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. 2023 Jan 6;51(D1):D1512-D1518.
doi: 10.1093/nar/gkac1005.

LitCovid in 2022: an information resource for the COVID-19 literature

Qingyu Chen  1 , Alexis Allot  1 , Robert Leaman  1 , Chih-Hsuan Wei  1 , Elaheh Aghaarabi  1   2 , John J Guerrerio  1   3 , Lilly Xu  1   4 , Zhiyong Lu  1
Affiliations

LitCovid in 2022: an information resource for the COVID-19 literature

Qingyu Chen et al. Nucleic Acids Res. .

Erratum in

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 2.5 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 United States. In response to the continuing evolution of the COVID-19 pandemic, this article describes significant updates to LitCovid over the last 2 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 impairment, and profound fatigue. Second, we provided new annotations on the latest COVID-19 strains and vaccines mentioned in the literature. Third, we improved several existing features with more accurate machine learning algorithms for annotating topics and classifying articles relevant to COVID-19. LitCovid has been widely used with millions of accesses by users worldwide on various information needs and continues to play a critical role in collecting, curating and standardizing the latest knowledge on the COVID-19 literature.

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Figures

Figure 1.
Figure 1.
The overall growth of LitCovid by July 2022. (A) The accumulated daily literature growth; (B) the quarterly ratio of LitCovid articles compared with entire PubMed articles.
Figure 2.
Figure 2.
The overall growth of LitCovid topics by July 2022. (A) The accumulated topic growth; (B) the distributions of topics assigned per LitCovid article; (C) topic co-occurrences.
Figure 3.
Figure 3.
Comparative analysis of the long COVID topic coverage between LitCovid and Clinical Queries.
Figure 4.
Figure 4.
The updated LitCovid interface (screenshot in 10 September 2022).
See this image and copyright information in PMC

References

    1. Chen Q., Leaman R., Allot A., Luo L., Wei C.-H., Yan S., Lu Z.. Artificial intelligence in action: addressing the COVID-19 pandemic with natural language processing. Annu. Rev. Biomed. Data Sci. 2021; 4:313–339. - PubMed
    1. Chen Q., Allot A., Lu Z.. Keep up with the latest coronavirus research. Nature. 2020; 579:193–193. - PubMed
    1. Chen Q., Allot A., Lu Z.. LitCovid: an open database of COVID-19 literature. Nucleic Acids Res. 2021; 49:D1534–D1540. - PMC - PubMed
    1. Callard F., Perego E.. How and why patients made long covid. Soc. Sci. Med. 2021; 268:113426. - PMC - PubMed
    1. Bergmans R.S., Chambers-Peeple K., Aboul-Hassan D., Dell’Imperio S., Martin A., Wegryn-Jones R., Xiao L.Z., Yu C., Williams D.A., Clauw D.J.. Opportunities to improve long COVID care: implications from semi-structured interviews with black patients. Patient. 2022; 15:715–728. - PMC - PubMed

Publication types

Supplementary concepts