TY - JOUR
AU - Lever, Jake
AU - Altman, Russ B.
PY - 2021
DA - June
TI - Analyzing the vast coronavirus literature with CoronaCentral
JO - Proceedings of the National Academy of Sciences of the United States of America
VL - 118
IS - 23
DO - https://doi.org/10.1073/pnas.2100766118
AB - The SARS-CoV-2 pandemic has caused a surge in research exploring all aspects of the virus and its effects on human health. The overwhelming publication rate means that researchers are unable to keep abreast of the literature. To ameliorate this, we present the CoronaCentral resource that uses machine learning to process the research literature on SARS-CoV-2 together with SARS-CoV and MERS-CoV. We categorize the literature into useful topics and article types and enable analysis of the contents, pace, and emphasis of research during the crisis with integration of Altmetric data. These topics include therapeutics, disease forecasting, as well as growing areas such as “long COVID” and studies of inequality. This resource, available at https://coronacentral.ai, is updated daily.
PB - National Academy of Sciences
UR - http://eprints.gla.ac.uk/242644/
KW - Coronavirus (COVID-19)
ER