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LitCovid in 2022: an information resource for the COVID-19 literature (2209.13428v1)

Published 27 Sep 2022 in cs.DL and cs.IR

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 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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Authors (8)
  1. Qingyu Chen (57 papers)
  2. Alexis Allot (11 papers)
  3. Robert Leaman (15 papers)
  4. Chih-Hsuan Wei (16 papers)
  5. Elaheh Aghaarabi (1 paper)
  6. John J. Guerrerio (1 paper)
  7. Lilly Xu (1 paper)
  8. Zhiyong Lu (113 papers)
Citations (21)

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