The classic way of modeling the progression of an infectious disease has been called the SIR (Susceptible-Infected-Recovered) model. Further refinements of this types of models capture additional states like the SEIRD model (Susceptible, Exposed, Infected, Recovered, and Dead) that captures the exposure and death states. The COVID-19 pandemic has resulted in a number of these models—built for different countries—that capture additional states. We examine current literature in this rich area of epidemiological models including states for contact, quarantined, not quarantined, pre-symptomatic and pre-asymptomatic, symptomatic and asymptomatic states, hospitalization, and immunized. The more states of infection a model captures, the more it facilitates fine-grained decision-making. We review these new models and how they have been used during the pandemic to make spatio-temporal predictions on the progression of COVID19