A simulation framework for modeling the within-patient evolutionary dynamics of SARS-CoV-2
John W Terbot1,2, Brandon S Cooper2, Jeffrey M Good2
1Arizona State University, School of Life Sciences, Center for Evolution & Medicine, Tempe, Arizona, United States of America.
Biorxiv : the Preprint Server for Biology
|July 28, 2023
Summary
This study models Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) evolution within patients. Plausible models reveal severe infection bottlenecks and strong negative selection, aiding early variant detection.
Area of Science:
- Virology
- Evolutionary Biology
- Genomics
Background:
- Detecting SARS-CoV-2 variants of concern (VOCs) requires understanding intra-host genomic changes.
- Existing models lack integrated evolutionary factors for accurate SARS-CoV-2 intra-host dynamics.
Approach:
- Developed a simulation framework to model patient-level SARS-CoV-2 variation.
- Evaluated 12,096 parameter combinations against empirical data to identify plausible models.
Key Points:
- 592 models (~5%) were plausible, indicating severe infection bottlenecks.
- Low reproductive skew and a high proportion of deleterious mutations characterize intra-host evolution.
- Identified areas of model uncertainty and data needs for refinement.
Conclusions:
- The study establishes a baseline model for SARS-CoV-2 intra-host evolutionary dynamics.
- This framework improves the analysis of patient-level genomic data for early VOC detection.
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