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. 2021 Jan 8;49(D1):D1534-D1540.
doi: 10.1093/nar/gkaa952.

LitCovid: an open database of COVID-19 literature

Qingyu Chen  1 , Alexis Allot  1 , Zhiyong Lu  1
Affiliations

LitCovid: an open database of COVID-19 literature

Qingyu Chen et al. Nucleic Acids Res. .

Abstract

Since the outbreak of the current pandemic in 2020, there has been a rapid growth of published articles on COVID-19 and SARS-CoV-2, with about 10,000 new articles added each month. This is causing an increasingly serious information overload, making it difficult for scientists, healthcare professionals and the general public to remain up to date on the latest SARS-CoV-2 and COVID-19 research. Hence, we developed LitCovid (https://www.ncbi.nlm.nih.gov/research/coronavirus/), a curated literature hub, to track up-to-date scientific information in PubMed. LitCovid is updated daily with newly identified relevant articles organized into curated categories. To support manual curation, advanced machine-learning and deep-learning algorithms have been developed, evaluated and integrated into the curation workflow. To the best of our knowledge, LitCovid is the first-of-its-kind COVID-19-specific literature resource, with all of its collected articles and curated data freely available. Since its release, LitCovid has been widely used, with millions of accesses by users worldwide for various information needs, such as evidence synthesis, drug discovery and text and data mining, among others.

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Figures

Figure 1.
Figure 1.
An overview of the LitCovid daily workflow (A–G). The PubMed publications are retrieved using a query via NCBI’s E-utils. The publications are classified as to whether they are relevant to COVID-19. The relevant publications are curated further by topic categorization as well as geolocation and chemical extraction. The curated publications are then imported into our Solr database.
Figure 2.
Figure 2.
LitCovid user interface: (A) free-text search box; (B) topic navigation; (C) filter facets by chemical, journal title, or country; (D) document summary with article meta-information; (E) download and RSS options; (F) growth of articles per week; and (G) a visualization on geolocations mentioned in titles and abstracts.
Figure 3.
Figure 3.
(A) cumulative growth of LitCovid, accessed 21 September 2020. The compound daily growth rate was calculated based on the number of articles added daily; (B) topic distributions and co-occurrences among the topics.
Figure 4.
Figure 4.
Comparative analysis of the database coverage between LitCovid and PubMed queries.
Figure 5.
Figure 5.
Proportion of users accessing different features of LitCovid, based on the data in July 2020.
See this image and copyright information in PMC

References

    1. Chen Q., Allot A., Lu Z.. Keep up with the latest coronavirus research. Nature. 2020; 579:193. - PubMed
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    1. Fiorini N., Leaman R., Lipman D.J., Lu Z.. How user intelligence is improving PubMed. Nat. Biotechnol. 2018; 36:937–945. - PubMed
    1. Leaman R., Wei C.H., Allot A., Lu Z.. Ten tips for a text-mining-ready article: how to improve automated discoverability and interpretability. PLoS Biol. 2020; 18:e3000716. - PMC - PubMed

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