混合变阶分数型冠状病毒(2019-nCov)数学模型的最优控制数值的治疗方法

IF 3.1 3区 环境科学与生态学 Q2 ECOLOGY Ecological Complexity Pub Date : 2022-03-01 DOI:10.1016/j.ecocom.2022.100983
N.H. Sweilam , S.M. AL-Mekhlafi , T.M. Al-Ajami
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引用次数: 3

摘要

新型冠状病毒是一个严重的全球问题,对埃及的经济产生了负面影响。根据公开报告的数据,埃及于2020年2月14日报告了首例新型冠状病毒病例。从大流行开始到8月18日,埃及共记录了96753例病例,其中96,581人是埃及人,172人是外国人。最近,人们认为许多数学模型可以更好地理解冠状病毒感染。这些模型大多是基于经典的整阶导数,不能捕捉到许多生物现象中的衰落记忆和交叉行为。因此,我们利用新的变阶分数阶导数来探索COVID-19感染的动态,以此来研究冠状病毒病。提出了冠状病毒变阶分数阶混合模型的最优控制问题。通过一个辅助参数对变阶分数算子进行修改,以满足所得到的变阶分数方程两边的维数匹配。证明了解的存在性、唯一性、有界性、正性、局部稳定性和全局稳定性。考虑两个控制变量来减少感染向健康人的传播。为了逼近新的混合变阶算子,采用了gr nwald- letnikov近似。采用混合变阶算子有限差分法和广义四阶龙格-库塔法求解最优性系统。为验证所采用方法的适用性和说明这些近似方法的简洁性,给出了数值算例和对比研究。此外,利用所提出的方法,我们可以得出结论,本文给出的模型很好地描述了世界卫生组织关于埃及的确认真实数据。
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Optimal control of hybrid variable-order fractional coronavirus (2019-nCov) mathematical model; numerical treatments

A novel coronavirus is a serious global issue and has a negative impact on the economy of Egypt. According to the publicly reported data, the first case of the novel corona virus in Egypt was reported on 14 February 2020. Total of 96753 cases were recorded in Egypt from the beginning of the pandemic until the eighteenth of August, where 96, 581 individuals were Egyptians and 172 were foreigners. Recently, many mathematical models have been considered to better understand coronavirus infection. Most of these models are based on classical integer-order derivatives which can not capture the fading memory and crossover behavior found in many biological phenomena. Therefore, we study the coronavirus disease in this paper by exploring the dynamics of COVID-19 infection using new variable-order fractional derivatives. This paper presents an optimal control problem of the hybrid variable-order fractional model of Coronavirus. The variable-order fractional operator is modified by an auxiliary parameter in order to satisfy the dimensional matching between the both sides of the resultant variable-order fractional equations. Existence, uniqueness, boundedness, positivity, local and global stability of the solutions are proved. Two control variables are considered to reduce the transmission of infection into healthy people. To approximate the new hybrid variable-order operator, Grünwald-Letnikov approximation is used. Finite difference method with a hybrid variable-order operator and generalized fourth order Runge-Kutta method are used to solve the optimality system. Numerical examples and comparative studies for testing the applicability of the utilized methods and to show the simplicity of these approximation approaches are presented. Moreover, by using the proposed methods we can concluded that, the model given in this paper describes well the confirmed real data given by WHO about Egypt.

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来源期刊
Ecological Complexity
Ecological Complexity 环境科学-生态学
CiteScore
7.10
自引率
0.00%
发文量
24
审稿时长
3 months
期刊介绍: Ecological Complexity is an international journal devoted to the publication of high quality, peer-reviewed articles on all aspects of biocomplexity in the environment, theoretical ecology, and special issues on topics of current interest. The scope of the journal is wide and interdisciplinary with an integrated and quantitative approach. The journal particularly encourages submission of papers that integrate natural and social processes at appropriately broad spatio-temporal scales. Ecological Complexity will publish research into the following areas: • All aspects of biocomplexity in the environment and theoretical ecology • Ecosystems and biospheres as complex adaptive systems • Self-organization of spatially extended ecosystems • Emergent properties and structures of complex ecosystems • Ecological pattern formation in space and time • The role of biophysical constraints and evolutionary attractors on species assemblages • Ecological scaling (scale invariance, scale covariance and across scale dynamics), allometry, and hierarchy theory • Ecological topology and networks • Studies towards an ecology of complex systems • Complex systems approaches for the study of dynamic human-environment interactions • Using knowledge of nonlinear phenomena to better guide policy development for adaptation strategies and mitigation to environmental change • New tools and methods for studying ecological complexity
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