具有媒体效应时滞的SEIRS-M传染病模型的动力学分析
Dynamic Analysis of SEIRS-M Epidemic Mod-el with Media Effect Delay
DOI: 10.12677/AAM.2023.1210427, PDF,    国家自然科学基金支持
作者: 秦 秀, 鲁延玲*, 黄丽霞:南京邮电大学理学院,江苏 南京
关键词: 传染病模型时滞媒体效应稳定性分析Hopf分叉Infectious Disease Model Time Delay Media Effect Stability Analysis Hopf Bifurcation
摘要: 为了研究在突发疫情中媒体信息对病毒传播的影响,本文建立了包含正负面信息影响的SEIRS-M模型,并且在模型中加入了一个媒体效应时滞。理论部分证明了模型解的非负性,得到了基本再生数和平衡点的表达式,分析了模型的稳定性以及Hopf分叉的存在性。最后通过数值模拟验证了理论结果,参数敏感性分析表明时滞越小疫情传播规模就越小,不同的信息贯彻率对疫情传播速度也有很大影响。因此,媒体应该在及时发布疫情相关信息的同时保障信息的质量,为群众提供有价值的信息从而增强人们的疾病预防意识,减少疫情传播规模。
Abstract: In order to study the influence of media information on virus transmission in an outbreak, a SEIRS- M model containing positive and negative information is established in this paper, and a media ef-fect delay is added to the model. In the theoretical part, the non-negative solution of the model is proved, the expressions of the basic regeneration number and the equilibrium point are obtained, and the stability of the model and the existence of Hopf bifurcation are analyzed. Finally, the theo-retical results were verified by numerical simulation. The parameter sensitivity analysis showed that the smaller the time delay, the smaller the epidemic transmission scale, and different infor-mation implementation rates also had a great impact on the epidemic transmission speed. There-fore, the media should release epidemy-related information in a timely manner and ensure the quality of information, so as to provide valuable information for the masses, so as to enhance peo-ple’s awareness of disease prevention and reduce the scale of epidemic transmission.
文章引用:秦秀, 鲁延玲, 黄丽霞. 具有媒体效应时滞的SEIRS-M传染病模型的动力学分析[J]. 应用数学进展, 2023, 12(10): 4338-4349. https://doi.org/10.12677/AAM.2023.1210427

参考文献

[1] World Health Statistics (2022).
https://www.who.int/publications/i/item/9789240051157
[2] Greenhalgh, D., Ra-na, S., Samanta, S., Sardar, T., Bhattacharya, S. and Chattopadhyay, J. (2015) Awareness Programs Control Infectious Disease-Multiple Delay Induced Mathematical Model. Applied Mathematics and Computation, 251, 539-563. [Google Scholar] [CrossRef
[3] Huo, H.F., Yang, P. and Xiang H. (2018) Stability and Bifurcation for an SEIS Epidemic Model with the Impact of Media. Physica A: Statistical Mechanics and Its Applications, 490, 702-720. [Google Scholar] [CrossRef
[4] Zhou, W., Wang, A., Xia, F., Xiao, Y. and Tang, S. (2020) Ef-fects of Media Reporting on Mitigating Spread of COVID-19 in the Early Phase of the Outbreak. Mathematical Biosci-ences and Engineering, 17, 2693-2707. [Google Scholar] [CrossRef] [PubMed]
[5] 常星花. 疾病信息与传染病共演化动力学性态分析与研究[D]: [博士学位论文]. 太原: 中北大学, 2022.
[6] Xiao, Y. and Zhao, T. (2013) Dynamics of an Infectious Diseases with Media/Psychology Induced Non-Smooth Incidence. Mathematical Biosciences and Engineering, 10, 445-461. [Google Scholar] [CrossRef] [PubMed]
[7] Gao, D. and Ruan, S. (2011) An SIS Patch Model with Variable Transmission Coefficients. Mathematical Biosciences, 232, 110-115. [Google Scholar] [CrossRef] [PubMed]
[8] 张丽娟, 王福昌, 赵宜宾, 等. 具有媒体效应和时滞的反应扩散传染病模型的控制与预测[J]. 数学的实践与认识, 2019, 49(10): 234-243.
[9] Cheng, X., Wang, Y. and Huang, G. (2021) Global Dynamics of a Network-Based SIQS Epidemic Model with Nonmonotone Incidence Rate. Chaos, Solitons and Fractals, 153, Article ID: 111502. [Google Scholar] [CrossRef] [PubMed]
[10] Driessche, P. and Watmough, J. (2002) Reproduction Numbers and Sub-Threshold Endemic Equilibria for Compartmental Models of Disease Transmission. Mathematical Biosciences, 180, 29-48. [Google Scholar] [CrossRef
[11] Teklu, S.W. and Terefe, B.B. (2022) Mathematical Modeling Analysis on the Dynamics of University Students’ Animosity towards Mathematics with Optimal Control Theory. Scien-tific Reports, 12, Article No. 11578. [Google Scholar] [CrossRef] [PubMed]