  The Covid-19 outbreak of 2020 has required many governments to develop and
adopt mathematical-statistical models of the pandemic for policy and planning
purposes. To this end, this work provides a tutorial on building a
compartmental model using Susceptible, Exposed, Infected, Recovered, Deaths and
Vaccinated (SEIRDV) status through time. The proposed model uses interventions
to quantify the impact of various government attempts made to slow the spread
of the virus. Furthermore, a vaccination parameter is also incorporated in the
model, which is inactive until the time the vaccine is deployed. A Bayesian
framework is utilized to perform both parameter estimation and prediction.
Predictions are made to determine when the peak Active Infections occur. We
provide inferential frameworks for assessing the effects of government
interventions on the dynamic progression of the pandemic, including the impact
of vaccination. The proposed model also allows for quantification of number of
excess deaths averted over the study period due to vaccination.
