Author(s): Manisha Mandal
The models on disease transmission are useful in planning decisions on pandemic, resource allocation and implementation of non-pharmaceutical intervention. The SEIR differs from SIR model with an additional exposure period due to the incubation period of COVID-19 during which individuals are not yet infectious. I have applied Bayesian approach with Monte Carlo Markov Chain (MCMC) sampling on SEIR and SIR epidemiological models using python code PymC3 to study the dynamics of COVID-19 pandemic in India, assess the effectiveness of non-pharmaceutical measures from March to October 2020, and generate predictions on daily new and cumulative infected cases. The accuracy of prediction was computed by symmetric mean absolute prediction error (SMAPE) and mean squared relative prediction error (MSRPE
