New publication comparing large-scale computational approaches to epidemic spreading

GLEaM has been used in a new publication comparing the performance of  large-scale computational approaches to  the modeling of infectious disease spreading The detailed results can be found in the manuscript:

Comparing large-scale computational approaches to epidemic modeling: agent-based versus structured metapopulation models
Marco Ajelli, Bruno Gonçalves, Duygu Balcan, Vittoria Colizza, Hao Hu, Jose J Ramasco, Stefano Merler and Alessandro Vespignani
BMC Infectious Diseases 2010, 10:190.

In recent years, two major classes of methodologies emerged in the large-scale and spatial spreading simulation of influenza-like-illnesses (ILIs) and other emerging infectious diseases. The first one is the very accurate epidemic description with agent-based models, which keep track of each individual in the population in an extremely detailed way. The second scheme relies on metapopulation structured models that considers in a detailed way the long range mobility scheme at the inter-population level while using coarse-grained techniques at the intra-population level. It is clearly important to assess the level of agreement that the two different approaches can provide on the quantities accessible in both cases and the respective data needed and computational costs associated.


Snapshots of the epidemic evolution in GLEaM (top) and in the agent-based model (bottom) at three different timesteps of the simulation with R0=1.9. Maps report the average number of cases at the resolution scale of the Italian municipalities.

The paper by Ajelli and co-workers contains the first side-by-side comparison of the results obtained with an Agent-Based model and metapopulation approach offered GLEaM. The two models are carefully calibrated in order to simulate an epidemic described by the same natural history and key parameters.  The country used for the study is Italy, a large European country that provides the necessary geographical and population heterogeneity to assess the models performance in the case of highly structured populations. For the sake of clarity, the two models consider a hypothetical influenza pandemic event for which the same parameterization and initial conditions in the far east. Both models, despite the difference in the data integration and model structure, provide epidemic profiles with spatio-temporal patterns in very good agreement.

The good agreement of the two approaches reinforces the message that computational approaches are stable with respect to different data integration strategies and modeling assumption.The presented results hint to the possibility of combining the two methodologies in order to devise multiscale approaches that use the data parsimony of the metapopulation approaches at the global level and the high resolution of the agent-based model in specific locations of interest where detailed data are available.

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