Developing an appropriate evolutionary baseline model for the study of SARS-CoV-2 patient samples.

Area of Science:

• Virology
• Evolutionary Biology
• Genomic Surveillance

Background:

• Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused a global health crisis over the past three years.
• Millions of SARS-CoV-2 isolates are available in public databases, facilitating genomic surveillance.
• Identifying emerging viral variants is challenging due to complex, interacting evolutionary processes.

Purpose of the Study:

• To outline critical components of an evolutionary baseline model for SARS-CoV-2.
• To describe the current state of knowledge on SARS-CoV-2 evolutionary parameters.
• To provide recommendations for future research in clinical sampling, model construction, and statistical analysis.

Main Methods:

• Review and synthesis of current knowledge on SARS-CoV-2 evolutionary processes.
• Identification of key model components: mutation rates, recombination rates, distribution of fitness effects, infection dynamics, and compartmentalization.
• Analysis of the state of knowledge for each parameter in SARS-CoV-2.

Main Results:

• Multiple evolutionary processes, including mutation, recombination, fitness effects, infection dynamics, and compartmentalization, must be jointly modeled for accurate SARS-CoV-2 inference.
• The current understanding of these parameters in SARS-CoV-2 is described.
• Specific knowledge gaps and areas for future research are highlighted.

Conclusions:

• Accurate genomic surveillance of SARS-CoV-2 requires a comprehensive evolutionary baseline model.
• Further research is needed to refine our understanding of mutation rates, recombination, fitness effects, infection dynamics, and compartmentalization.
• Recommendations are provided for improving clinical sampling, model development, and statistical analysis to enhance SARS-CoV-2 tracking and prediction.