Modeling Effect of Lockdowns and Other Effects on India Covid-19 Infections Using SEIR Model and Machine Learning

TL;DR

This paper enhances the SEIR epidemiological model by incorporating effects of lockdowns and other factors, fitting it to India's Covid-19 data to improve infection predictions.

Contribution

It introduces a modified SEIR model that accounts for lockdowns and other influences, improving fit to real-world Covid-19 data in India.

Findings

Modified SEIR model accurately fits India's Covid-19 infection data

Incorporating lockdown effects improves model predictions

Model can be adapted for other influencing factors

Abstract

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.