Alessandro Vespignani gave a talk titled Real time numerical forecast of global epidemic spreading using large-scale computational models: case study of 2009 A/H1N1pdm, illustrating the side by side comparison between the epidemiological surveillance data for the 2009 H1N1 pandemic peak of more than 40 countries in the Northern Hemisphere and the predictions obtained in real-time by GLEaM. The agreement of the model prediction and the surveillance data demonstrate the importance that computational tools such as GLEaM may have in facing the emergence of a pandemic event.

Corrado Gioannini illustrates the new features of the forthcoming GLEaMviz simulator 2.9 during the poster session

Furthermore, in his talk Alessandro presented the forthcoming 2.9 version of the simulator, including brand new visualization features and a redesigned user interface aimed at maximizing flexibility and usability. The participants of the workshop could also experiment live the new features of the forthcoming GLEaMviz 2.9 release, as Corrado Gioannini was providing a hands-on experience during the poster sessions. The exhibit gathered a lot of interest and many people stopped ad asked about the project.

Stay tuned for the 2.9 release!

The poster session of the workshop featured two posters based on the GLEaMviz project. The first poster, presented by Michele Tizzoni, focused on the study of the impact of travel restrictions in delaying and containing the spreading of an epidemic. The study, published in PLoS ONE, combines a theoretical analysis with a computational modeling work and, by considering the 2009 H1N1 pandemic as an example, shows the scarce effectiveness of such an intervention measures.
A second poster, titled Integrating the GLEaMviz Simulator tool with the Epidemic Marketplace platform, was presented by Corrado Gioannini and Joao Zamite, describing the effort carried out within the EPIWORK project aimed at providing a powerful but seamless integration of the GLEaMviz simulator tool with the Epidemic Marketplace information platform.

Human mobility networks, travel restrictions and the gobal spread of 2009 H1N1 pandemic

Integrating the GLEaMviz Simulator tool with the Epidemic Marketplace platform

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.

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.

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.

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.

]]> http://www.gleamviz.org/2011/10/new-release-gleamviz-simulator-v28/feed/ http://www.gleamviz.org/2011/07/gleamviz-simulator-new-public-release-with-multiple-output-data/ http://www.gleamviz.org/2011/07/gleamviz-simulator-new-public-release-with-multiple-output-data/#comments Fri, 29 Jul 2011 10:00:28 +0000 corrado http://www.gleamviz.org/?p=2787 A new public release of the GLEaMviz simulator is now available!
The main difference of this new update relates to the simulations’ output data that can be retrieved after execution to be visualized and analyzed. It is indeed now possible for GLEaMviz users to download from the server the simulation’s output of multiple compartments at the same time, separately, and then choose to visualize and analyze them one by one or aggregate them in a flexible way in the visualization window.

Detail of the Visualization Window of the GLEaMviz simulator showing the widget to choose the compartments.

Additional improvements have been added to this release, including the possibility to visualize an arbitrary number of plot charts in the visualization window and to immediately save the data of a plot chart to a file for subsequent analysis.

The new features have been documented in the updated version of the manual, which can be found here.
You are welcome to try our latest release and give us your feedback!

Science On a Sphere was initially developed as a way to explore environmental data using new visualization techniques and its is now used as unique and powerful teaching tool. Science On a Sphere is installed in more than 60 sites around the world, and the number is continuously increasing: the current list of installation locations can be found here.

The visualization can be extensively customized by clicking on the gear icon in the navigation interface, which opens up the configuration interface. The user can, for example, select from a number of backdrop maps onto which the visualization is projected, details of which are shown in the following figure. The option shown in the left panel is based on the NASA Blue Marble map, while the other two options provide a more neutral base onto which the cell-level cases can be inspected with greater visual accuracy.

Samples of the three backdrop maps from which the user can choose.

