Analyzing the vast coronavirus literature with CoronaCentral

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.

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This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY) [http://creativecommons.org/licenses/by/4.0/]

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Identifiers

DOI

https://doi.org/10.1073/pnas.2100766118

Repository URI

http://eprints.gla.ac.uk/242644/