Background COVID-19 knowledge has been changing rapidly with the fast pace of
information that accompanied the pandemic. Since peer-reviewed research is a trusted
source of evidence, capturing and predicting the emerging themes in COVID-19 literature
are crucial for guiding research and policy. Machine learning, natural language processing
and dynamical networks have the potential to enable rapid distillation and prediction of
actionable insights for ending the pandemic. Objective We hypothesized that emerging …

Background Evidence from peer-reviewed literature is the cornerstone for designing
responses to global threats such as COVID-19. In massive and rapidly growing corpuses,
such as COVID-19 publications, assimilating and synthesizing information is challenging.
Leveraging a robust computational pipeline that evaluates multiple aspects, such as network
topological features, communities, and their temporal trends, can make this process more
efficient. Objective We aimed to show that new knowledge can be captured and tracked …