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模拟推特在减少和增加流感疫情传播方面的影响。

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

作者信息

Huo Hai-Feng, Zhang Xiang-Ming

机构信息

Department of Applied Mathematics, Lanzhou University of Technology, Lanzhou, 730050 Gansu People's Republic of China.

出版信息

Springerplus. 2016 Jan 27;5:88. doi: 10.1186/s40064-016-1689-4. eCollection 2016.

Abstract

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.

摘要

引入了一个更现实的数学流感模型,该模型包含推特的动态变化,推特动态可能会减少或增加流感的传播。推导了基本再生数并证明了稳态的稳定性。通过分析相关特征方程,还证明了霍普夫分岔的存在。此外,还进行了数值模拟和相关参数的敏感性分析。我们的结果表明,推特负面信息对流感的影响并不比推特正面信息对流感的影响显著,而推特负面信息对流感病毒的影响仍然很大。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5de2/4729764/87ad60a27aa5/40064_2016_1689_Fig1_HTML.jpg

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