<p>Epidemic models are essential tools for evaluating Non-Pharmaceutical Interventions (NPIs) aimed at controlling infectious disease spread. Accurately modelling contact networks is particularly important for evaluating NPIs that reduce person-to-person contact, such as school closures and travel restrictions. However, contact structure alone is insufficient to capture realistic disease transmission dynamics. In particular, heterogeneity in individual compliance with risk-reduction measures, such as mask-wearing, significantly influences outcomes and warrants careful consideration. Accordingly, we present SW-SEIQR, an individual-based epidemic model in which individuals are represented as nodes of a Watts–Strogatz small-world contact network. Parametrised by the mean degree and rewiring probability, the network captures both local and non-local interactions, enabling the evaluation of contact-reduction NPIs. Compliance heterogeneity is incorporated through individual-specific compliance parameters that modulate transmission probability according to the adherence levels of both the individual and their infectious contacts to risk-reduction measures. Simulation experiments were conducted to assess the impact of key model parameters and corresponding NPIs on epidemic dynamics. The results highlight that flattening the epidemic curve requires combining contact-reduction measures with interventions that reduce transmission risk. Furthermore, intervention effectiveness depends not only on the proportion of compliant individuals but, more importantly, on the efficacy of the adopted protective measures. The results indicate that highly effective risk-reduction measures, such as high-quality mask use, can substantially reduce disease transmission even when adopted by only a fraction of the population.</p>