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Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples

Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples

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

Yani Zhang^1,2,3, Qiankun Li², Haijun Duan⁴

¹Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China.

Journal of Translational Medicine

|November 23, 2024

Summary

Prolonged severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) shedding is a challenge. This study identified six key risk factors, including vaccination status and hypertension, to predict long-term SARS-CoV-2 infection in hospitalized patients.

Keywords:

COVID-19 Duration of viral shedding Machine learning SARS-CoV-2 SHAP interpretability analysis XGBoost

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Published on: September 28, 2022

Area of Science:

Infectious Diseases
Epidemiology
Machine Learning in Healthcare

Background:

The COVID-19 pandemic highlights challenges in managing prolonged severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) shedding.
Identifying risk factors for delayed SARS-CoV-2 clearance is crucial for effective prevention and treatment strategies.

Purpose of the Study:

To develop a predictive model for prolonged SARS-CoV-2 viral shedding.
To identify significant risk factors associated with extended SARS-CoV-2 infection duration in hospitalized patients.

Main Methods:

Retrospective analysis of 56,878 hospitalized patients.
Utilized the extreme gradient boosting (XGBoost) machine learning algorithm to build a predictive model.
Employed Shapley Additive Explanations (SHAP) for detailed risk factor analysis.

Main Results:

Six features significantly impacted prolonged SARS-CoV-2 shedding: vaccination, hypertension, admission cycle threshold (Ct) value, gender, age, and family accompaniment.
The XGBoost model effectively predicted prolonged viral shedding based on these identified risk factors.
Analysis confirmed the association of these six factors with extended SARS-CoV-2 infection duration.

Conclusions:

A predictive model for prolonged SARS-CoV-2 shedding was successfully developed.
Six key risk factors were identified, aiding in screening individuals at risk for long-term infection.
Findings offer valuable insights for preventive control, resource allocation, and personal protection strategies against SARS-CoV-2.