Genetic drift acts strongly on within-host influenza virus populations during acute infection but does not act alone.

The evolutionary dynamics of seasonal influenza A viruses (IAVs) have been well characterized at the population level, with antigenic drift known to be a major force in driving strain turnover. The evolution of IAV populations at the within-host level, however, is still less well characterized. Improving our understanding of within-host IAV evolution has the potential to shed light on the source of new strains, including new antigenic variants, at the population level. Existing studies have pointed towards the role that stochastic processes play in shaping within-host viral evolution in acute infections of both humans and pigs. Here, we apply a population genetic model called the 'Beta-with-Spikes' approximation to longitudinal intrahost Single Nucleotide Variant (iSNV) frequency data to quantify the extent of genetic drift acting on IAV populations at the within-host scale. We estimate small effective population sizes in both human IAV infections (, , 95% confidence interval: [22-72]) and swine IAV infections ( , 95% confidence interval: [8-14]). Moreover, we evaluate the consistency of the observed iSNV dynamics with Wright-Fisher model simulations. For the human IAV dataset that we analyze, we find that observed within-host IAV evolutionary dynamics are consistent with this classic model at the estimated low effective population size. However, for the swine IAV dataset, we find statistical evidence for rejecting the classic Wright-Fisher model as the only process governing within-host iSNV frequency dynamics. Our results contribute to the growing number of studies that point towards the important role of genetic drift in shaping patterns of genetic diversity in IAV populations within acutely infected hosts. It further raises questions about whether and what other processes, such as spatial compartmentalization, viral progeny production dynamics with strong skew, or selection, may be needed to explain patterns of within-host IAV evolution.