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The year 2020 has seen an unprecedented COVID-19 pandemic due to the outbreakof a novel strain of coronavirus in 180 countries. In a desperate effort todiscover new drugs and vaccines for COVID-19, many scientists are workingaround the clock. Their valuable time and effort may benefit fromcomputer-based mining of a large volume of health science literature that is atreasure trove of information. In this paper, we have developed a graph-basedmodel using abstracts of 10,683 scientific articles to find key information onthree topics: transmission, drug types, and genome research related tocoronavirus. A subgraph is built for each of the three topics to extract moretopic-focused information. Within each subgraph, we use a betweennesscentrality measurement to rank order the importance of keywords related todrugs, diseases, pathogens, hosts of pathogens, and biomolecules. The resultsreveal intriguing information about antiviral drugs (Chloroquine, Amantadine,Dexamethasone), pathogen-hosts (pigs, bats, macaque, cynomolgus), viralpathogens (zika, dengue, malaria, and several viruses in the coronaviridaevirus family), and proteins and therapeutic mechanisms (oligonucleotide,interferon, glycoprotein) in connection with the core topic of coronavirus. Thecategorical summary of these keywords and topics may be a useful reference toexpedite and recommend new and alternative directions for COVID-19 research.

Information Mining for COVID-19 Research From a Large Volume of Scientific Literature