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新型冠状病毒感染治疗动态的数学评估:一项分数阶研究。

Mathematical assessment of the dynamics of novel coronavirus infection with treatment: A fractional study.

作者信息

Liu Xuan, Ullah Saif, Alshehri Ahmed, Altanji Mohamed

机构信息

Department of Mathematics, Hanshan Normal University, Chaozhou 515041 China.

Department of Mathematics, University of Peshawar, Khyber Pakhtunkhwa, Pakistan.

出版信息

Chaos Solitons Fractals. 2021 Dec;153:111534. doi: 10.1016/j.chaos.2021.111534. Epub 2021 Nov 4.

Abstract

In this paper, a mathematical model is formulated to study the transmission dynamics of the novel coronavirus infection under the effect of treatment. The compartmental model is firstly formulated using a system of nonlinear ordinary differential equations. Then, with the help of Caputo operator, the model is reformulated in order to obtain deeper insights into disease dynamics. The basic mathematical features of the time fractional model are rigorously presented. The nonlinear least square procedure is implemented in order to parameterize the model using COVID-19 cumulative cases in Saudi Arabia for the selected time period. The important threshold parameter called the basic reproduction number is evaluated based on the estimated parameters and is found . The fractional Lyapunov approach is used to prove the global stability of the model around the disease free equilibrium point. Moreover, the model in Caputo sense is solved numerically via an efficient numerical scheme known as the fractional Adamas-Bashforth-Molten approach. Finally, the model is simulated to present the graphical impact of memory index and various intervention strategies such as social-distancing, disinfection of the virus from environment and treatment rate on the pandemic peaks. This study emphasizes the important role of various scenarios in these intervention strategies in curtailing the burden of COVID-19.

摘要

在本文中,建立了一个数学模型来研究在治疗作用下新型冠状病毒感染的传播动力学。首先使用非线性常微分方程组建立了 compartmental 模型。然后,借助 Caputo 算子对模型进行重新构建,以便更深入地洞察疾病动态。严格呈现了时间分数阶模型的基本数学特征。实施非线性最小二乘法,以便使用沙特阿拉伯在选定时间段内的 COVID - 19 累计病例对模型进行参数化。基于估计参数评估了称为基本再生数的重要阈值参数,并得出结果。使用分数阶 Lyapunov 方法证明了模型在无病平衡点附近的全局稳定性。此外,通过一种称为分数阶 Adamas - Bashforth - Molten 方法的高效数值格式对 Caputo 意义下的模型进行数值求解。最后,对模型进行模拟,以呈现记忆指数和各种干预策略(如社交距离、从环境中消除病毒和治疗率)对疫情高峰的图形影响。本研究强调了这些干预策略中各种情景在减轻 COVID - 19 负担方面的重要作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6246/8566449/cfc13c79275f/gr1_lrg.jpg

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