Predictive models have played a critical role in local, national, and international response to the COVID-19 pandemic. In the U.S, health care systems and governmental agencies have relied on several models such as IHME, YYG, MIT, CDC ensemble, etc. to predict short and long-term trends in disease activity. The Mayo Clinic Bayesian SIR model, recently been made publicly available, has informed Mayo Clinic practice leadership at all sites across the US and has been shared with Minnesota governmental leadership to help inform critical decisions over the past year. One key to the accuracy of the Mayo model is its ability to adapt to the constantly changing dynamics of the pandemic and uncertainties of human behavior, such as changes in the rate of contact among the population over time and by geographic location and now new virus variants. The Mayo model can also be used forecast COVID-19 trends in different hypothetical worlds in which (i) no vaccine is available, (ii) vaccinations are no longer being accepted from this point forward, and (iii) 75% of the population is already vaccinated. Surveys indicate that half of American adults are hesitant to receive a COVID-19 vaccine, and lack of understanding of the benefits of vaccination is an important barrier to use. The focus of this paper is to illustrate the stark contrast between these three scenarios and to demonstrate, mathematically, the benefit of high vaccine uptake on the future course of the pandemic.