More or less deadly? A mathematical model that predicts SARS-CoV-2 evolutionary direction

Area of Science:

• Virology
• Mathematical Biology
• Epidemiology

Background:

• Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused significant global mortality.
• Predicting the evolutionary trajectory of SARS-CoV-2 is crucial for public health.
• Understanding the impact of mutations on viral characteristics is essential.

Purpose of the Study:

• To propose a novel mathematical model for predicting SARS-CoV-2 evolutionary trends.
• To investigate the effects of mutations on viral assembly capacity and dynamics within a host.
• To quantify and understand the interplay between viral virulence and transmissibility.

Main Methods:

• Development of a robust coarse-grained mathematical model to simulate virus dynamics.
• Quantification of virulence and transmissibility parameters within the model.
• Construction of a virus assembly model to simulate the particle packing process.

Main Results:

• The model predicts a potential decrease in SARS-CoV-2 virulence with enhanced transmissibility, reaching a stable equilibrium.
• Viral virulence is not expected to disappear entirely but will stabilize within a certain range.
• The model explains how minor mutations can lead to significant variations in clinical presentation by affecting particle formation and virulence.

Conclusions:

• Mathematical modeling offers a novel approach to understanding RNA virus evolution.
• The study provides insights into the evolutionary driving forces of SARS-CoV-2.
• The findings highlight the complex relationship between viral mutations, assembly, and pathogenicity.