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Emerging Dominant SARS-CoV-2 Variants.

Jiahui Chen1, Rui Wang1, Yuta Hozumi1

  • 1Department of Mathematics, Michigan State University, Lansing, Michigan 48824, United States.

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|December 28, 2022
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Summary
This summary is machine-generated.

Artificial intelligence models accurately forecast emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. New dominant strains like BN.1* are predicted to drive future infection surges, aiding preparedness.

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Area of Science:

  • Virology and Bioinformatics
  • Computational Biology and Artificial Intelligence

Background:

  • Forecasting emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants is crucial for public health preparedness and vaccine development.
  • Previous artificial intelligence (AI) models accurately predicted the dominance of Omicron (BA.1), BA.2, and BA.4/BA.5 variants.

Purpose of the Study:

  • To analyze the impact of all possible spike (S) protein receptor-binding domain (RBD) mutations on SARS-CoV-2 infectivity using newly available experimental data.
  • To forecast potential future dominant SARS-CoV-2 variants capable of driving new infection waves.

Main Methods:

  • Development of AI models integrating biophysics, viral genome genotyping, experimental data, algebraic topology, and deep learning.
  • Analysis of experimental data to assess the infectivity effects of all potential SARS-CoV-2 S protein RBD mutations.
  • Forecasting of emerging variants based on predicted infectivity and evolutionary mechanisms.

Main Results:

  • The study identified key viral evolutionary mechanisms, including natural selection driven by enhanced infectivity and antibody resistance.
  • Forecasted variants BP.1, BL*, BA.2.75*, BQ.1*, and especially BN.1* show high potential for future dominance.
  • The projection made on October 18, 2022, accurately predicted the emergence of these dominant variants by late November 2022.

Conclusions:

  • AI-driven analysis of viral mutations provides reliable forecasting of emerging SARS-CoV-2 variants.
  • The identified variants, particularly BN.1*, pose a significant threat for future infection surges.
  • This predictive capability allows for timely public health interventions and vaccine strategy adjustments.