Machine learning based predictive modeling and risk factors for prolonged SARS-CoV-2 shedding
We developed a predictive model and identified six risk factors associated with prolonged SARS-CoV-2 viral shedding. Our study contributes to identifying and screening individuals with potential long-term SARS-CoV-2 infections. Moreover, our research also provides a reference for future preventive control, optimizing medical resource allocation and guiding epidemiological prevention, and guidelines for personal protection against SARS-CoV-2.
Authors
- Liang Tan (ORCID: https://orcid.org/0000-0002-6916-2134)
- Haijun Duan
- Junxin Chen (ORCID: https://orcid.org/0000-0003-4745-8361)
- Qiankun Li (ORCID: https://orcid.org/0000-0001-5121-1682)
- Ying Cao (ORCID: https://orcid.org/0000-0001-8637-9987)
- Yani Zhang (ORCID: https://orcid.org/0009-0007-8769-8518)
Institutions
- University of Science and Technology of China (CN)
- Army Medical University (CN)
- Dalian University of Foreign Languages (CN)
- Chinese Academy of Sciences (CN)
- Dalian University of Technology (CN)
- Hefei Institutes of Physical Science (CN)
- Southwest Hospital (CN)
Publication Details
- Journal
- Journal of Translational Medicine
- Published
- 2024-11-22
- DOI
- https://doi.org/10.1186/s12967-024-05872-7
- Citations
- 2
- Primary Topic
- COVID-19 Clinical Research Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.78
Funders
- National Natural Science Foundation of China
- Fundamental Research Funds for the Central Universities