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Incorporating Time Delays in the Mathematical Modelling of the Human Immune Response in Viral Infections

机译:在病毒感染中的人类免疫反应的数学建模中纳入时间延迟

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Mathematical modelling helps to describe the functional and causal relationships between objects in the physical world. The complexity of these models increases as more components and variables are added to maintain and observe. Differential equations are regularly used in these models, as they are able to display the interactions between several variables and describe non-linear behaviour. Differential equations are commonly used in immune response mathematical models to help describe these complex and dynamic interactions within the immune system of the organism. Time delays in the immune system are common and are often disregarded due to the low-resolution of models, which provide limited description of the specific section of immune system being studied. The few models that incorporate time delays are mostly at the epidemiological level, to track the spread of the virus in the population. In this paper we review the applications of the models based on differential equations and describe their potential utilization for the studies of immune response in SARS-CoV-2.
机译:数学建模有助于描述物理世界中对象之间的功能和因果关系。随着更多组件和变量来维护和观察,这些模型的复杂性增加。在这些模型中定期使用微分方程,因为它们能够在若干变量之间显示相互作用并描述非线性行为。微分方程通常用于免疫应答数学模型,以帮助描述生物体免疫系统内的这些复杂和动态相互作用。免疫系统中的时间延迟是常见的,并且由于模型的低分辨率而常被忽视,这提供了对正在研究的免疫系统的特定部分的有限描述。结合时间延迟的少数模型主要在流行病学层面,跟踪病毒在人口中的传播。在本文中,我们审查了基于微分方程的模型的应用,并描述了SARS-COV-2中免疫应答研究的潜在利用。

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