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Spread of competing viruses on heterogeneous networks

In this paper, we propose a model where two strains compete with each other at the expense of common susceptible individuals on heterogeneous networks by using pair-wise approximation closed by the probability-generating function (PGF). All of the strains obey the susceptible–infected–recovered (SIR...

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Detalles Bibliográficos
Autores principales: Chen, Shanshan, Wang, Kaihua, Sun, Mengfeng, Fu, Xinchu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society Publishing 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5434075/
https://www.ncbi.nlm.nih.gov/pubmed/28507229
http://dx.doi.org/10.1098/rsta.2016.0284
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author Chen, Shanshan
Wang, Kaihua
Sun, Mengfeng
Fu, Xinchu
author_facet Chen, Shanshan
Wang, Kaihua
Sun, Mengfeng
Fu, Xinchu
author_sort Chen, Shanshan
collection PubMed
description In this paper, we propose a model where two strains compete with each other at the expense of common susceptible individuals on heterogeneous networks by using pair-wise approximation closed by the probability-generating function (PGF). All of the strains obey the susceptible–infected–recovered (SIR) mechanism. From a special perspective, we first study the dynamical behaviour of an SIR model closed by the PGF, and obtain the basic reproduction number via two methods. Then we build a model to study the spreading dynamics of competing viruses and discuss the conditions for the local stability of equilibria, which is different from the condition obtained by using the heterogeneous mean-field approach. Finally, we perform numerical simulations on Barabási–Albert networks to complement our theoretical research, and show some dynamical properties of the model with competing viruses. This article is part of the themed issue ‘Mathematical methods in medicine: neuroscience, cardiology and pathology’.
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spelling pubmed-54340752018-06-28 Spread of competing viruses on heterogeneous networks Chen, Shanshan Wang, Kaihua Sun, Mengfeng Fu, Xinchu Philos Trans A Math Phys Eng Sci Articles In this paper, we propose a model where two strains compete with each other at the expense of common susceptible individuals on heterogeneous networks by using pair-wise approximation closed by the probability-generating function (PGF). All of the strains obey the susceptible–infected–recovered (SIR) mechanism. From a special perspective, we first study the dynamical behaviour of an SIR model closed by the PGF, and obtain the basic reproduction number via two methods. Then we build a model to study the spreading dynamics of competing viruses and discuss the conditions for the local stability of equilibria, which is different from the condition obtained by using the heterogeneous mean-field approach. Finally, we perform numerical simulations on Barabási–Albert networks to complement our theoretical research, and show some dynamical properties of the model with competing viruses. This article is part of the themed issue ‘Mathematical methods in medicine: neuroscience, cardiology and pathology’. The Royal Society Publishing 2017-06-28 2017-05-15 /pmc/articles/PMC5434075/ /pubmed/28507229 http://dx.doi.org/10.1098/rsta.2016.0284 Text en © 2017 The Author(s) http://creativecommons.org/licenses/by/4.0/ Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
spellingShingle Articles
Chen, Shanshan
Wang, Kaihua
Sun, Mengfeng
Fu, Xinchu
Spread of competing viruses on heterogeneous networks
title Spread of competing viruses on heterogeneous networks
title_full Spread of competing viruses on heterogeneous networks
title_fullStr Spread of competing viruses on heterogeneous networks
title_full_unstemmed Spread of competing viruses on heterogeneous networks
title_short Spread of competing viruses on heterogeneous networks
title_sort spread of competing viruses on heterogeneous networks
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5434075/
https://www.ncbi.nlm.nih.gov/pubmed/28507229
http://dx.doi.org/10.1098/rsta.2016.0284
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