Viral evolution plays a critical role in shaping disease spread dynamics, particularly within heterogeneous human contact networks. Real-world interactions are inherently uneven, with some individuals maintaining numerous connections while others have very few. This heterogeneity profoundly impacts how diseases spread and evolve, especially for rapidly mutating viruses that pose unique challenges by altering their transmissibility mid-outbreak, complicating prediction and control efforts.

To explore this interplay, we developed a simulation framework that captures three key aspects: the spread of infection, the emergence and dominance of more transmissible viral strains through mutation, and the influence of social network structure. Our model examines networks reflecting diverse real-world configurations, such as tightly-knit communities and networks dominated by highly connected "super-spreaders." By analyzing how viral strains compete and evolve within these settings, we aim to uncover actionable patterns to inform public health strategies, particularly for timing interventions to curb outbreaks.

Preliminary results indicate that networks with extensive inter-group connections facilitate faster viral evolution and spread. Furthermore, the emergence of a more transmissible strain not only accelerates disease propagation but also systematically outcompetes and replaces older strains. Future work will investigate the dynamics of mixed and more complex network structures, with the goal of translating these findings into optimized epidemic control strategies.