New Virus Variant Detection Based on the Optimal Natural Metric

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.

Keywords: SARS-CoV-2; natural vectors; new virus detection; optimal metric.

Figure A1

Distribution of $I_{in}$ and $I_{out}$ (HIV-1).

Figure A2

Distribution of $I_{in}$ and $I_{out}$ (Orthocoronavirinae).

Figure 1

Flowchart of Algorithm 1.

Figure 2

Distribution of $I_{in}\left(Alpha\right)$ and $I_{out}(·,Alpha)$ (SARS-CoV-2).

Figure 3

Distribution of $I_{in}$ and $I_{out}$ (SARS-CoV-2).

Figure 4

Scatter plot of $Di{s}^R$ distribution for four types of viruses: A, B, A1B, 03_A6B.