  When COVID-19 first started spreading and quarantine was implemented, the
Society for Industrial and Applied Mathematics (SIAM) Student Chapter at the
University of Minnesota-Twin Cities began a collaboration with Ecolab to use
our skills as data scientists and mathematicians to extract useful insights
from relevant data relating to the pandemic. This collaboration consisted of
multiple groups working on different projects. In this write-up we focus on
using clustering techniques to help us find groups of similar counties in the
US and use that to help us understand the pandemic. Our team for this project
consisted of University of Minnesota students Cora Brown, Sarah Milstein,
Tianyi Sun, and Cooper Zhao, with help from Ecolab Data Scientist Jimmy
Broomfield and University of Minnesota student Skye Ke. In the sections below
we describe all of the work done for this project. In Section 2, we list the
data we gathered, as well as the feature engineering we performed. In Section
3, we describe the metrics we used for evaluating our models. In Section 4, we
explain the methods we used for interpreting the results of our various
clustering approaches. In Section 5, we describe the different clustering
methods we implemented. In Section 6, we present the results of our clustering
techniques and provide relevant interpretation. Finally, in Section 7, we
provide some concluding remarks comparing the different clustering methods.
