Introduction
There is urgency to accelerate research that can help contain the spread of the COVID-19 epidemic, to ensure that those affected are promptly diagnosed and receive optimal care and to support research priorities in a way that leads to the development of global research platforms in preparation for the next disease epidemic, thus allowing for accelerated research, and research and development for diagnostics, therapeutics and vaccines and their timely access. In view of the urgency of this outbreak, the international community is mobilising to find ways to significantly accelerate the development of interventions.1 Experts have identified key knowledge gaps and research priorities and shared scientific data on ongoing research, thereby accelerating the generation of critical scientific information to contribute to the control of the COVID-19 emergency.2
However, the pace and volume of research mean that it is hard to stay up to date with the growing body of new scientific papers about the disease and the novel coronavirus that causes it. To mitigate this, many organisations are hosting digital collections holding thousands of freely available papers that can help researchers quickly find the information they seek, and several studies have described or mapped the rapid evidence generation in this area.3–5 By one estimate, the COVID-19 literature published since January has reached more than 200 000 papers and is doubling every 30 days, one of the biggest episodes of disease-specific publications of scientific literature ever.6
One approach to navigating and searching such knowledge collections is through graph databases, which represent the connections between the semantic concepts with nodes, edges and other properties of the data.7 This allows semantic queries to search across the data set to find relationships between papers on any set of data points. Such a graph displayed in a visualisation tool gives an interactive overview of the nodes and connections between the concepts across the papers and allows one to move around and focus on what is interesting to the researcher.8
The aim of this short report is to demonstrate the feasibility of using a network graph approach for rapid navigation of the COVID-19 literature in a publicly available format and to present an openly available tool for exploring a COVID-19 knowledge data set.