Predicting the trend of SARS-CoV-2 mutation frequencies using historical data.

ABSTRACT:

Motivation

As the SARS-CoV-2 virus rapidly evolves, predicting the trajectory of viral mutations has become a critical yet complex task. A deep understanding of future mutation patterns, in particular the mutations that will prevail in the near future, is vital in steering diagnostics, therapeutics, and vaccine strategies for disease control.

Results

In this study, we developed a model to forecast future SARS-CoV-2 mutation surges in real-time, using historical mutation frequency data from the USA. We transformed the temporal prediction problem into a supervised learning framework using a sliding window approach. This involved breaking the time series of mutation frequencies into very short segments. Considering the time-dependent nature of the data, we focused on modeling t

SUBMITTER: Zhou X 

PROVIDER: S-EPMC12502910 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature