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Among the realistic ingredients to be considered in the computationalmodeling of infectious diseases, human mobility represents a crucial challengeboth on the theoretical side and in view of the limited availability ofempirical data. In order to study the interplay between small-scale commutingflows and long-range airline traffic in shaping the spatio-temporal pattern ofa global epidemic we i) analyze mobility data from 29 countries around theworld and find a gravity model able to provide a global description ofcommuting patterns up to 300 kms; ii) integrate in a worldwide structuredmetapopulation epidemic model a time-scale separation technique for evaluatingthe force of infection due to multiscale mobility processes in the diseasedynamics. Commuting flows are found, on average, to be one order of magnitudelarger than airline flows. However, their introduction into the worldwide modelshows that the large scale pattern of the simulated epidemic exhibits onlysmall variations with respect to the baseline case where only airline trafficis considered. The presence of short range mobility increases however thesynchronization of subpopulations in close proximity and affects the epidemicbehavior at the periphery of the airline transportation infrastructure. Thepresent approach outlines the possibility for the definition of layeredcomputational approaches where different modeling assumptions and granularitiescan be used consistently in a unifying multi-scale framework.

Multiscale mobility networks and the large scale spreading of infectious diseases