Machine learning based predictive modeling and risk factors for prolonged SARS-CoV-2 shedding.

Journal: Journal of translational medicine

Published Date: Nov 22, 2024

Abstract

BACKGROUND: The global outbreak of the coronavirus disease 2019 (COVID-19) has been enormously damaging, in which prolonged shedding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, previously 2019-nCoV) infection is a challenge in the prevention and treatment of COVID-19. However, there is still incomplete research on the risk factors that affect delayed shedding of SARS-CoV-2.

Authors

Yani Zhang

Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China.
Qiankun Li

University of Science and Technology of China, Hefei, Anhui, China.
Haijun Duan

Department of Neurosurgery, Southwest Hospital, Army Medical University, Chongqing, China.
Liang Tan

Department of Neurosurgery, Southwest Hospital, Third Military Medical University (Army Military Medical University), Chongqing, China.
Ying Cao
Junxin Chen

Keywords

Adolescent Adult Aged China COVID-19 Female Humans Machine Learning Male Middle Aged Retrospective Studies Risk Factors SARS-CoV-2 Time Factors Virus Shedding Young Adult

External Resources

View on PubMed Access via DOI PubMed (39578848)

Machine learning based predictive modeling and risk factors for prolonged SARS-CoV-2 shedding.

Abstract

Authors

Keywords

External Resources

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