When a pandemic occurs, it can cost fatal damages to human life. Therefore, it is important to understand the dynamics of a global pandemic in order to find a way of prevention. This project contains an empirical study regarding the dynamics of the current COVID-19 pandemic. We have formulated a dynamic model of COVID-19 pandemic by subdividing the total population into six different classes namely susceptible, asymptomatic, infected, recovered, quarantined, and vaccinated. The basic reproduction number corresponding to our model has been determined. Moreover, sensitivity analysis has been conducted to find the most important parameters which can be crucial in preventing the outbreak. Numerical simulations have been made to visualize the movement of population in different classes and specifically to see the effect of quarantine and vaccination processes. The findings from our model reveal that both vaccination and quarantine are important to curtail the spread of COVID-19 pandemic. The present study can be effective in public health sectors for minimizing the burden of any pandemic.