  The year 2020 has seen an unprecedented COVID-19 pandemic due to the outbreak
of a novel strain of coronavirus in 180 countries. In a desperate effort to
discover new drugs and vaccines for COVID-19, many scientists are working
around the clock. Their valuable time and effort may benefit from
computer-based mining of a large volume of health science literature that is a
treasure trove of information. In this paper, we have developed a graph-based
model using abstracts of 10,683 scientific articles to find key information on
three topics: transmission, drug types, and genome research related to
coronavirus. A subgraph is built for each of the three topics to extract more
topic-focused information. Within each subgraph, we use a betweenness
centrality measurement to rank order the importance of keywords related to
drugs, diseases, pathogens, hosts of pathogens, and biomolecules. The results
reveal intriguing information about antiviral drugs (Chloroquine, Amantadine,
Dexamethasone), pathogen-hosts (pigs, bats, macaque, cynomolgus), viral
pathogens (zika, dengue, malaria, and several viruses in the coronaviridae
virus family), and proteins and therapeutic mechanisms (oligonucleotide,
interferon, glycoprotein) in connection with the core topic of coronavirus. The
categorical summary of these keywords and topics may be a useful reference to
expedite and recommend new and alternative directions for COVID-19 research.
