  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.
