Fractional derivative has a memory and non-localization features that make it very useful in modelling epidemics’ transition. The kernel of Caputo-Fabrizio fractional derivative has many features such as non-singularity, non-locality and an exponential form. Therefore, it is preferred for modeling disease spreading systems. In this work, we suggest to formulate COVID-19 epidemic transmission via ${\mathit{SEIAS}}_q{E}_q\mathit{HR}$ paradigm using the Caputo-Fabrizio fractional derivation method. In the suggested fractional order COVID-19 ${\mathit{SEIAS}}_q{E}_q\mathit{HR}$ paradigm, the impact of changing quarantining and contact rates are examined. The stability of the proposed fractional order COVID-19 ${\mathit{SEIAS}}_q{E}_q\mathit{HR}$ paradigm is studied and a parametric rule for the fundamental reproduction number formula is given. The existence and uniqueness of stable solution of the proposed fractional order COVID-19 ${\mathit{SEIAS}}_q{E}_q\mathit{HR}$ paradigm are proved. Since the genetic algorithm is a common powerful optimization method, we propose an optimum control strategy based on the genetic algorithm. By this strategy, the peak values of the infected population classes are to be minimized. The results show that the proposed fractional model is epidemiologically well-posed and is a proper elect.

小数导数具有记忆和非本地化功能，因此在模拟流行病的过渡过程中非常有用。Caputo-Fabrizio分数阶导数的核具有许多特性，例如非奇异性，非局部性和指数形式。因此，优选用于对疾病传播系统进行建模。在这项工作中，我们建议通过以下方式制定COVID-19流行病传播途径：${\mathit{东南亚}}_q{Ë}_q\mathit{人力资源}$使用Caputo-Fabrizio分数导数方法的范例。以建议的分数顺序COVID-19${\mathit{东南亚}}_q{Ë}_q\mathit{人力资源}$在范式中，检查了隔离和联系率变化的影响。拟议的分数阶COVID-19的稳定性${\mathit{东南亚}}_q{Ë}_q\mathit{人力资源}$研究了范式，给出了基本再现数公式的参数规则。提出的分数阶COVID-19稳定解的存在性和唯一性${\mathit{东南亚}}_q{Ë}_q\mathit{人力资源}$范式被证明。由于遗传算法是一种常用的强大优化方法，因此我们提出了一种基于遗传算法的最优控制策略。通过这种策略，被感染的人群类别的峰值将被最小化。结果表明，所提出的分数模型在流行病学上具有良好的定位，并且是适当的选择。