The 2019 novel coronavirus, also known as COVID-19, is an infectious disease which was first reported in late 2019 and soon spread to become a global pandemic, prompting major action from world governments. Soon after, many institutions began attempts to analyze and predict the spread and severity of the disease via statistical modeling. Some information is not available for public consumption; however, a number of institutions have published the results of their analyses and some have made public repositories of the code used to build the models. This research paper attempts use these and other resources to examine the modeling techniques employed by several such institutions and assess their accuracy and efficacy. In addition, it will explore these techniques and discuss their implementations as well as what assumptions they rely upon and how small parameter changes affect output
