New Virus Variant Detection Based on the Optimal Natural Metric

TL;DR

This paper introduces a new algorithm for automatically detecting new virus variants using an optimal natural metric, improving the speed and accuracy of variant identification.

Contribution

A novel algorithm for automatic variant detection using an alignment-free optimal natural metric and hypothesis testing.

Findings

The method achieves nearly 100% precision in identifying new SARS-CoV-2 and HIV-1 variants.

It successfully detects novel genera in Orthocoronavirinae.

The approach enables timely surveillance of emerging viral threats.

Abstract

The highly variable SARS-CoV-2 virus responsible for the COVID-19 pandemic frequently undergoes mutations, leading to the emergence of new variants that present novel threats to public health. The determination of these variants often relies on manual definition based on local sequence characteristics, resulting in delays in their detection relative to their actual emergence. In this study, we propose an algorithm for the automatic identification of novel variants. By leveraging the optimal natural metric for viruses based on an alignment-free perspective to measure distances between sequences, we devise a hypothesis testing framework to determine whether a given viral sequence belongs to a novel variant. Our method demonstrates high accuracy, achieving nearly 100% precision in identifying new variants of SARS-CoV-2 and HIV-1 as well as in detecting novel genera in Orthocoronavirinae. This approach holds promise for timely surveillance and management of emerging viral threats in the field of public health.

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