Understanding the spatial and temporal dynamics of infectious disease transmission is critical for effective epidemic preparedness and response. COVID-19 transmission is influenced by mobility patterns, regional connectivity, and evolving public health interventions, making it challenging to quantify region-specific transmission risks. Our study integrates intervention-driven analysis, real-world data, and high-resolution modeling to establish a robust computational framework for assessing interregional transmission dynamics. We employ a multi-patch model to estimate the time-dependent regional effective reproduction number and systematically quantify interregional infection contributions. By integrating high-resolution mobility and COVID-19 incidence data from South Korea, we identify key transmission hubs and assess the impact of mobility-driven transmission across different epidemic phases. Our results highlight Seoul and Gyeonggi as dominant sources of interregional spread, with their influence varying across phases of the pandemic. By distinguishing locally transmitted infections from mobility-induced cases, we introduce a data-driven approach to evaluate the effectiveness of movement restrictions and targeted interventions. Findings from the Pre-Delta phase demonstrate that mobility controls in transmission hubs significantly reduced the spread of infections. Our results underscore that densely connected regions disproportionately drive nationwide transmission, emphasizing the need for adaptive, phase-dependent intervention strategies rather than uniform nationwide policies. This study advances computational epidemiology by providing a scalable framework for integrating real-world mobility data with epidemic modeling to inform targeted, data-driven public health responses.