<p>This paper introduces a novel framework that integrates agent-based mobility simulation with social network analysis to model epidemic diffusion, using Las Vegas as a high-stakes test bed. By mining real-world transportation, demographic, and geographic data, we construct a high-resolution, dynamic contact network that captures the heterogeneous interactions among key subpopulations. Applying graph theory and diffusion modeling to this evolving network demonstrates that epidemic risk is structurally determined by critical transmission hotspots (major resorts, transport hubs) and is highly sensitive to the initial seeding location, with an introduction at the airport leading to a far more explosive outbreak than one in a residential zone. We demonstrate how distinct population groups play unique network roles: tourists act as primary importation vectors, while hospitality workers serve as crucial bridging nodes between otherwise disconnected clusters. The model also shows how some agents achieve “immunity by circumstance” due to mobility patterns that isolate them from high-traffic areas. Our experiments prove that targeted interventions, such as capacity limits at identified hotspots, substantially outperform universal restrictions. The framework validated in Las Vegas provides a transferable tool for strategic epidemic preparedness, offering a new paradigm for data-driven public health policy applicable to any globally connected urban center.</p>