Machine Learning Early Detection of SARS-CoV-2 High-Risk Variants

Area of Science:

• Virology and Bioinformatics
• Epidemiology and Public Health
• Computational Biology

Background:

• Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants pose ongoing public health challenges, necessitating rapid identification of high-risk strains.
• Traditional methods for detecting and characterizing novel SARS-CoV-2 variants are often slow, hindering timely epidemic control efforts.
• Early warning systems are crucial for managing infectious disease outbreaks and mitigating their impact.

Purpose of the Study:

• To develop and validate a machine learning algorithm, HiRisk-Detector, for the early computational detection of high-risk SARS-CoV-2 variants.
• To assess the effectiveness, robustness, and generalizability of HiRisk-Detector using a large dataset of viral genomes.
• To demonstrate the utility of HiRisk-Detector in identifying emerging threats, including Omicron sub-lineages.

Main Methods:

• Development of HiRisk-Detector, a machine learning algorithm utilizing haplotype networks for variant analysis.
• Validation using over 7.6 million high-quality SARS-CoV-2 genomes and associated metadata.
• Performance evaluation through empirical data analysis, simulated reduced sequencing intensity, and application to Omicron sub-lineages.

Main Results:

• HiRisk-Detector successfully identified all 13 high-risk SARS-CoV-2 variants, on average 27 days before World Health Organization declarations.
• The algorithm maintained effectiveness with reduced sequencing data, showing only a minor delay of 3.8 days.
• High performance metrics (ROC-AUC and PR-AUC) were achieved when applied to SARS-CoV-2 Omicron variant sub-lineages.

Conclusions:

• HiRisk-Detector offers a powerful computational tool for the early detection of high-risk SARS-CoV-2 variants.
• The algorithm's speed and accuracy provide significant advantages over traditional detection methods for epidemic preparedness.
• HiRisk-Detector has broad applicability for public health emergencies involving novel infectious agents.