The EPIWORK project proposes a multidisciplinary research effort aimed at developing the appropriate framework of tools and  knowledge needed for the design of epidemic forecast infrastructures to be used in by epidemiologists and public health scientists. The project is a truly interdisciplinary effort, anchored to the research questions and needs of epidemiology research by the participation in the consortium of leading epidemiologists, public health specialists and mathematical biologists.

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In 2009 the Future & Emerging Technology unit (FET) of the European Commission launched its COSI-ICT proactive work programme – Science of Complex Systems for Socially Intelligence ICT.

This Workshop is the first in a series intended to formulate a systematic theory of ‘social intelligence’ and engineering principles for applications.
It focuses on the questions:

• What is ICT-enabled ‘Social Intelligence’ ?

• What theory(s) exists on Socially Intelligent ICT ?

• Engineering principles for SocialIy Intelligent ICT?

The Workshop will be hosted at Imperial College, London, on August 3rd 2010.

The keynote speaker of the event will be Ricardo Baeza-Yates, Yahoo Research Labs (Barcelona).

Epiwork’s coordinating team will participate to the event with a short talk on Epiwork’s view on the three questions.

The XLDB team at LASIGE (hosted by the Department of Informatics, Faculty of Sciences at University of Lisbon), in charge for the development and deployment of the Epiwork Epidemic Marketplace, has recently published an article on the Computational Biology special issue of the ERCIM (European Research Consortium for Informatics and Mathematics) news magazine.

“Computational Biology is defined as the science of understanding complex biological phenomena by the analysis of multi-sample and multi-variate quantitative data.

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Two teams (ISI Foundation and Bruno Kessler Foundation) of Epiwork Work Package 4 (Computational Modeling Platform) have produced 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 by GLEaM, the discrete stochastic epidemic computational model on which Epiwork Computational Modeling Platform is based on. 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.