Throughout the course of the SARS-CoV-2 pandemic, genetic variation has contributed to the spread and persistence of the virus. We compared two methods that estimate the fitness effects of viral mutations using the abundant sequence data gathered over the course of the pandemic. We characterized differences in the distributions of fitness values inferred by each approach and in the ranks of fitness values that they assign to sequences across time. We find that in a large fraction of weeks the two methods are in good agreement as to their top-ranked sequences, i.e., as to which sequences observed that week are most fit. We also find that agreement between ranking of sequences varies with genetic unimodality in the population in a given week.
The talk is based on joint work with Hong-Li Zeng, Cheng-Long Yang, Bo Jing and John Barton, available as arXiv:2403.14202