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José J. Ramasco

Researcher at Spanish National Research Council

Publications -  178
Citations -  11449

José J. Ramasco is an academic researcher from Spanish National Research Council. The author has contributed to research in topics: Complex network & Computer science. The author has an hindex of 42, co-authored 161 publications receiving 9837 citations. Previous affiliations of José J. Ramasco include University of the Balearic Islands & University of Cantabria.

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Multiscale mobility networks and the spatial spreading of infectious diseases

TL;DR: In this paper, the authors study the interplay between short-scale commuting flows and long-range airline traffic in shaping the spatio-temporal pattern of a global epidemic.
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Finding Statistically Significant Communities in Networks

TL;DR: OSLOM (Order Statistics Local Optimization Method), the first method capable to detect clusters in networks accounting for edge directions, edge weights, overlapping communities, hierarchies and community dynamics, is presented.
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Human mobility: Models and applications

TL;DR: This survey reviews the approaches developed to reproduce various mobility patterns, with the main focus on recent developments, and organizes the subject by differentiating between individual and population mobility and also between short-range and long-range mobility.
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Human mobility networks, travel restrictions, and the global spread of 2009 H1N1 pandemic

TL;DR: A comprehensive computational and theoretical study of the role of travel restrictions in halting and delaying pandemics by using a model that explicitly integrates air travel and short-range mobility data with high-resolution demographic data across the world and that is validated by the accumulation of data from the 2009 H1N1 pandemic.
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Modeling the spatial spread of infectious diseases: The GLobal Epidemic and Mobility computational model

TL;DR: The flexible structure of the model that is open to the inclusion of different disease structures and local intervention policies makes GLEaM suitable for the computational modeling and anticipation of the spatio-temporal patterns of global epidemic spreading.