The GLEaMviz Simulator is discussed in great detail in this new publication:

The GLEaMviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale.
Wouter Van den Broeck, Corrado Gioannini, Bruno Goncalves, Marco Quaggiotto, Vittoria Colizza, Alessandro Vespignani
BMC Infectious Diseases, 11:37 (2011)

The paper discusses the objectives and functionalities offered by this software system, comparing these with similar publicly available software tools. A detailed description of the implementation of both the client-side and server-side components that comprise the complete system are given, and a realistic simulation example and its results are discussed.

With this new release and this publication we hope to further our objective of providing public access to sophisticated computational models in teaching/training settings and in the use and exploitation of large-scale simulations in public health scenario analysis. In particular our focus is on the simulation of emerging ILI infectious diseases at the global scale based on a detailed data-driven spatial epidemic and mobility model that offers an innovative solution in terms of flexibility, realism, and computational efficiency.

Human mobility networks, travel restrictions and the global spread of 2009 H1N1 pandemic
Paolo Bajardi, Chiara Poletto, Jose J Ramasco, Michele Tizzoni, Vittoria Colizza, Alessandro Vespignani
PLoS ONE 6(1): e16591 (2011).

During the early phase of the 2009 H1N1 pandemic outbreak, some countries implemented travel-related measures to prevent the infection from crossing the national borders. Many governments advised against non-essential travel to Mexico and activated airports entry screening to detect the potentially infected travelers. Even a few countries banned every flight connection to/from Mexico. All these measures, with the addition of self-imposed travel limitations due to the pandemic concerns following the international alert, contributed to an almost 40% reduction in the international passengers flying to and from Mexico. However, no containment was achieved by such restrictions and the virus was able to reach pandemic proportions in a short time.

Illustration of the global invasion of the 2009 H1N1 pandemic during the early stage of the outbreak. The arrows represent the seeding of unaffected countries due to infected individuals traveling from Mexico. The color code indicates the time of the seeding. The map shows the layer of the worldwide air transportation network, which is incorporated into GLEaM.

GLEaM is suitable to simulate the spreading of an influenza-like illness and, in particular, it has been calibrated to simulate the 2009 H1N1 pandemic considering the etiology of the disease and the initial conditions. Taking advantage of the high detailed mobility data at the global level integrated in the model structure, Bajardi and coworkers assessed the impact of different travel reduction policies in the unfolding of the simulated pandemics. The work  shows that feasible mobility limitations, highly disruptive in economic terms, generally are not effective: even with strong and lasting restrictions (a 90% reduction in the international air traffic to/from Mexico starting with the international alert and kept to the end of the epidemic was tested) the delay achieved is limited to two weeks .

A) Delay in the case importation from Mexico to a given country compared with the reference scenario. The parameter α is a worldwide passengers reduction to/from Mexico. The computational approach allows tests on draconian restrictions like α=90%. The dotted line shows the logarithmic behavior relating the delay as a function of the imposed restrictions. B) The global invasion threshold R is the key parameter for describing a global outbreak: if R* >1, the disease might globally spread. The basic reproductive number R0 is the average number of infections that a typical infectious individual can generate. Only in the case of extremely low values of R0 or extremely large values of α is it possible to reduce R* below the threshold.

In a pandemic scenario, this delay can be used to allocate resources and to enhance the surveillance systems, but it is definitely too short to develop a vaccine. Finally, the paper provides a quantitative discussion devoted to explain how the large heterogeneity of human mobility patterns is responsible for the ineffectiveness of travel restrictions. It is unlikely that, given the ever-increasing mobility of people around the world, travel restrictions could be used effectively in a future pandemic event.

Epidemic Planet displays the evolution of the 2009 H1N1 influenza pandemic and enables its users to interactively compare and learn about the effect of a number of intervention scenarios. The epidemic spreading on the global scale is simulated using GLEaM.
The application is in continuous evolution, and it has already been shown at the Science Gallery of the Trinity College in Dublin, at the Edinburgh International Science Festival, at the Science beyond Fiction event at the EU Parliament in Strasbourg, at the CosmoCaixa Science Museum in Barcelona, and more.

The venue of the SC10 Exhibition, in which the Epidemic Planet was hosted, wass the Ernest N. Moriale Convention Center (ENMCC).

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.

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.

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.