To evidence elimination of hepatitis C virus (HCV), countries need to demonstrate that incidence is ≤2/100 person years in people who inject drugs (PWID). Ongoing prospective cohort studies (gold-standard method) may be too expensive and unsustainable. Here, we first summarise the utility of public health surveillance data, including bio-behavioural surveys, laboratory testing, and treatment registers with additional linkage to administrative data to monitor HCV incidence or related measures. Second, we present five modelling approaches that could be used to generate evidence for assessing progress towards elimination: 1) dynamic deterministic, 2) methods for estimating the force of infection, 3) multi-parameter evidence synthesis (MPES) with back-calculation, 4) Bayesian hierarchical, and 5) cohort multi-state. Some of these approaches can generate modelled incidence estimates based on trends in chronic HCV prevalence, while others focus on related measures such as the decline in chronic HCV prevalence, or the total number of chronic HCV infections that have been diagnosed. We propose that multiple different measures of progress are possible and together, can provide evidence on whether the UK has achieved elimination.