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.


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

Cite as

Lever, J. & Altman, R. 2021, 'Analyzing the vast coronavirus literature with CoronaCentral', Proceedings of the National Academy of Sciences of the United States of America, 118(23), article no: e2100766118 . https://doi.org/10.1073/pnas.2100766118

Downloadable citations

Download HTML citationHTML Download BIB citationBIB Download RIS citationRIS
Last updated: 17 June 2022
Was this page helpful?