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

• Published in: PLoS pathogens Vol. 19; no. 4; p. e1011265
• Main Authors: Terbot, 2nd, John W, Johri, Parul, Liphardt, Schuyler W, Soni, Vivak, Pfeifer, Susanne P, Cooper, Brandon S, Good, Jeffrey M, Jensen, Jeffrey D
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.04.2023; Public Library of Science (PLoS)
• Subjects: Analysis; Biology and life sciences; Coronaviruses; COVID-19; Estimates; Evolution; Evolutionary biology; Genetic diversity; Genetic variation; Genomes; Genomics; Global health; Health aspects; Human populations; Humans; Infections; Mathematical models; Medicine and health sciences; Methods; Mutation; Mutation rates; Public health; Recombination; Review; SARS-CoV-2; Severe acute respiratory syndrome; Severe acute respiratory syndrome coronavirus 2; Statistical analysis; Viral diseases; Viruses; United States; United States > US; Montana
• ISSN: 1553-7374; 1553-7366; 1553-7374
• DOI: 10.1371/journal.ppat.1011265

Over the past 3 years, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has spread through human populations in several waves, resulting in a global health crisis. In response, genomic surveillance efforts have proliferated in the hopes of tracking and anticipating the evolution of this virus, resulting in millions of patient isolates now being available in public databases. Yet, while there is a tremendous focus on identifying newly emerging adaptive viral variants, this quantification is far from trivial. Specifically, multiple co-occurring and interacting evolutionary processes are constantly in operation and must be jointly considered and modeled in order to perform accurate inference. We here outline critical individual components of such an evolutionary baseline model-mutation rates, recombination rates, the distribution of fitness effects, infection dynamics, and compartmentalization-and describe the current state of knowledge pertaining to the related parameters of each in SARS-CoV-2. We close with a series of recommendations for future clinical sampling, model construction, and statistical analysis.