Modeling Effect of Lockdowns and Other Effects on India Covid-19 Infections Using SEIR Model and Machine Learning
This work addresses the need for more accurate epidemiological predictions in India during the COVID-19 pandemic, but it is incremental as it builds on the widely used SEIR model.
The authors tackled the problem of predicting COVID-19 infections in India by modifying the SEIR model to incorporate effects like lockdowns, vaccines, and re-infections, and they reported that their modified model accurately fits the available infection data.
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.