COVID-19 is an ongoing infectious disease where individuals progress through the following four stages: susceptible (S), exposed (E), infected (I), and recovered (R). The standard mathematical model for the spread of such diseases through a large population is a system of ordinary differential equations, called the SEIR model. However, the qualitative features of the outbreak predicted from the SEIR model do not match with what the actual course of COVID-19 is doing in the U.S. population. In this thesis, we explore several different modifications of the standard SEIR model to determine and observe whether these modifications can recreate the qualitative features of the real world data.