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Modeling the influence of Twitter in reducing and increasing the spread of influenza epidemics

A more realistic mathematical influenza model including dynamics of Twitter, which may reduce and increase the spread of influenza, is introduced. The basic reproductive number is derived and the stability of the steady states is proved. The existence of Hopf bifurcation are also demonstrated by ana...

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Detalles Bibliográficos
Autores principales: Huo, Hai-Feng, Zhang, Xiang-Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4729764/
https://www.ncbi.nlm.nih.gov/pubmed/26848428
http://dx.doi.org/10.1186/s40064-016-1689-4
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author Huo, Hai-Feng
Zhang, Xiang-Ming
author_facet Huo, Hai-Feng
Zhang, Xiang-Ming
author_sort Huo, Hai-Feng
collection PubMed
description A more realistic mathematical influenza model including dynamics of Twitter, which may reduce and increase the spread of influenza, is introduced. The basic reproductive number is derived and the stability of the steady states is proved. The existence of Hopf bifurcation are also demonstrated by analyzing the associated characteristic equation. Furthermore, numerical simulations and sensitivity analysis of relevant parameters are also carried out. Our results show that the impact posed by the negative information of Twitter is not significant than the impact posed by the positive information of Twitter on influenza while the impact posed by the negative information of Twitter on the influenza virus is still extraordinary.
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spelling pubmed-47297642016-02-04 Modeling the influence of Twitter in reducing and increasing the spread of influenza epidemics Huo, Hai-Feng Zhang, Xiang-Ming Springerplus Research A more realistic mathematical influenza model including dynamics of Twitter, which may reduce and increase the spread of influenza, is introduced. The basic reproductive number is derived and the stability of the steady states is proved. The existence of Hopf bifurcation are also demonstrated by analyzing the associated characteristic equation. Furthermore, numerical simulations and sensitivity analysis of relevant parameters are also carried out. Our results show that the impact posed by the negative information of Twitter is not significant than the impact posed by the positive information of Twitter on influenza while the impact posed by the negative information of Twitter on the influenza virus is still extraordinary. Springer International Publishing 2016-01-27 /pmc/articles/PMC4729764/ /pubmed/26848428 http://dx.doi.org/10.1186/s40064-016-1689-4 Text en © Huo and Zhang. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Research
Huo, Hai-Feng
Zhang, Xiang-Ming
Modeling the influence of Twitter in reducing and increasing the spread of influenza epidemics
title Modeling the influence of Twitter in reducing and increasing the spread of influenza epidemics
title_full Modeling the influence of Twitter in reducing and increasing the spread of influenza epidemics
title_fullStr Modeling the influence of Twitter in reducing and increasing the spread of influenza epidemics
title_full_unstemmed Modeling the influence of Twitter in reducing and increasing the spread of influenza epidemics
title_short Modeling the influence of Twitter in reducing and increasing the spread of influenza epidemics
title_sort modeling the influence of twitter in reducing and increasing the spread of influenza epidemics
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4729764/
https://www.ncbi.nlm.nih.gov/pubmed/26848428
http://dx.doi.org/10.1186/s40064-016-1689-4
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