This paper investigates the role of multiscale mobility networks in the global spread of infectious diseases. The authors analyze mobility data from 29 countries to understand how small-scale commuting flows and long-range airline traffic influence the spatio-temporal patterns of an epidemic. They develop a gravity model that captures commuting patterns globally, up to 300 km, and integrate it into a structured metapopulation epidemic model to evaluate the force of infection due to multiscale mobility processes. The results show that while commuting flows are one order of magnitude larger than airline flows, the global epidemic patterns are mainly determined by the airline network. However, short-range commuting interactions increase synchronization among nearby subpopulations and affect epidemic behavior at the periphery of the airline network. The study highlights the importance of considering different mobility scales in epidemic modeling and proposes a layered approach that integrates various modeling assumptions and granularities in a unified multi-scale framework. The authors also develop a time-scale separation technique to evaluate the force of infection due to different mobility couplings. The results from simulations show that the inclusion of commuting flows has a limited impact on the overall epidemic pattern but plays a significant role in the synchronization of local epidemic profiles. The study demonstrates that the level of detail in mobility networks can be chosen based on the scale of interest, and that neglecting local coupling does not significantly affect global patterns. The findings have important implications for the development of predictive large-scale data-driven epidemic models and for the integration of real-world data in computational modeling of infectious diseases.This paper investigates the role of multiscale mobility networks in the global spread of infectious diseases. The authors analyze mobility data from 29 countries to understand how small-scale commuting flows and long-range airline traffic influence the spatio-temporal patterns of an epidemic. They develop a gravity model that captures commuting patterns globally, up to 300 km, and integrate it into a structured metapopulation epidemic model to evaluate the force of infection due to multiscale mobility processes. The results show that while commuting flows are one order of magnitude larger than airline flows, the global epidemic patterns are mainly determined by the airline network. However, short-range commuting interactions increase synchronization among nearby subpopulations and affect epidemic behavior at the periphery of the airline network. The study highlights the importance of considering different mobility scales in epidemic modeling and proposes a layered approach that integrates various modeling assumptions and granularities in a unified multi-scale framework. The authors also develop a time-scale separation technique to evaluate the force of infection due to different mobility couplings. The results from simulations show that the inclusion of commuting flows has a limited impact on the overall epidemic pattern but plays a significant role in the synchronization of local epidemic profiles. The study demonstrates that the level of detail in mobility networks can be chosen based on the scale of interest, and that neglecting local coupling does not significantly affect global patterns. The findings have important implications for the development of predictive large-scale data-driven epidemic models and for the integration of real-world data in computational modeling of infectious diseases.