COVID-19 is an infectious respiratory disease that has developed into a worldwide pandemic. This presentation seeks to use mathematical modeling as a means of creating a starting point to understand and predict the behavior of the spread of COVID-19 over time. We use the SIR model of three coupled non-linear differential equations to forecast the proportion of susceptible, infected, and recovered people in Ohio. While we cannot account for all factors that have the potential to change the modeling results (such as pathogen mutation, inconsistent implementation of safety precautions, and varying symptoms/symptom severity) due to complexity and insufficient data, our modeling provides the groundwork for guiding pandemic response and evaluating the effectiveness of current measures.