Analysis of COVID19 Outbreak in India using SEIR model

TL;DR

This paper uses a generalized SEIR model to predict COVID-19 outbreak patterns in India, successfully estimating peak timings and case numbers despite demographic and meteorological complexities.

Contribution

It applies a generalized SEIR model to Indian COVID-19 data, achieving close peak predictions and demonstrating its suitability for outbreak analysis.

Findings

Peak prediction matches real data within one week.

Predicted case numbers differ due to policy changes.

Model effectively captures outbreak timing in India.

Abstract

The prediction of spread patterns of COVID19 virus in India is very difficult due to its versatile demographic as well as meteorological data distribution. Various researchers across the globe have attempted to correlate the interdependency of these data with the spread pattern of COVID19 cases in India. But it is hard to predict the exact pattern, especially the peak in the number of active cases. In the present article we have tried to predict the number of active, recovered, death and total cases of COVID19 in India using generalized SEIR model. In our prediction, the occurrence of peak in the active cases curve has a very close match with the peak in the real data (difference of only one week). Although the number of predicted cases differs with the real number of cases (due to unlocking the movement restrictions gradually from June 2020 onwards), the close resemblance in the actual and predicted time (in the peak of active cases curve) makes this model relatively suitable for analysis of COVID19 outbreak in India.