GLEaM in detail
GLEaM is a discrete stochastic epidemic computational model based on a meta-population approach in which the world is defined in geographical census areas connected in a network of interactions by human travel fluxes corresponding to transportation infrastructures and mobility patterns. The GLEaM 2.0 simulation engine includes a multiscale mobility model integrating different layer of transportation networks ranging from the long range airline connections to the short range daily commuting pattern.
It is based on a highly detailed population database with demographic data in census cells of 15 × 15 minutes covering the entire Earth surface (source: GPWv3–Gridded Population of the World v3, Columbia University). The multiscale mobility model consider the long range airline fluxes to and from the airports listed in the airline IATA database (source: IATA, International Air Transport Association), accounting for more than 99% commercial traffic worldwide. The high level of spatial resolution allows for a subdivision of the worldwide population in terms of geographical census areas according to a Voronoi decomposition of the world surface centered on the IATA airport location (see figure below). In addition to long range travel, GLEaM includes the effect of short range mobility patterns corresponding to ground movements and commuting patterns as effective interactions among subpopulations in neighboring Voronoi tassels (source: data from more than 25 national and International databases).
The infection dynamics takes place inside each geographical census area, and is described by compartmental schemes in which the discrete stochastic dynamics of the individuals among the different compartments depend on the specific etiology of the disease and the containment interventions considered. Local outbreaks taking place in the geographical census areas are coupled by the mobility (short- and long-range) of individuals.
GLEaM simulation engine provides simulations of global epidemic spreading aimed at understanding historical epidemics, identifying key mechanisms and spreading patterns, assessing the role of human mobility in shaping global epidemics, predicting future scenarios and assessing the efficacy of interventions.
The GLEaM 1.0 simulation engine that considers urban areas coupled by air travel patterns has been used to
- quantify the accuracy and reliability of predictions in case of an emerging infectious disease [PNAS (2006), Bull Math Biol (2006)]
- forecast the next pandemic influenza and strategies to contain it [PLoS (2007)]
- analyze the worldwide spreading pattern of SARS outbreak in 2002-2003 to investigate the predictive power of GLEaM and develop tools for risk assessment scenario analysis [BMC Med (2007)]
GLEaM is coded in C/C++ and runs conveniently on high-end desktop machines.
What is keeping us busy at the moment:
- Introduction of age structure in all world countries.
- Programming interface for the disease model.
The core simulation of GLEaM is coupled to a visualization interface called GLEaMviz.