Analyzing the vast coronavirus literature with CoronaCentral

Lever, J. and Altman, R. B. (2021) Analyzing the vast coronavirus literature with CoronaCentral. Proceedings of the National Academy of Sciences of the United States of America, 118(23), 2100766118. (doi: 10.1073/pnas.2100766118) (PMID:34016708)

Abstract

he 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.

• Item Type: Articles
• Additional Information: This project has been supported by the Chan Zuckerberg Biohub and through National Library of Medicine Grant LM05652 (to R.B.A.).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Lever, Dr Jake
• Authors: Lever, J., and Altman, R. B.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Proceedings of the National Academy of Sciences of the United States of America
• Publisher: National Academy of Sciences
• ISSN: 0027-8424
• ISSN (Online): 1091-6490
• Published Online: 20 May 2021
• Copyright Holders: Copyright © 2021 The Authors
• First Published: First published in Proceedings of the National Academy of Sciences of the United States of America
• Publisher Policy: Reproduced under a Creative Commons Licence