The COVID-19 pandemic has affected worldwide with unprecedented catastrophes. Susceptible-Infected-Recovered-Death (SIRD) model is a well-known mathematical model to replicate the illness epidemic. Estimation of the epidemiological parameters of the SIRD model is crucial for understanding the virus’s transmission and effect of the virus, thus, helping in making informed decisions about the required interventions. In this study, we propose a Metropolis-Hastings algorithm of the Markov Chain Monte Carlo (MCMC) method to estimate the epidemiological parameters of infectious rate, fatality rate, recovery rate, and reproduction numbers. An analysis is performed to investigate how the parameter changes throughout the lifespan of the pandemic. Numerical results show that the Metropolis-Hastings algorithm can adequately estimate the parameters of the COVID-19 pandemic, providing valuable insights into the spread of the virus and the changes in the pandemic behavior over time.

Topics
Mathematical modeling, Monte Carlo methods, Coronaviruses
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