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Related Experiment Video

Updated: Aug 15, 2025

Author Spotlight: A Pseudotype Virus System for Assessing Omicron Subvariants and Neutralizing Antibodies in SARS-CoV-2 Research

Author Spotlight: A Pseudotype Virus System for Assessing Omicron Subvariants and Neutralizing Antibodies in SARS-CoV-2 Research

Published on: September 8, 2023

Predicting emerging SARS-CoV-2 variants of concern through a One Class dynamic anomaly detection algorithm.

Giovanna Nicora^1,2, Marco Salemi³, Simone Marini⁴

¹Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.

BMJ Health & Care Informatics

|January 3, 2023

Summary

This study introduces an automated method using machine learning to detect new SARS-CoV-2 variants, including variants of concern (VOC) and variants of interest (VOI), significantly faster than official classifications.

Keywords:

COVID-19 data science machine learning public health informatics

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Area of Science:

Virology
Genomics
Computational Biology

Background:

The emergence of SARS-CoV-2 variants necessitates rapid detection methods.
Traditional variant identification relies on epidemiological indicators, often with a time lag.

Purpose of the Study:

To implement an automated system for weekly detection of novel SARS-CoV-2 variants.
To identify non-neutral variants, including variants of concern (VOC) and variants of interest (VOI), early in their emergence.

Main Methods:

Utilized spike protein sequences from GISAID, represented as k-mer counts.
Employed a One Class Support Vector Machine (SVM) classification algorithm trained on neutral sequences.
Evaluated sequences weekly for anomalous patterns indicative of new variants since July 2020.

Main Results:

The One Class SVM classifier successfully identified known VOCs and VOIs (e.g., Alpha, Delta, Omicron) before official designation.
On average, the classifier detected non-neutral variants as outliers 10 weeks prior to their official classification.

Conclusions:

Automated surveillance systems leveraging protein sequence data can significantly enhance early variant identification.
Machine learning, specifically One Class SVM, offers a valuable tool for variant detection during evolving pandemics.