Adomian分解法在新型冠状病毒疫苗防控中的应用

Somaya Saad Faisal
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摘要

摘要:新冠肺炎疫情是一场大范围、跨国界、影响人群的流行病。只有少数几次大流行导致一小部分人或整个社区出现严重疾病。该病毒主要影响老年人。导致Covid-19的病毒主要通过感染者呼气、打喷嚏和咳嗽时产生的飞沫传播。这些症状太严重了;悬在空中,然后迅速地落到地面或地板上。包括疫苗接种运动在内的COVID-19大流行模型是一种自然现象,可以表示为一阶微分方程系统;数学模型包括一个由多个二阶非线性方程组成的系统。我们将Adomian分解方法应用于新冠肺炎的数学模型。该方法的主要优点是可以直接应用于各种线性和非线性微分方程,无论是齐次的还是非齐次的,常系数的还是变系数的。冠状病毒模型各室室的导数在t≥0时连续。模型的解是非负性的。它表明,感染,将逐渐流行和消失,将停止。如果,R_0>1,表示每个受影响个体的平均值。不止一人感染,感染的发生率在皱纹。这意味着疫情不会结束,同时保持疾病的存在,R_0=1意味着每个感染患者导致的平均感染。
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Applying Adomian Decomposition Method for solving the Covid-19 epidemic with Vaccine
Abstract— A pandemic Covid-19 is an epidemic that spreads over a big region, Crosse international borders, and often affects a lot of people. Only a few pandemics result in severe illness in a subset of people or in an entire community. The virus has mainly affects the elderly population. The virus, that causes Covid-19, has mainly been transmitted through droplet generate once an infected persons exhales, sneezes and coughs. These symptoms are too heavy; to hang in air, and quickly, fall on surface or floor. The COVID-19 pandemic model including the Vaccination Campaign is of natural phenomenon which can be represented as a system of differential equations for the first order; the mathematical models include a system of several second order nonlinear equations. We applied the Adomian decomposition methods to the mathematical models of Covid-19. The main advantage of this method is that it can be directly applied to all kinds of linear and nonlinear differential equations, homogeneous or nonhomogeneous, with constant or variable coefficients. The derivatives of all compartments of the coronavirus model are continuous at t ≥ 0. The solutions of the model are non-negativity. It indicates that the, infection, will be gradually the epidemic and disappear will, stop. If, R_0>1, the average of each affected individually. More than one person has infected, and the incidence of infection is in wrinkles. That means the epidemic, will not be end, while maintain the existence of the disease, the R_0=1 means that each infected patient results in an average infections.
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