Analyzing the vast coronavirus literature with CoronaCentral

  

View ORCID ProfileJake Lever,  View ORCID ProfileRuss B. Altman

doi: https://doi.org/10.1101/2020.12.21.423860

This article is a preprint and has not been certified by peer review [what does this mean?].

PDF: https://www.biorxiv.org/content/10.1101/2020.12.21.423860v1.full.pdf

Abstract

The global SARS-CoV-2 pandemic has caused a surge in research exploring all aspects of the virus and its effects on human health. The overwhelming rate of publications means that human researchers are unable to keep abreast of the research.

To ameliorate this, we present the CoronaCentral resource which uses machine learning to process the research literature on SARS-CoV-2 along with articles on SARS-CoV and MERS-CoV. We break the literature down into useful categories and enable analysis of the contents, pace, and emphasis of research during the crisis. These categories cover therapeutics, forecasting as well as growing areas such as “Long Covid” and studies of inequality and misinformation. Using this data, we compare topics that appear in original research articles compared to commentaries and other article types. Finally, using Altmetric data, we identify the topics that have gained the most media attention.

This resource, available at https://coronacentral.ai, is updated multiple times per day and provides an easy-to-navigate system to find papers in different categories, focussing on different aspects of the virus along with currently trending articles.

Competing Interest Statement

The authors have declared no competing interest.

https://www.biorxiv.org/content/10.1101/2020.12.21.423860v1