LitCovid in 2022: an information resource for the COVID-19 literature
Qingyu Chen, Alexis Allot, Robert Leaman, Chih-Hsuan Wei, Elaheh, Aghaarabi, John J. Guerrerio, Lilly Xu, Zhiyong Lu

TL;DR
LitCovid is a comprehensive, continuously updated literature hub for COVID-19 research, incorporating new collections, annotations, and improved algorithms to support global information needs during the pandemic.
Contribution
This paper details significant updates to LitCovid, including new collections, annotations, and machine learning enhancements, to better serve COVID-19 research needs.
Findings
Growth from 55,000 to 300,000 articles
Introduction of Long Covid collection
Enhanced annotation and classification algorithms
Abstract
LitCovid (https://www.ncbi.nlm.nih.gov/research/coronavirus/), first launched in February 2020, is a first-of-its-kind literature hub for tracking up-to-date published research on COVID-19. The number of articles in LitCovid has increased from 55,000 to ~300,000 over the past two and half years, with a consistent growth rate of ~10,000 articles per month. In addition to the rapid literature growth, the COVID-19 pandemic has evolved dramatically. For instance, the Omicron variant has now accounted for over 98% of new infections in the U.S. In response to the continuing evolution of the COVID-19 pandemic, this article describes significant updates to LitCovid over the last two years. First, we introduced the Long Covid collection consisting of the articles on COVID-19 survivors experiencing ongoing multisystemic symptoms, including respiratory issues, cardiovascular disease, cognitive…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsCOVID-19 diagnosis using AI · Biomedical Text Mining and Ontologies · Machine Learning in Healthcare
