Emerging dominant SARS-CoV-2 variants

TL;DR

This paper presents AI models integrating biophysics, genotyping, experimental data, algebraic topology, and deep learning to forecast emerging SARS-CoV-2 variants, aiding preparedness for future infection waves.

Contribution

It introduces a novel AI framework that accurately predicts dominant SARS-CoV-2 variants using multidisciplinary data and analysis of viral mutations and evolution.

Findings

Forecasted Omicron variants BA.1, BA.2, BA.4/BA.5 accurately

Identified potential new dominant variants BA.2.75+R346T and others

Analyzed viral evolution mechanisms through infectivity and antibody resistance

Abstract

Accurate and reliable forecasting of emerging dominant severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants enables policymakers and vaccine makers to get prepared for future waves of infections. The last three waves of SARS-CoV-2 infections caused by dominant variants Omicron (BA.1), BA.2, and BA.4/BA.5 were accurately foretold by our artificial intelligence (AI) models built with biophysics, genotyping of viral genomes, experimental data, algebraic topology, and deep learning. Based on newly available experimental data, we analyzed the impacts of all possible viral spike (S) protein receptor-binding domain (RBD) mutations on the SARS-CoV-2 infectivity. Our analysis sheds light on viral evolutionary mechanisms, i.e., natural selection through infectivity strengthening and antibody resistance. We forecast that BA.2.10.4, BA.2.75, BQ.1.1, and particularly, BA.2.75+R346T, have high potential to become new dominant variants to drive the next surge.