New publication describing the data integration, modeling schemes and algorithmic implementations of GLEaM

The components lying at the core of the GLEaM simulator are presented extensively in the new publication:

Modeling the spatial spread of infectious diseases: The GLobal Epidemic and Mobility computational model
Duygu Balcan, Bruno Goncalves, Hao Hu, Jose J Ramasco, Vittoria Colizza and Alessandro Vespignani
Journal of Computational Science 2010, 1: 132-145.

Illustration of the procedure used for the GLEaM simulator. Left column represents input databases and right column the data structures generated. Program flow occurs in the center.

Illustration of the procedure used for the GLEaM simulator. Left column represents input databases and right column the data structures generated. Program flow occurs in the center.

The GLEaM model is developed and used intensively in the context of emerging infectious diseases. The model combines infection dynamics with long- and short-range human mobility. Each of these processes operates on a different time scale, which obviously poses theoretical and computational challenges. In this paper it is presented in detail how GLEaM overcomes this problem by using a time-scale separation technique in which the short-time dynamics is integrated into an effective force of infection. The modeling framework is robust and flexible allowing for the integration of new features. The simulator is implemented in a modular way. Each module performs a single function, and can be combined in different ways to include or remove specific features. In order to achieve refined analysis, including the impact of an epidemic on different age groups, this manuscript demonstrates how to generalize the basic formalism in order to take into account different contact rates among individuals belonging to different age groups. The algorithmic structure of the GLEaM simulator and the implementations at all stages are also illustrated in detail for the first time in this publication.

While the GLEaM model is developed and tested in the context of emerging infectious diseases, it considers different transportation and interaction layers and distinguishes the mobility modeling from dynamical processes mediated by human dynamics. This allows for the integration of different processes of social contagion that are not necessarily of biological origin but that occur via individuals’ mobility.

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