RNA viruses exhibit high mutation rates due to the lack of proofreading mechanisms during replication, leading to diverse intra-host viral populations. Variants with higher fitness tend to dominate the population due to enhanced transmissibility and immune escape. Fitness of viral variants depends on individual SNVs and epistatic links between pairs of SNVs as well as competition with other viral variants within the population. Recent machine learning methods have successfully predicted emerging COVID-19 variants based on epistatic SNV links, implying that SNV links contribute to fitness of viral variants. We define the epistatic density of a viral variant as the number of positively linked SNV pairs between mutated positions in its genome. We computed epistatic density of intra-host Hepatitis C Virus (HCV) populations sampled from 85 chronic and 28 acute patients with HCV 1a genotypes. On average, epistatic density was higher in chronic patients than in acute cases. Additionally, the epistatic density distributions are more irregular and choppy in acute populations. Finally, we applied the epistatic density properties to distinguish between intra-host populations of chronic and acute HCV patients.