National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, MD, USA.
Towson University, Towson, MD, USA.
Nucleic Acids Res. 2023 Jan 6;51(D1):D1512-D1518. doi: 10.1093/nar/gkac1005.
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.
LitCovid(https://www.ncbi.nlm.nih.gov/research/coronavirus/)于 2020 年 2 月首次推出,是一个专门用于追踪 COVID-19 最新已发表研究的文献枢纽。在过去的 2.5 年中,LitCovid 中的文章数量从 55000 篇增加到了约 300000 篇,每月的增长率稳定在约 10000 篇。除了文献数量的快速增长,COVID-19 大流行也发生了巨大变化。例如,奥密克戎变异株现在已经占美国新感染病例的 98%以上。为了应对 COVID-19 大流行的持续演变,本文描述了过去 2 年来 LitCovid 的重大更新。首先,我们引入了长新冠集合,其中包含了关于 COVID-19 幸存者经历持续多系统症状(包括呼吸问题、心血管疾病、认知障碍和严重疲劳)的文章。其次,我们对文献中提到的最新 COVID-19 株和疫苗提供了新的注释。第三,我们改进了几个现有的功能,使用更准确的机器学习算法来注释主题和对与 COVID-19 相关的文章进行分类。LitCovid 已被全球数百万用户广泛使用,用于满足各种信息需求,并且继续在收集、整理和规范 COVID-19 文献的最新知识方面发挥关键作用。