Global stability and optimal control of an age-structured SVEIR epidemic model with waning immunity and relapses.

IF 2.2 4区 数学 Q2 BIOLOGY Journal of Mathematical Biology Pub Date : 2024-07-22 DOI:10.1007/s00285-024-02131-7
Shuanghong Ma, Tian Tian, Haifeng Huo
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Abstract

The efficacy of vaccination, incomplete treatment and disease relapse are critical challenges that must be faced to prevent and control the spread of infectious diseases. Age heterogeneity is also a crucial factor for this study. In this paper, we investigate a new age-structured SVEIR epidemic model with the nonlinear incidence rate, waning immunity, incomplete treatment and relapse. Next, the asymptotic smoothness, the uniform persistence and the existence of interior global attractor of the solution semi-flow generated by the system are given. We define the basic reproduction number R 0 and prove the existence of the equilibria of the model. And we study the global asymptotic stability of the equilibria. Then the parameters of the model are estimated using tuberculosis data in China. The sensitivity analysis of R 0 is derived by the Partial Rank Correlation Coefficient method. These main theoretical results are applied to analyze and predict the trend of tuberculosis prevalence in China. Finally, the optimal control problem of the model is discussed. We choose to take strengthening treatment and controlling relapse as the control parameters. The necessary condition for optimal control is established.

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具有免疫力减退和复发的年龄结构 SVEIR 流行病模型的全局稳定性和最优控制。
疫苗接种效果、治疗不彻底和疾病复发是预防和控制传染病传播必须面对的严峻挑战。年龄异质性也是这一研究的关键因素。在本文中,我们研究了一种新的年龄结构 SVEIR 流行病模型,该模型具有非线性发病率、免疫力减弱、不完全治疗和复发。接着,给出了该系统产生的解半流的渐近平稳性、均匀持久性和内部全局吸引子的存在性。我们定义了基本繁殖数 R 0,并证明了模型均衡的存在性。我们还研究了均衡点的全局渐近稳定性。然后利用中国结核病数据对模型参数进行了估计。通过偏等级相关系数法得出 R 0 的敏感性分析。将这些主要理论结果应用于分析和预测中国结核病流行趋势。最后,讨论了模型的最优控制问题。我们选择加强治疗和控制复发作为控制参数。建立了最优控制的必要条件。
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来源期刊
CiteScore
3.30
自引率
5.30%
发文量
120
审稿时长
6 months
期刊介绍: The Journal of Mathematical Biology focuses on mathematical biology - work that uses mathematical approaches to gain biological understanding or explain biological phenomena. Areas of biology covered include, but are not restricted to, cell biology, physiology, development, neurobiology, genetics and population genetics, population biology, ecology, behavioural biology, evolution, epidemiology, immunology, molecular biology, biofluids, DNA and protein structure and function. All mathematical approaches including computational and visualization approaches are appropriate.
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