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Review
. 2011 Feb 2:11:37.
doi: 10.1186/1471-2334-11-37.

The GLEaMviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale

Wouter Van den Broeck  1 , Corrado Gioannini, Bruno Gonçalves, Marco Quaggiotto, Vittoria Colizza, Alessandro Vespignani
Affiliations
Review

The GLEaMviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale

Wouter Van den Broeck et al. BMC Infect Dis. .

Abstract

Background: Computational models play an increasingly important role in the assessment and control of public health crises, as demonstrated during the 2009 H1N1 influenza pandemic. Much research has been done in recent years in the development of sophisticated data-driven models for realistic computer-based simulations of infectious disease spreading. However, only a few computational tools are presently available for assessing scenarios, predicting epidemic evolutions, and managing health emergencies that can benefit a broad audience of users including policy makers and health institutions.

Results: We present "GLEaMviz", a publicly available software system that simulates the spread of emerging human-to-human infectious diseases across the world. The GLEaMviz tool comprises three components: the client application, the proxy middleware, and the simulation engine. The latter two components constitute the GLEaMviz server. The simulation engine leverages on the Global Epidemic and Mobility (GLEaM) framework, a stochastic computational scheme that integrates worldwide high-resolution demographic and mobility data to simulate disease spread on the global scale. The GLEaMviz design aims at maximizing flexibility in defining the disease compartmental model and configuring the simulation scenario; it allows the user to set a variety of parameters including: compartment-specific features, transition values, and environmental effects. The output is a dynamic map and a corresponding set of charts that quantitatively describe the geo-temporal evolution of the disease. The software is designed as a client-server system. The multi-platform client, which can be installed on the user's local machine, is used to set up simulations that will be executed on the server, thus avoiding specific requirements for large computational capabilities on the user side.

Conclusions: The user-friendly graphical interface of the GLEaMviz tool, along with its high level of detail and the realism of its embedded modeling approach, opens up the platform to simulate realistic epidemic scenarios. These features make the GLEaMviz computational tool a convenient teaching/training tool as well as a first step toward the development of a computational tool aimed at facilitating the use and exploitation of computational models for the policy making and scenario analysis of infectious disease outbreaks.

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Figures

Figure 1
Figure 1
The GLEaMviz tool system involves one or more GLEaMviz clients that interact with a GLEaMviz server over a TCP connection. The server consists of a GLEaMviz proxy middleware component and a number of GLEaMviz simulation engines. The middleware component handles the interactions with the clients and manages the engine instances. Each submitted simulation is performed by one GLEaMviz simulation engine instance, which consists of a wrapper that launches the actual simulation cores, one for each run, and performs statistical analysis on the aggregated results.
Figure 2
Figure 2
The principal GLEaMviz tool workflow with the GLEaMviz components involved in each step. The compartmental model of the infectious disease is created in the Model Builder. Next, the Simulation Wizard is used to configure the simulation and submit it for execution by the Engine on the server. The Simulation History keeps track of the submitted simulations and retrieves the simulation results when they become available. The simulation results are finally inspected in the interactive Visualization.
Figure 3
Figure 3
The three population and mobility data layers in GLEaM. The population layer consists of demographic data at a resolution of 15 × 15 minutes of arc, organized in geo-referenced census areas obtained with a Voronoi tessellation procedure around transportation hubs. The short-range mobility layer covers commuting patterns between adjacent subpopulations based on data collected and analyzed from more than 30 countries on 5 continents across the world, modeled with a time-scale separation approach that defines the effective force of infections in connected subpopulations. The long-range mobility layer covers the air travel flow, measured in available seats between worldwide airport pairs connected by direct flights.
Figure 4
Figure 4
The compartmental Model Builder allows the user to define the dynamics of the infection by creating population compartments and specifying the infectious and the spontaneous transitions that occur between them. The Model Builder provides an interactive diagram of the compartmental model, a table with the values for the variables used in the model, a list of current inconsistencies in the model, and a panel that provides concise descriptions on the various functionalities in the interactive diagram.
Figure 5
Figure 5
The interactive compartmental model diagram in the Model Builder represents the compartments and the transitions. Various user interface elements in these representations allow the user to manipulate the model occurring to his/her needs.
Figure 6
Figure 6
The Simulation Wizard provides a sequence of panels that leads the user through the definition of the settings and parameters that characterize the simulation. These panels prompt the user to specify: (a) the type of simulation; (b) the compartmental model; (c) the simulation parameters; (d) the initial distribution of the population amongst the compartments; (e) the initial conditions of the start of the epidemic outbreak; and (f) the compartments considered for the results gathered at the end of the simulation.
Figure 7
Figure 7
The main window contains the main menu and the Simulations History, which provides an overview of all the simulations set up by the user, and a contextual menu with the applicable operations.
Figure 8
Figure 8
The simulation results can be inspected in an interactive visualization of the geo-temporal evolution of the epidemic. The map shows the state of the epidemic on a particular day with infected population cells color-coded according to the number of new cases of the quantity that is being displayed. Pop-ups provide more details upon request for each city basin. The zoomable multi-scale map allows the user to get a global overview, or to focus on a part of the world. The media-player-like interface at the bottom is used to select the day of interest, or show the evolution of the epidemic like a movie. Two sets of charts on the right show the incidence curve and the cumulative size of the epidemics for selectable areas of interest.
Figure 9
Figure 9
Compartmental structure in each subpopulation in the intervention scenario. A modified Susceptible-Latent-Infectious-Recovered model is considered, to take into account asymptomatic infections, traveling behavior while ill, and use of antiviral drugs as a pharmaceutical measure. In particular, infectious individuals are subdivided into: asymptomatic (Infectious_a), symptomatic individuals who travel while ill (Infectious_s_t), symptomatic individuals who restrict themselves from travel while ill (Infectious_s_nt), symptomatic individuals who undergo the antiviral treatment (Infectious_AVT). A susceptible individual interacting with an infectious person may contract the illness with rate beta and enter the latent compartment where he/she is infected but not yet infectious. The infection rate is rescaled by a factor ra in case of asymptomatic infection [41,46], and by a factor rAVT in case of a treated infection. At the end of the latency period, of average duration equal to eps-1, each latent individual becomes infectious, showing symptoms with probability 1-pa, whereas becoming asymptomatic with probability pa [41,46]. Change in travelling behavior after the onset of symptoms is modeled with probability pt set to 50% that individuals would stop travelling when ill [41]. Infectious individuals recover permanently after an average infectious period mu -1 equal to 2.5 days. We assume the antiviral treatment regimen to be administered to a 30% fraction (i.e. pAVT = 0.3) of the symptomatic infectious individuals within one day from the onset of symptoms, reducing the infectiousness and shortening the infectious period of 1 day. [41,42].
Figure 10
Figure 10
Simulated incidence profiles for North America and Western Europe in the baseline case (left panels) and in the AV treatment scenario (right panels). The plots are extracted from the GLEaMviz tool visualization. In the upper plots of each pair the curves and shaded areas correspond to the median and 95% reference range of 100 stochastic runs, respectively. The lower curves show the cumulative size of the infection. The dashed vertical line marks the same date for each scenario, clearly showing the shift in the epidemic spreading due to the AV treatment.
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