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分析具有分数阶算子的复杂非线性新冠模型的混合 NAR-RBF 网络。

Analysis of Hybrid NAR-RBFs Networks for complex non-linear Covid-19 model with fractional operators.

机构信息

Department of Mathematics, Ghazi University, DG Khan, 32200, Pakistan.

Department of Mathematics, Khawaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan.

出版信息

BMC Infect Dis. 2024 Sep 27;24(1):1051. doi: 10.1186/s12879-024-09329-6.

Abstract

The Hybrid NAR-RBFs Networks for COVID-19 fractional order model is examined in this scientific study. Hybrid NAR-RBFs Networks for COVID-19, that is more infectious which is appearing in numerous areas as people strive to stop the COVID-19 pandemic. It is crucial to figure out how to create strategies that would stop the spread of COVID-19 with a different age groups. We used the epidemic scenario in the Hybrid NAR-RBFs Networks as a case study in order to replicate the propagation of the modified COVID-19. In this research work, existence and stability are verified for COVID-19 as well as proved unique solutions by applying some results of fixed point theory. The developed approach to investigate the impact of Hybrid NAR-RBFs Networks due to COVID-19 at different age groups is relatively advanced. Also obtain solutions for a proposed model by utilizing Atanga Toufik technique and fractal fractional which are the advanced techniques for such type of infectious problems for continuous monitoring of spread of COVID-19 in different age groups. Comparisons has been made to check the efficiency of techniques as well as for finding the reliable solutions to understand the dynamical behavior of Hybrid NAR-RBFs Networks for non-linear COVID-19. Finally, the parameters are evaluated to see the impact of illness and present numerical simulations using Matlab to see actual behavior of this infectious disease for Hybrid NAR-RBFs Networks of COVID-19 for different age groups.

摘要

本研究考察了用于 COVID-19 分数阶模型的混合 NAR-RBFs 网络。COVID-19 的混合 NAR-RBFs 网络,这种病毒在人们努力阻止 COVID-19 大流行的过程中,在许多地区更加猖獗。弄清楚如何制定策略来阻止 COVID-19 在不同年龄段的传播是至关重要的。我们使用混合 NAR-RBFs 网络中的疫情情景作为案例研究,以复制修改后的 COVID-19 的传播。在这项研究工作中,应用不动点理论的一些结果验证了 COVID-19 的存在和稳定性,并证明了唯一解。提出的方法用于研究 COVID-19 对不同年龄段混合 NAR-RBFs 网络的影响,相对先进。还通过利用 Atanga Toufik 技术和分形分数获得了所提出模型的解,这些技术是针对此类传染病问题的先进技术,用于连续监测 COVID-19 在不同年龄段的传播。进行了比较以检查技术的效率,并找到可靠的解决方案,以了解 COVID-19 混合 NAR-RBFs 网络的非线性动力学行为。最后,评估参数以了解疾病的影响,并使用 Matlab 进行实际数值模拟,以观察 COVID-19 混合 NAR-RBFs 网络中这种传染病的实际行为。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3fc/11430756/654fe225bdd1/12879_2024_9329_Fig1_HTML.jpg

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