  With the rapid spread of COVID-19 worldwide, viral genomic data is available
in the order of millions of sequences on public databases such as GISAID. This
Big Data creates a unique opportunity for analysis towards the research of
effective vaccine development for current pandemics, and avoiding or mitigating
future pandemics. One piece of information that comes with every such viral
sequence is the geographical location where it was collected -- the patterns
found between viral variants and geographical location surely being an
important part of this analysis. One major challenge that researchers face is
processing such huge, highly dimensional data to obtain useful insights as
quickly as possible. Most of the existing methods face scalability issues when
dealing with the magnitude of such data. In this paper, we propose an approach
that first computes a numerical representation of the spike protein sequence of
SARS-CoV-2 using $k$-mers (substrings) and then uses several machine learning
models to classify the sequences based on geographical location. We show that
our proposed model significantly outperforms the baselines. We also show the
importance of different amino acids in the spike sequences by computing the
information gain corresponding to the true class labels.
