  COVID-19 is one of the most important topic these days, specifically on
search engines and news. While fake news are easily shared, scientific papers
are reliable sources where information can be extracted. With about 24,000
scientific publications on COVID-19 and related research on PUBMED, automatic
computer-assisted analysis is required. In this paper, we develop two
methodologies to get insights on specific sub-topics of interest and latest
research sub-topics. They rely on natural language processing and graph-based
visualizations. We run these methodologies on two cases: the virus origin and
the uses of existing drugs.
