Human mobility is a critical driver of epidemics by substantially altering the probability of encounters, patterns of exposure, and the likelihood of disease propagation. While long-range movements may shape patterns of pathogen importation, short-range mobility and contact structures amplify local epidemics. Characterizing mobility patterns and social mixing across scales is therefore essential for understanding why and how epidemics emerge and spread, as well as for developing effective prevention and control strategies. The COVID-19 crisis, sparked a data-sharing revolution, with network operators such as Orange and Telefonica, along with tech giants like Google, Apple, and Facebook, providing real-time aggregated mobility data from mobile phone traces to track human mobility and help fight the pandemic. Epidemiological research is now focused on developing novel mathematical and computational frameworks to integrate high-resolution mobility data into models, enabling both retrospective analyses and real-time epidemic monitoring. In my talk, I will discuss how we utilized these data during the early stages of COVID-19 in France to capture the dynamic shifts in social mixing caused by mobility interventions and address critical public health questions. Additionally, I will present a retrospective theoretical study that characterizes the mobility factors shaping geographical diffusion across scales in the United States and demonstrates a model designed to optimize reliability for outbreak response while balancing mobility data requirements.