  With the rapid global spread of COVID-19, more and more data related to this
virus is becoming available, including genomic sequence data. The total number
of genomic sequences that are publicly available on platforms such as GISAID is
currently several million, and is increasing with every day. The availability
of such \emph{Big Data} creates a new opportunity for researchers to study this
virus in detail. This is particularly important with all of the dynamics of the
COVID-19 variants which emerge and circulate. This rich data source will give
us insights on the best ways to perform genomic surveillance for this and
future pandemic threats, with the ultimate goal of mitigating or eliminating
such threats. Analyzing and processing the several million genomic sequences is
a challenging task. Although traditional methods for sequence classification
are proven to be effective, they are not designed to deal with these specific
types of genomic sequences. Moreover, most of the existing methods also face
the issue of scalability. Previous studies which were tailored to coronavirus
genomic data proposed to use spike sequences (corresponding to a subsequence of
the genome), rather than using the complete genomic sequence, to perform
different machine learning (ML) tasks such as classification and clustering.
However, those methods suffer from scalability issues. In this paper, we
propose an approach called Spike2Vec, an efficient and scalable feature vector
representation for each spike sequence that can be used for downstream ML
tasks. Through experiments, we show that Spike2Vec is not only scalable on
several million spike sequences, but also outperforms the baseline models in
terms of prediction accuracy, F1 score, etc.
