Convergence in Viral Outbreak Research: Using Natural Language Processing to Define Network Bridges in the Bench-Bedside-Population Paradigm

Abstract

Research on viral outbreaks at the pandemic scale responds to heightened social urgency and the need to expedite scientific discovery from the “bench” to the “bedside” to the wider population. We sought to better understand translational research within the context of pandemics, both historical and present day, by tracking publication trends in the immediate aftermath of virus outbreaks. We used a blend of natural language processing (NLP), social network analysis and human annotation approaches to analyze the 85,663 articles in the COVID-19 Open Research Dataset (CORD-19). We found stable and repeated characteristics throughout subsets of peer-reviewed published literature corresponding to seven different viral outbreaks over the last several decades. Three distinct groups or “neighborhoods” recurred across all of the model networks – (1) bench science, (2) clinical treatments, and (3) broader public health trends. Notably, in each historical virus model, small “bridge” nodes representing translational research connect the three otherwise disconnected neighborhoods. These bridging studies embody research convergence by both integrating the vocabulary and methods of different disciplines and bodies of previous work and by citing other papers beyond their narrow field. In the case of COVID-19, the literature continues to evolve apace along with the virus, and we can witness the phases of response unfold as the science progresses. This study demonstrates how the different sectors of biomedical research respond independently to public health emergencies and how translational research can facilitate greater information synthesis and exchange between disciplinary silos.