A quantitative understanding of evolution rests on the analysis of the mutation accumulation process in biological populations, but is largely limited to high-frequency mutations due to the resolution of conventional sequencing technologies. Here, we examine the mutation composition of a poliovirus population over multiple passages using a highly-accurate sequencing strategy, that enables detection of up to 99% of all possible mutations, most of which are present at low-frequency. This data informs a mathematical model describing trajectory patterns of individual mutations to understand the type of interactions shaping population dynamics. We identify mutations consistent with a locus-independent behavior, and others deviating from that simple model by interactions. Clonal interference, followed by hitchhiking, appear to be the most prevalent interactions in the virus population. Epistasis, while presents, but does not significantly affect the distribution of mutational fitness on the short time scale examined in our study. Our study provides a comprehensive analysis of the allelic composition and how mutation rate, fitness, epistasis, clonal interference and hitchhiking influence population dynamics and evolution.