  Throughout the course of the SARS-CoV-2 pandemic, genetic variation has
contributed to the spread and persistence of the virus. For example, various
mutations have allowed SARS-CoV-2 to escape antibody neutralization or to bind
more strongly to the receptors that it uses to enter human cells. Here, we
compared two methods that estimate the fitness effects of viral mutations using
the abundant sequence data gathered over the course of the pandemic. Both
approaches are grounded in population genetics theory but with different
assumptions. One approach, tQLE, features an epistatic fitness landscape and
assumes that alleles are nearly in linkage equilibrium. Another approach, MPL,
assumes a simple, additive fitness landscape, but allows for any level of
correlation between alleles. 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.
