In the midst of the ongoing COVID-19 pandemic, the Fall semester has completed at college campuses across the United States. To confront this crisis, universities have employed widely varying policies regarding instruction and infection testing. Armed with data on cases numbers and positivity rates, researchers now have the opportunity to reflect and consider the effectiveness of these policies. University COVID-19 mitigation policies have primarily operated on two fronts: shifting the level of in-person instruction and enacting infection testing and isolation programs. The effect of these policies and practices is reflected in the heterogeneity of reported campus case numbers. A CDC report found that the incidence of COVID-19 at large institutions of higher education was highly dependent on whether the institution had primarily remote or in-person instruction. Further, campus outbreaks have been linked to transmission in their neighboring communities, leading researchers to label some universities as ‘super-spreaders’. Simple transmission models can provide insight into the efficacy of public health policies across a spectrum of organization sizes and types. Model analysis produces measures which quantify the outbreak potential of a pathogen in a given population, generating hypotheses that can be tested against data. However, there is a major gap in the application of population level transmission models to complex and heterogeneous organizations like universities.