The course of an infection was modeled as a controlled nonlinear process. Understanding the substantial differences observed in the trajectory of the disease caused by the new coronavirus SARS-CoV-2 is of critical importance at the moment. Numerous factors have been considered to explain the fact that symptoms vary highly among different people and the infection transmission rate varies among local populations. Virus replication within the host cell and the development of an immune response to virus antigens in the body are two interdependent processes, which have aftereffects and depend on the preexisting states of the cell and virus populations. Different scenarios with the same input parameters are necessary to consider for modeling the properties of the states. The efficiency of the immune response is the most important factor, including the time it takes to develop defense responses from three levels of the immune system, which is a complex system of the body. A computational description of infection scenarios was proposed on the basis of a delay differential equation with two values of the time lag. In the new model, transitions between phases of infectious disease depend on the initial virus dose and the delayed immune response to infection. A variation in the dose of the virus and response time can lead to a transition from an acute phase of the disease with overt symptoms to a chronic phase or fatal outcome. Asymptomatic transmission of viral infection was calculated and described in the model as a situation where the virus is rapidly and efficiently suppressed after a short replication phase, while still persisting in the body in minor amounts. An analysis of the model behavior is consistent with the theory that the initial number of virions can affect the quality of the immune response. The reasons that high individual differences are observed in the trajectory of COVID-19 disease and the formation of types of the immune response to coronavirus are still poorly understood. Known trajectories of hepatitis C virus (HCV) infection were used as a basis for model scenarios.

感染过程被建模为受控的非线性过程。了解新型冠状病毒 SARS-CoV-2 引起的疾病发展轨迹中观察到的实质性差异目前至关重要。人们考虑了多种因素来解释不同人之间的症状差异很大以及当地人群之间的感染传播率不同的事实。病毒在宿主细胞内的复制和体内对病毒抗原的免疫反应的发展是两个相互依赖的过程，它们会产生后遗症，并取决于细胞和病毒群体预先存在的状态。对状态属性进行建模时，需要考虑具有相同输入参数的不同场景。免疫反应的效率是最重要的因素，包括从免疫系统的三个层面产生防御反应所需的时间，免疫系统是一个复杂的身体系统。基于具有两个时滞值的延迟微分方程，提出了感染场景的计算描述。在新模型中，传染病阶段之间的转变取决于初始病毒剂量和对感染的延迟免疫反应。病毒剂量和反应时间的变化可能导致疾病从具有明显症状的急性期转变为慢性期或致命结果。病毒感染的无症状传播在模型中被计算和描述为一种情况，即病毒在短暂的复制阶段后被快速有效地抑制，同时仍以少量存在于体内。 对模型行为的分析与病毒粒子的初始数量可以影响免疫反应质量的理论是一致的。人们对 COVID-19 疾病轨迹和冠状病毒免疫反应类型形成中观察到的高度个体差异的原因仍知之甚少。已知的丙型肝炎病毒（HCV）感染轨迹被用作模型场景的基础。