  As the major target of many vaccines and neutralizing antibodies against
SARS-CoV-2, the spike (S) protein is observed to mutate over time. In this
paper, we present statistical approaches to tackle some challenges associated
with the analysis of S-protein data. We build a Bayesian hierarchical model to
study the temporal and spatial evolution of S-protein sequences, after grouping
the sequences into representative clusters. We then apply sampling methods to
investigate possible changes to the S-protein's 3-D structure as a result of
commonly observed mutations. While the increasing spread of D614G variants has
been noted in other research, our results also show that the co-occurring
mutations of D614G together with S477N or A222V may spread even more rapidly,
as quantified by our model estimates.
