  The epidemiology of pandemics is classically viewed using geographical and
political borders; however, these artificial divisions can result in a
misunderstanding of the current epidemiological state within a given region. To
improve upon current methods, we propose a clustering algorithm which is
capable of recasting regions into well-mixed clusters such that they have a
high level of interconnection while minimizing the external flow of the
population towards other clusters. Moreover, we analyze and identify so called
core clusters, clusters that retain their features over time (temporally
stable) and independent of the presence or absence of policy measures. In order
to demonstrate the capabilities of this algorithm, we use US county-level
cellular mobility data to divide the country into such clusters. Herein, we
show a more granular spread of SARS-CoV-2 throughout the first weeks of the
pandemic. Moreover, we are able to identify areas (groups of counties) that
were experiencing above average levels of transmission within a state, as well
as pan-state areas (clusters overlapping more than one state) with very similar
disease spread. Therefore, our method enables policymakers to make more
informed decisions on the use of public health interventions within their
jurisdiction, as well as guide collaboration with surrounding regions to
benefit the general population in controlling the spread of communicable
diseases.
