Analyzing the vast coronavirus literature with CoronaCentral

Proc Natl Acad Sci U S A. 2021 Jun 8;118(23):e2100766118. doi: 10.1073/pnas.2100766118.


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, is updated daily.

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

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Animals
  • COVID-19* / epidemiology
  • COVID-19* / metabolism
  • COVID-19* / therapy
  • COVID-19* / transmission
  • Humans
  • Machine Learning*
  • Middle East Respiratory Syndrome Coronavirus / metabolism*
  • Middle East Respiratory Syndrome Coronavirus / pathogenicity
  • Pandemics*
  • SARS-CoV-2 / metabolism*
  • SARS-CoV-2 / pathogenicity
  • Severe Acute Respiratory Syndrome* / epidemiology
  • Severe Acute Respiratory Syndrome* / metabolism
  • Severe Acute Respiratory Syndrome* / therapy
  • Severe Acute Respiratory Syndrome* / transmission