Modeling techniques have been in use in the field of epidemiology for centuries now. With one of the earliest examples found in its use to track the life expectancy in patients who were inoculated for smallpox. The foundation is then there to take the raw data derived from public health authorities and create models to understand and predict the direction of an ongoing health crisis. One such model is the SIR model that has its roots in a simple relationship between three possible states of being susceptible, infectious, and recovered. The transitions between these three states can be treated in the same manner of chemical kinetics where a concentration of the states exists that change in time depending on various factors. Two of those factors that will be focused heavily on in the procedure will be replacement number Rt/Re and the basic replacement number R0. The SIR model correlates the replacement number with the relative spreading speed of the virus. By adding additional parameters such as vaccination rates, SIR model can be extended to simulate the real-world data with a reasonable replacement number, especially at key points of public policy or social events. We have reported a laboratory procedure last year to analyze COVID-19 spreading in a state at the early stage with no vaccinations. This year, we added the vaccination data into the analysis and updated the SIR model to SIR-vaccinated (SIRV) model to analyze and predict the trends of COVID-spreading in the U.S. The skills taught and utilized in this procedure are useful in the physical chemistry classroom and many other places in the educational journey of an undergraduate student. The ability for students to see in real time the impact of public policy, and the statistical parameters they will derive from the model leaves a lasting impression about the usefulness and limitations of such techniques.