OBJECTIVES: The cost of new hepatitis C virus (HCV) direct-acting antiviral treatments in the UK is high, so there is intense focus on identifying the most cost-effective treatment strategies. Modelling evidence suggests that treating those at risk of onwards transmission (such as people who inject drugs, PWID) may provide population-level prevention benefits. However, in the UK, health technology assessment submissions for HCV treatments have mainly modelled cohorts of non-interacting patients. The purpose of this study was to understand the impact of incorporating both reinfection risk and prevention benefits on treatment cost-effectiveness. METHODS: A dynamic transmission model (DTM) of HCV disease progression among ex or non-PWID, and disease progression and transmission among active PWID was built. The model focused on HCV treatment for genotype 3 (G-3) patients in the UK. The model was built to mirror a recent manufacturer’s model for a new HCV treatment but to additionally incorporate reinfection and the population prevention benefits among PWID. Costs (in 2014 GBP) and health utilities attached to each model state. A baseline chronic HCV G-3 prevalence of 16.5% was assumed among PWID. RESULTS: The DTM showed that the proportion of the population treated is an important cost-effectiveness driver. The higher the proportion treated, the more cost-effective the intervention, although the degree of improvement depended on the efficacy of the comparator chosen. In addition, as the baseline chronic HCV G-3 prevalence was increased, cost-effectiveness decreased. Cost-effectiveness estimates from the DTM were improved compared to those reported from the manufacturer’s Markov model. CONCLUSIONS: Depending on the proportion of the population treated, DTMs may lead to different cost-effectiveness conclusions because they depend on the proportion treated and the disease prevalence. These effects cannot be easily explored through the use of Markov models built to estimate costs and health benefits for a non-interacting cohort of patients.