具有最优控制的Atangana-Baleanu分数阶SEAIR流行病模型分析。

IF 4.1 3区 数学 Q1 Mathematics
Advances in Difference Equations Pub Date : 2021-01-01 Epub Date: 2021-03-19 DOI:10.1186/s13662-021-03334-8
Chernet Tuge Deressa, Gemechis File Duressa
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引用次数: 1

摘要

考虑具有Atangana-Baleanu分数阶导数的SEAIR流行病模型。我们使用Toufic和Atangana开发的数值格式近似求解模型。对几个分数阶对应的数值模拟表明,分数阶从1开始减小,地方病的传播速度变慢。最优控制分析和仿真结果表明,所设计的控制策略在减少不同车厢的车次数量方面是有效的。此外,对最优剖面的模拟表明,将分数阶从1降低,需要在尽可能高的水平上快速启动所设计的控制策略的应用,并在大流行期间的大部分时间内保持这种策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Analysis of Atangana-Baleanu fractional-order SEAIR epidemic model with optimal control.

Analysis of Atangana-Baleanu fractional-order SEAIR epidemic model with optimal control.

Analysis of Atangana-Baleanu fractional-order SEAIR epidemic model with optimal control.

Analysis of Atangana-Baleanu fractional-order SEAIR epidemic model with optimal control.

We consider a SEAIR epidemic model with Atangana-Baleanu fractional-order derivative. We approximate the solution of the model using the numerical scheme developed by Toufic and Atangana. The numerical simulation corresponding to several fractional orders shows that, as the fractional order reduces from 1, the spread of the endemic grows slower. Optimal control analysis and simulation show that the control strategy designed is operative in reducing the number of cases in different compartments. Moreover, simulating the optimal profile revealed that reducing the fractional-order from 1 leads to the need for quick starting of the application of the designed control strategy at the maximum possible level and maintaining it for the majority of the period of the pandemic.

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来源期刊
自引率
0.00%
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0
审稿时长
4-8 weeks
期刊介绍: The theory of difference equations, the methods used, and their wide applications have advanced beyond their adolescent stage to occupy a central position in applicable analysis. In fact, in the last 15 years, the proliferation of the subject has been witnessed by hundreds of research articles, several monographs, many international conferences, and numerous special sessions. The theory of differential and difference equations forms two extreme representations of real world problems. For example, a simple population model when represented as a differential equation shows the good behavior of solutions whereas the corresponding discrete analogue shows the chaotic behavior. The actual behavior of the population is somewhere in between. The aim of Advances in Difference Equations is to report mainly the new developments in the field of difference equations, and their applications in all fields. We will also consider research articles emphasizing the qualitative behavior of solutions of ordinary, partial, delay, fractional, abstract, stochastic, fuzzy, and set-valued differential equations. Advances in Difference Equations will accept high-quality articles containing original research results and survey articles of exceptional merit.
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