  Coronavirus disease (COVID-19) is an infectious disease, which is caused by
the SARS-CoV-2 virus. Due to the growing literature on COVID-19, it is hard to
get precise, up-to-date information about the virus. Practitioners, front-line
workers, and researchers require expert-specific methods to stay current on
scientific knowledge and research findings. However, there are a lot of
research papers being written on the subject, which makes it hard to keep up
with the most recent research. This problem motivates us to propose the design
of the COVID-19 Search Engine (CO-SE), which is an algorithmic system that
finds relevant documents for each query (asked by a user) and answers complex
questions by searching a large corpus of publications. The CO-SE has a
retriever component trained on the TF-IDF vectorizer that retrieves the
relevant documents from the system. It also consists of a reader component that
consists of a Transformer-based model, which is used to read the paragraphs and
find the answers related to the query from the retrieved documents. The
proposed model has outperformed previous models, obtaining an exact match ratio
score of 71.45% and a semantic answer similarity score of 78.55%. It also
outperforms other benchmark datasets, demonstrating the generalizability of the
proposed approach.
