This paper deals with a general SEIR model for the coronavirus disease 2019 (COVID-19) with the effect of time delay proposed. We get the stability theorems for the disease-free equilibrium and provide adequate situations of the COVID-19 transmission dynamics equilibrium of present and absent cases. A Hopf bifurcation parameter τ concerns the effects of time delay and we demonstrate that the locally asymptotic stability holds for the present equilibrium. The reproduction number is brief in less than or greater than one, and it effectively is controlling the COVID-19 infection outbreak and subsequently reveals insight into understanding the patterns of the flare-up. We have included eight parameters and the least square method allows us to estimate the initial values for the Indian COVID-19 pandemic from real-life data. It is one of India’s current pandemic models that have been studied for the time being. This Covid19 SEIR model can apply with or without delay to all country’s current pandemic region, after estimating parameter values from their data. The sensitivity of seven parameters has also been explored. The paper also examines the impact of immune response time delay and the importance of determining essential parameters such as the transmission rate using sensitivity indices analysis. The numerical experiment is calculated to illustrate the theoretical results.

本文讨论了2019年冠状病毒疾病的一般SEIR模型（COVID-19），并提出了时延的影响。我们获得了无病平衡的稳定性定理，并提供了当前和缺席病例的COVID-19传播动力学平衡的适当情况。Hopf分叉参数τ关于时间延迟的影响，我们证明了局部渐近稳定性对于当前平衡成立。复制数量少于或大于1是短暂的，并且有效地控制了COVID-19感染的爆发，随后揭示了对了解爆发模式的了解。我们已经包括了八个参数，最小二乘法使我们能够从现实数据中估算出印度COVID-19大流行的初始值。它是印度目前正在研究的当前大流行模型之一。从其数据估算参数值之后，此Covid19 SEIR模型可以无延迟地应用于所有国家当前的大流行区域。还探讨了七个参数的敏感性。本文还研究了免疫反应时间延迟的影响以及使用敏感性指数分析确定基本参数（如传播率）的重要性。计算结果表明了理论结果。