This project aims to study the parameters of the Deterministic SIR(Susceptible → Infected → Recovered) model of
COVID-19 in a Bayesian MCMC framework. Several deterministic mathematical models are being developed everyday to
forecast the spread of COVID-19 correctly. Here, I have tried to model and study the parameters of the SIR Infectious disease
model using the Bayesian Framework and Markov-Chain Monte-Carlo (MCMC) techniques. I have used Bayesian Inference to
predict the Basic Reproductive Rate ࢚ࡾ
in real time using and following this, demonstrated the process of how the parameters of
the SIR Model can be estimated using Bayesian Statistics and Markov-Chain Monte-Carlo Methods.
