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新型冠状病毒肺炎动态的数学建模与最优控制

Mathematical modeling and optimal control of the COVID-19 dynamics.

作者信息

Shen Zhong-Hua, Chu Yu-Ming, Khan Muhammad Altaf, Muhammad Shabbir, Al-Hartomy Omar A, Higazy M

机构信息

School of Mathematics, Hangzhou Normal University, Hangzhou 311121, PR China.

Department of Mathematics, Huzhou University, Huzhou 313000, PR China.

出版信息

Results Phys. 2021 Dec;31:105028. doi: 10.1016/j.rinp.2021.105028. Epub 2021 Nov 27.

Abstract

We are considering a new COVID-19 model with an optimal control analysis when vaccination is present. Firstly, we formulate the vaccine-free model and present the associated mathematical results involved. Stability results for are shown. In addition, we frame the model with the vaccination class. We look at the mathematical results with the details of the vaccine model. Additionally, we are considering setting controls to minimize infection spread and control. We consider four different controls, such as prevention, vaccination control, rapid screening of people in the exposed category, and people who are identified as infected without screening. Using the suggested controls, we develop an optimal control model and derive mathematical results from it. In addition, the mathematical model with control and without control is resolved by the forward-backward Runge-Kutta method and presents the results graphically. The results obtained through optimal control suggest that controls can be useful for minimizing infected individuals and improving population health.

摘要

我们正在考虑一个新的新冠病毒模型,并在有疫苗接种的情况下进行最优控制分析。首先,我们构建了无疫苗模型并给出了相关的数学结果。展示了 的稳定性结果。此外,我们将疫苗接种类别纳入模型。我们研究了疫苗模型细节下的数学结果。另外,我们正在考虑设置控制措施以尽量减少感染传播和控制。我们考虑了四种不同的控制措施,如预防、疫苗接种控制、对暴露人群进行快速筛查以及对未筛查出的感染者进行识别。使用建议的控制措施,我们开发了一个最优控制模型并从中推导出数学结果。此外,通过向前向后龙格 - 库塔方法求解了有控制和无控制的数学模型,并以图形方式展示结果。通过最优控制获得的结果表明,控制措施有助于减少感染个体数量并改善人群健康。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fddf/8627298/06689117b04d/gr1_lrg.jpg

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