Biosecurity and Bioterrorism: Biodefense Strategy, Practice, and Science Volume 11, Supplement 1, 2013 ª Mary Ann Liebert, Inc. DOI: 10.1089/bsp.2012.0071
A Generic Open-Source Software Framework Supporting Scenario Simulations in Bioterrorist Crises Alexander Falenski, Matthias Filter, Christian Tho¨ns, Armin A. Weiser, Jan-Frederik Wigger, Matthew Davis, Judith V. Douglas, Stefan Edlund, Kun Hu, James H. Kaufman, Bernd Appel, and Annemarie Ka¨sbohrer
Since the 2001 anthrax attack in the United States, awareness of threats originating from bioterrorism has grown. This led internationally to increased research efforts to improve knowledge of and approaches to protecting human and animal populations against the threat from such attacks. A collaborative effort in this context is the extension of the open-source Spatiotemporal Epidemiological Modeler (STEM) simulation and modeling software for agro- or bioterrorist crisis scenarios. STEM, originally designed to enable community-driven public health disease models and simulations, was extended with new features that enable integration of proprietary data as well as visualization of agent spread along supply and production chains. STEM now provides a fully developed open-source software infrastructure supporting critical modeling tasks such as ad hoc model generation, parameter estimation, simulation of scenario evolution, estimation of effects of mitigation or management measures, and documentation. This open-source software resource can be used free of charge. Additionally, STEM provides critical features like built-in worldwide data on administrative boundaries, transportation networks, or environmental conditions (eg, rainfall, temperature, elevation, vegetation). Users can easily combine their own confidential data with built-in public data to create customized models of desired resolution. STEM also supports collaborative and joint efforts in crisis situations by extended import and export functionalities. In this article we demonstrate specifically those new software features implemented to accomplish STEM application in agro- or bioterrorist crisis scenarios.
I
n public or animal health crisis situations, timely and scientifically based exposure and risk assessments are of utmost importance for all involved stakeholders (Regulation (EC) 178/2002).1 These exposure and risk assessments are even more important in bioterrorist or agroterrorist crisis situations in which the human or animal population is at high risk. The EU CBRN task force stated
the need for the exchange of methodologies for scenarios and for modeling between the member states (Recommendation 128).2 As could be witnessed in recent years during international foodborne disease outbreaks, tools and methodologies supporting efficient exposure assessments including the tracing back and forward of contaminated commodities are of crucial importance.3 In an outbreak,
Dr. Alexander Falenski, Matthias Filter, Christian Tho¨ns, Dr. Armin A. Weiser, and Jan-Frederik Wigger are Research Scientists in the Department of Biological Safety; Prof. Bernd Appel is Head of the Department of Biological Safety; Dr. Annemarie Ka¨sbohrer is Head of Unit Epidemiology and Zoonoses, Department Biological Safety; all at the Federal Institute for Risk Assessment, Berlin, Germany. Matthew Davis is a software engineer, Public Health Research; Judith V. Douglas is a technical writer; Stefan Edlund is a senior software engineer, Public Health Research; Kun Hu is a Research Scientist, Public Health Research; and Dr. James H. Kaufman is Manager, Public Health Research; all at IBM Almaden Research Center, San Jose, California. S134
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risk assessors have to respond to questions that crisis managers raise; the answers should support and enable critical decisions. Frequently, these questions concern the health status of populations in focus or the estimated spatial distribution and spread of an agent. Other questions relate to the likelihood of specific health-related effects, spatial and temporal disease outbreak patterns, and efficient management options for preventing or containing disease spread. To be able to answer these types of questions, risk assessors may use epidemiologic modeling software tools to create situation-specific epidemiologic models and run simulations for different scenarios they have defined. The challenge is that none of the epidemiologic modeling software solutions currently available is able to address the modeling tasks relevant in bioterrorist crisis situations.
Specific Requirements Bioterrorist attacks and foodborne disease outbreaks arise suddenly and require timely countermeasures to minimize cases of illness or even death. In such crisis situations, it can be crucial to be able to compute the outcomes for a range of possible scenarios—especially if relevant laboratory or public health data are not available in time. Software could help to develop (and test) control strategies based on computer simulations.4 Additionally, it would address the most important information gaps for creation of exposure assessments and risk characterizations faster and more specifically.5 A major requirement for simulation software in foodborne disease outbreaks is the need to be able to rapidly integrate information on commodity flows and commodity production processes into simulations of outbreak situations. Moreover, software solutions must enable the use of predefined disease, population, or transportation models and provide easy-to-use user interfaces to create new models immediately, based on the information available. This means the software has to be designed so that adaptations or modifications to existing models can be made by epidemiologists or risk assessors without software programming skills. This includes changing parameters of standard epidemiologic disease models, geographic information connected to model elements, and the epidemiologic model basis itself. In addition, users should have the choice to select stochastic or deterministic simulation approaches. Another necessity is the handling of real-world data. The software should allow the import of real-world outbreak data for visualization or model parameter estimation purposes (eg, transmission, incubation, and recovery rates). Efficient parameter estimation algorithms must therefore be in place to find solutions for the underlying system of differential equations matching real-world data. Additionally, imported real-world data frequently serve as the starting point for scenario evolution simulations. Simulation results in turn must be exportable for further analysis with statistical software packages. As an integrated feature, Volume 11, Supplement 1, 2013
sophisticated geospatial visualization options should be implemented. Visualization is not only essential for illustrating simulation results (eg, for risk communication to stakeholders, policymakers, or the general public), but it is also crucial for intuitive model generation and as a data and model validation tool. For example, the software should allow easy inspection of how the population size changes as a whole over the course of the simulation as well as in the different spatial disease compartments. Graphic representation of transportation and migration processes (eg, birds, food, people) are specific tasks that should be accomplishable. From a scientific community’s and software engineer’s point of view, it is important that the software be based on a modular and extendable structure. This guarantees efficiency with respect to reusability and extendibility of software code programming resources. For skilled users, this means that there are easy-to-accomplish steps by which software features can be added or adapted.
