The SEIR model is a widely used epidemiological model used to predict the rise in infections. This model has been widely used in different countries to predict the number of Covid-19 cases. But the original SEIR model does not take into account the effect of factors such as lockdowns, vaccines, and re-infections. In India the first wave of Covid started in March 2020 and the second wave in April 2021. In this paper, we modify the SEIR model equations to model the effect of lockdowns and other influencers, and fit the model on data of the daily Covid-19 infections in India using lmfit, a python library for least squares minimization for curve fitting. We modify R0 parameter in the standard SEIR model as a rectangle in order to account for the effect of lockdowns. Our modified SEIR model accurately fits the available data of infections.

翻译：SEIR模型是一种广泛使用的流行病学模型,用于预测感染病例的增加,这一模型在不同国家被广泛用于预测Covid-19病例的数量。但最初的SEIR模型没有考虑到锁定、疫苗和再感染等因素的影响。在印度,Covid第一波始于2020年3月,第二波于2021年4月。在本文中,我们修改了SEIR模型方程式,以模拟锁定和其他影响者的影响,并适合印度Covid-19传染病日报数据模型,该模型是用于尽量减少曲线安装的平方字库。我们修改了标准SEIR模型中的R0参数,作为计算锁定效应的矩形。我们修改过的SEIR模型准确符合现有感染数据。