An evolutionary theory on virus mutation in COVID-19

Area of Science:

• Virology
• Evolutionary Biology
• Computational Biology

Background:

• The rapid evolution of SARS-CoV-2 necessitates understanding the mechanisms driving new strain emergence.
• Identifying conditions conducive to viral mutation and adaptation is critical for public health.

Purpose of the Study:

• To develop a theoretical framework for analyzing SARS-CoV-2 mutations and predicting new strain generation.
• To establish a computational model for forecasting viral evolution and macro-lineage emergence.

Main Methods:

• Representing virus variants using 4-letter amino acid mutation sequences on the spike protein.
• Employing an n-distance algorithm to construct a variant phylogenetic tree.
• Proposing an A-X model integrating existing mutation sites (A) with random sites (X) to calculate new strain emergence.

Main Results:

• The theoretically derived phylogenetic tree shows strong alignment with experimental data on SARS-CoV-2 evolution.
• The A-X model demonstrates that sufficient random iterations predict new macro-lineage generation when the size of X is adequate.
• The study provides a predictive capability for the emergence of significant viral variants.

Conclusions:

• The presented evolutionary theory offers a robust foundation for understanding SARS-CoV-2 diversification.
• The A-X model serves as a valuable tool for anticipating future viral strain development.
• These findings are crucial for proactive strategies in managing viral evolution and pandemics.