susceptible) neighbors to prevent the epidemic from further spreading. Simulation results and numerical analyses based on the pairwise approach indicate that the information-driven adaptive process can not only slow down the speed of epidemic spreading, but can also diminish the epidemic prevalence at the final state significantly. In addition, the disease spreading and information diffusion pattern on the lattice as well as on a real-world network give visual representations about how the disease is trapped into an isolated field with the information-driven adaptive process. Furthermore, we perform the local bifurcation analysis on four types of dynamical regions, including healthy, a continuous dynamic behavior, bistable and endemic, to understand the evolution of the observed dynamical behaviors. This work may shed some lights on understanding how information affects human activities on responding to epidemic spreading.
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spelling pubmed-71269122020-04-08 Epidemic dynamics on information-driven adaptive networks Zhan, Xiu-Xiu Liu, Chuang Sun, Gui-Quan Zhang, Zi-Ke Chaos Solitons Fractals Article Research on the interplay between the dynamics on the network and the dynamics of the network has attracted much attention in recent years. In this work, we propose an information-driven adaptive model, where disease and disease information can evolve simultaneously. For the information-driven adaptive process, susceptible (infected) individuals who have abilities to recognize the disease would break the links of their infected (susceptible) neighbors to prevent the epidemic from further spreading. Simulation results and numerical analyses based on the pairwise approach indicate that the information-driven adaptive process can not only slow down the speed of epidemic spreading, but can also diminish the epidemic prevalence at the final state significantly. In addition, the disease spreading and information diffusion pattern on the lattice as well as on a real-world network give visual representations about how the disease is trapped into an isolated field with the information-driven adaptive process. Furthermore, we perform the local bifurcation analysis on four types of dynamical regions, including healthy, a continuous dynamic behavior, bistable and endemic, to understand the evolution of the observed dynamical behaviors. This work may shed some lights on understanding how information affects human activities on responding to epidemic spreading. Elsevier Ltd. 2018-03 2018-02-16 /pmc/articles/PMC7126912/ /pubmed/32288352 http://dx.doi.org/10.1016/j.chaos.2018.02.010 Text en © 2018 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Zhan, Xiu-Xiu
Liu, Chuang
Sun, Gui-Quan
Zhang, Zi-Ke
Epidemic dynamics on information-driven adaptive networks
title Epidemic dynamics on information-driven adaptive networks
title_full Epidemic dynamics on information-driven adaptive networks
title_fullStr Epidemic dynamics on information-driven adaptive networks
title_full_unstemmed Epidemic dynamics on information-driven adaptive networks
title_short Epidemic dynamics on information-driven adaptive networks
title_sort epidemic dynamics on information-driven adaptive networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7126912/
https://www.ncbi.nlm.nih.gov/pubmed/32288352
http://dx.doi.org/10.1016/j.chaos.2018.02.010
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