  As the COVID-19 pandemic continues to impact the world, data is being
gathered and analyzed to better understand the disease. Recognizing the
potential for visual analytics technologies to support exploratory analysis and
hypothesis generation from longitudinal clinical data, a team of collaborators
worked to apply existing event sequence visual analytics technologies to a
longitudinal clinical data from a cohort of 998 patients with high rates of
COVID-19 infection. This paper describes the initial steps toward this goal,
including: (1) the data transformation and processing work required to prepare
the data for visual analysis, (2) initial findings and observations, and (3)
qualitative feedback and lessons learned which highlight key features as well
as limitations to address in future work.
