The COVID-19 pandemic has exposed some deep problems in the ways that the US handles public health issues amongst vulnerable populations. One group that has been particularly affected is the homeless population. Living mostly on the street or in congregate settings placed them in situations where they were at high risk of contracting the virus. Their already poor health put them at further risk for severe disease. This, in combination with the lack of current research on homeless populations, motivated this project’s objective. This thesis aimed to develop an SEIR model of COVID-19 spread amongst homeless populations in four US cities. The model utilized probabilistic transitions between each stage of infection and between different housing settings to evaluate the actual and possible outcomes of outbreaks through total cases, deaths, and ICU admissions. The model was then applied to the specific intervention of emergency hotel housing to determine how the effectiveness of this control measure would have varied with earlier or delayed action. The results showed that emergency hotel housing was very necessary and effective in reducing the number of cases, deaths, and ICU admissions during the pandemic. The hope is that the results will encourage the importance of providing housing options to homeless populations, especially during crises as severe as the COVID-19 pandemic. Ideally, with further investigation, this model can be used to create a full evaluation on all of the best ways to protect such an underserved population.