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Traffic-driven SIR epidemic spreading in networks

We study SIR epidemic spreading in networks driven by traffic dynamics, which are further governed by static routing protocols. We obtain the maximum instantaneous population of infected nodes and the maximum population of ever infected nodes through simulation. We find that generally more balanced...

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Detalles Bibliográficos
Autores principales: Pu, Cunlai, Li, Siyuan, Yang, XianXia, Xu, Zhongqi, Ji, Zexuan, Yang, Jian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier B.V. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7127125/
https://www.ncbi.nlm.nih.gov/pubmed/32288096
http://dx.doi.org/10.1016/j.physa.2015.11.028
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author Pu, Cunlai
Li, Siyuan
Yang, XianXia
Xu, Zhongqi
Ji, Zexuan
Yang, Jian
author_facet Pu, Cunlai
Li, Siyuan
Yang, XianXia
Xu, Zhongqi
Ji, Zexuan
Yang, Jian
author_sort Pu, Cunlai
collection PubMed
description We study SIR epidemic spreading in networks driven by traffic dynamics, which are further governed by static routing protocols. We obtain the maximum instantaneous population of infected nodes and the maximum population of ever infected nodes through simulation. We find that generally more balanced load distribution leads to more intense and wide spread of an epidemic in networks. Increasing either average node degree or homogeneity of degree distribution will facilitate epidemic spreading. When packet generation rate [Formula: see text] is small, increasing [Formula: see text] favors epidemic spreading. However, when [Formula: see text] is large enough, traffic congestion appears which inhibits epidemic spreading.
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spelling pubmed-71271252020-04-08 Traffic-driven SIR epidemic spreading in networks Pu, Cunlai Li, Siyuan Yang, XianXia Xu, Zhongqi Ji, Zexuan Yang, Jian Physica A Article We study SIR epidemic spreading in networks driven by traffic dynamics, which are further governed by static routing protocols. We obtain the maximum instantaneous population of infected nodes and the maximum population of ever infected nodes through simulation. We find that generally more balanced load distribution leads to more intense and wide spread of an epidemic in networks. Increasing either average node degree or homogeneity of degree distribution will facilitate epidemic spreading. When packet generation rate [Formula: see text] is small, increasing [Formula: see text] favors epidemic spreading. However, when [Formula: see text] is large enough, traffic congestion appears which inhibits epidemic spreading. Elsevier B.V. 2016-03-15 2015-12-05 /pmc/articles/PMC7127125/ /pubmed/32288096 http://dx.doi.org/10.1016/j.physa.2015.11.028 Text en Copyright © 2015 Elsevier B.V. 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
Pu, Cunlai
Li, Siyuan
Yang, XianXia
Xu, Zhongqi
Ji, Zexuan
Yang, Jian
Traffic-driven SIR epidemic spreading in networks
title Traffic-driven SIR epidemic spreading in networks
title_full Traffic-driven SIR epidemic spreading in networks
title_fullStr Traffic-driven SIR epidemic spreading in networks
title_full_unstemmed Traffic-driven SIR epidemic spreading in networks
title_short Traffic-driven SIR epidemic spreading in networks
title_sort traffic-driven sir epidemic spreading in networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7127125/
https://www.ncbi.nlm.nih.gov/pubmed/32288096
http://dx.doi.org/10.1016/j.physa.2015.11.028
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