Jake Lever and Russ B. Altman

PNAS June 8, 2021 118 (23) e2100766118

Abstract

Figure 2: Communication of research has changed with a greater emphasis on social media and preprint servers.

The SARS-CoV-2 pandemic has caused a surge in research exploring all aspects of the virus and its effects on human health. The overwhelming publication rate means that researchers are unable to keep abreast of the literature. To ameliorate this, we present the CoronaCentral resource that uses machine learning to process the research literature on SARS-CoV-2 together with SARS-CoV and MERS-CoV. We categorize the literature into useful topics and article types and enable analysis of the contents, pace, and emphasis of research during the crisis with integration of Altmetric data. These topics include therapeutics, disease forecasting, as well as growing areas such as “long COVID” and studies of inequality.

The COVID-19 pandemic has led to the greatest surge in biomedical research on a single topic in documented history (Fig. 1A). This research is valuable both to current and future researchers as they examine the long-term effects of the virus on different aspects of society. Unfortunately, the vast scale of the literature makes it challenging to navigate. Machine-learning systems that can automatically identify topics and article types of papers would greatly benefit researchers who are searching for relevant coronavirus research.

See: https://www.pnas.org/content/118/23/e2100766118

Figure 1:

Overview of research trends and important topics. (A) Largest year-on-year changes in the percentage of papers that mention a biomedical concept using data from PubTator (8). (B) Frequency of each topic and (C) article type across the entire coronavirus literature. (D) The trajectories of the top five topics for original research and comment/editorial articles for SARS-CoV-2. (E) Different proportions of article types for each topic.