A Simulation Framework for Modeling the Within-Patient Evolutionary Dynamics of SARS-CoV-2

Area of Science:

• Virology
• Genomics
• Evolutionary Biology

Background:

• The emergence of SARS-CoV-2 variants of concern (VOCs) necessitates methods for early detection of beneficial mutations.
• Accurate detection of genomic changes within patient samples is vital for tracking VOC development.
• Existing models lack comprehensive integration of evolutionary factors influencing SARS-CoV-2 intrahost dynamics.

Purpose of the Study:

• To develop a simulation framework for modeling SARS-CoV-2 intrahost evolutionary dynamics.
• To establish baseline expectations for patient-level genomic variation.
• To identify key parameters and patterns governing within-host viral evolution.

Main Methods:

• Developed a simulation framework to model intrahost SARS-CoV-2 evolution.
• Varied eight key parameters, evaluating 12,096 model-parameter combinations.
• Compared simulation outputs with empirical data to identify plausible models.

Main Results:

• Identified 592 plausible models (approximately 5%) out of 12,096 evaluated.
• Plausible models indicate severe infection bottlenecks and low reproductive skew.
• Observed a fitness effect distribution skewed towards strongly deleterious mutations.

Conclusions:

• The study provides a foundational model for understanding SARS-CoV-2 intrahost evolution.
• Severe bottlenecks and deleterious mutations are key features of within-host viral dynamics.
• Highlights areas for model refinement and the need for additional sequence data for improved VOC detection.