  A dataset of COVID-19-related scientific literature is compiled, combining
the articles from several online libraries and selecting those with open access
and full text available. Then, hierarchical nonnegative matrix factorization is
used to organize literature related to the novel coronavirus into a tree
structure that allows researchers to search for relevant literature based on
detected topics. We discover eight major latent topics and 52 granular
subtopics in the body of literature, related to vaccines, genetic structure and
modeling of the disease and patient studies, as well as related diseases and
virology. In order that our tool may help current researchers, an interactive
website is created that organizes available literature using this hierarchical
structure.
