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Review
. 2021 Jun 8;118(23):e2100766118.
doi: 10.1073/pnas.2100766118.

Analyzing the vast coronavirus literature with CoronaCentral

Jake Lever  1 , Russ B Altman  2
Affiliations
Review

Analyzing the vast coronavirus literature with CoronaCentral

Jake Lever et al. Proc Natl Acad Sci U S A. .

Abstract

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. This resource, available at https://coronacentral.ai, is updated daily.

Keywords: coronavirus; literature analysis; literature categorization; machine learning.

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Conflict of interest statement

Competing interest statement: D.L.D., J.L., and R.B.A. are all affiliated with Stanford University.

Figures

Fig. 1.
Fig. 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.
Fig. 2.
Fig. 2.
Communication of research has changed with a greater emphasis on social media and preprint servers. (A) The number of papers categorized with each topic in the 100 papers with highest Altmetric scores. (B) Top journals and preprint servers. (C) Topic breakdown for each preprint server and nonpreprint peer-reviewed journals. Infrequent topics in preprints are grouped in “Other.”
See this image and copyright information in PMC

Update of

References

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    1. Zhang E., et al., Covidex: Neural ranking models and keyword search infrastructure for the covid-19 open research dataset. arXiv [Preprint] (2020). https://arxiv.org/abs/2007.07846 (Accessed 15 December 2020).

Publication types

MeSH terms