Clustering County-Wise COVID-19 Dynamics in North Carolina

Abstract

The COVID-19 pandemic has caused unprecedented impacts along with an enormous number of confirmed cases and deaths in the U.S. This study aims to identify hidden clusters among counties in North Carolina using the COVID-19 data. Since individual states implement their own policies to cope with the COVID-19 pandemic, our study is limited to a single state, North Carolina. We incorporated two clustering techniques, dynamic time warping and deep learning autoeconder. These clustering techniques identified similar upper-level hierarchical clusters separating three metropolitan areas and other regions with slightly different sub-clusters in the county-wise COVID-19 data. Our findings further understanding of county-wise COVID-19 dynamics and its implication.