Introduction

GLEaM is a Global Epidemic and Mobility modeler that integrates sociodemographic and population mobility data in a spatially structured stochastic disease approach to simulate the spread of epidemics at the worldwide scale. Read more about GLEaM.

The GLEaMviz project covers the research conducted with GLEaM as well as the tools derived from it. This website reports on the progress of this project, its main results, publications of academic papers and editorial material, presentations at international conferences and workshops, and other outreach activities.

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GLEaMviz in Big Data in a Living Web Conference

December 7th, 2011

GLEaMviz appeared today at the Top-IX Annual Conference Big Data in a Living Web in the keynote presentation by Alex Vespignani and in the invited talk by Marco Quaggiotto.

top-ix conference 2011

Can we foresee the start of a conflict? can we anticipate the spread of a pandemic pathogen? can we assess the systemic risk and the impact that a financial crisis, a nuclear disaster, or a pandemic event will have on our society and our planet?

GLEaMviz was presented by Alex as an example product of the Big Data Revolution, where high-resolution data on human behavior can be integrated into a disease model to anticipate the geographical and temporal spread of a pandemic influenza.

Alex @Top-IX2011 -1 Alex @Top-IX2011 -2

Marco presented the perspective of shaping the data produced by GLEaMviz into informative visualizations that help making sense of the simulated scenario and guiding the analysis of the epidemic pattern.

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The Conference also saw invited talks by Alan Mislove from Northeastern University in Boston, USA, and Cesar Hidalgo from MIT in Boston, USA.
We thank Top-IX, torino piemonte internet exchange, for the invitation to the event.

New release: GLEaMviz Simulator v2.8

October 24th, 2011

We are pleased to announce the release of GLEaMviz Simulator v2.8. The principal new feature in this release is the ability to specify time-dependent overrides of the default values for variables defined in the compartmental model. Such overrides can be used to model time-dependent changes in the infection dynamics of the epidemic such as the effect of mitigation policies on the disease parameters.

A new panel in the Simulation Wizard, shown in the figure below, enables the modeler to add one or more time-dependent variable overrides. Each override applies for one selected variable and is valid from the selected start date up to and including the selected end date. The overriding value is specified as an algebraic expression that may include references to other variables, just like a default value for variables or transition rates expressions.
Starting from this release it is now also possible to use parenthesis in such expressions.

New panel in the Simulation Wizard that enables the modeler to specify time-dependent variable overrides.

New panel in the Simulation Wizard that enables the modeler to specify time-dependent variable overrides.

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