Existing Software for Modeling Spatiotemporal Epidemiology Several modeling tools or modeling modules already exist. GLEAMviz5,6 is a server-based simulation tool to model transportation and infection dynamics. The simulation consists of 3 so-called ‘‘layers’’ with (1) data about the population, (2) data about transportation events, and (3) a disease model. The population is based on real-world data and divided into cells of 25-x-25-km size. Travel by people by land or air is represented by data from national statistics offices and the International Air Transport Association. By using these data sources, evolution and spread of diseases can be modeled. The implemented stochastic SEIR (Susceptible, Exposed, Infected, Recovered) compartment model calculates the proportion of people in the different compartments for every population cell. The open-source software platform Epigrass, developed by Coelho et al.,7 includes several defined spatiotemporal models (SI, SIR, SEIR) for the spread of diseases. It is possible to visualize geographic information system (GIS) data and import transportation events. The software is written in Python and can be used only on Linux. The Jena Adaptable Modeling System ( JAMS)8 is a software system for simulating hydrological environmental processes. The software is structured in modules that have the advantage of being reusable in new models. JAMS offers functionalities for simulation, visualization of geographic information, and import and export of data. It is an opensource software and programmed in JavaTM, meaning that it is expandable and can be integrated with other software systems. Netlogo9 is a development environment for modeling complex natural and social phenomena. It is programmed in JavaTM and capable of setting up agent-based models. These models can perform especially search and optimization S135
SIMULATION SOFTWARE FOR BIOTERRORIST SCENARIOS Table 1. A Selection of Software Tools for Epidemiologic Modeling Criteria
STEM
EpiGrass
JAMS
Netlogo
GLEAMviz
+ + + + + +
+ + + + + +
+ + + + +
+ + + + +
+ + + + + +
+ +
+
+
+
online tool
Integrated simulation tools Visualization of geographic data Visualization of models Import/export interface for data and simulation results Implemented epidemiologic models Low costs (software) Little time and effort for software adaptations and adjustments Software structure modular, extensible + = feature present; - = feature absent.
tasks—for example, for routing and process chain optimizations. The software also can be used to visualize epidemiologic data in GIS. A selection of software tools and their features are summarized in Table 1.
Research Motivation The Spatiotemporal Epidemiological Modeler (STEM) is community-developed open-source software providing an optimal basis for our efforts dedicated to the generation of software that accomplishes all of the abovementioned critical software features.10,11 In addition, STEM has a community of users that provide support and contribute to the STEM software and to the library of STEM models. Based on community contributions or requests, several disease models, like seasonal influenza (human-to-human transmission),12 malaria (vectorborne disease),13 and dengue fever,14 were developed and made publicly available. Originally, STEM was centered on modeling the spatial and temporal spread of human or animal diseases. However, this research aimed at extending the applicability of the software to scenarios that include commodity and food production and transportation events, such as in foodborne bioterrorist disease outbreak situations. Compared with other solutions, STEM has several advantages: It is platform independent, meaning that it runs on Windows, Mac, and Linux; it is written in JavaTM (a widely used programming language); and it is available as a free open-source solution under the liberal Eclipse Public License. Unlike other applications, STEM is also a framework that in principle is capable of modeling anything that can be described as a graph. In this article, we describe those new features developed recently to extend and support STEM’s applicability in bioterrorist crisis situations.
Equinox (www.eclipse.org) plug-in tool framework. This is an industry standard framework for component software. The components are integrated through a well-defined ‘‘extension point’’ mechanism that makes the entire code base highly extensible. The use of the Eclipse framework also provides for multiplatform portability. As an open-source software project under the umbrella of the Eclipse foundation, the source code can be accessed free of charge via the STEM project website at www.eclipse.org/ stem. The software package Eclipse (downloadable via www.eclipse.org) itself served as the JavaTM software development environment.
Modeling Concept STEM treats the world as a graph within a modular and hierarchical modeling structure. From bottom to top, this structure has 3 basic levels: graphs, models, and scenarios. Graphs can consist of:
Nodes, representing spatial entities with defined shape and geospatial location; Edges, representing transportation processes between nodes or the hierarchical or spatial dependencies of nodes (eg, different administrative levels within states); and Labels, which can be assigned to nodes or edges and which carry information relevant for the scenario to be modeled (eg, population size, area size).
The STEM software is written in JavaTM and organized as a set of well-defined reusable components using the Eclipse
Models consist of at least 1 graph and any combination of so-called Decorators, which relate to the integrated graph(s). Decorators exist for specific tasks: For example, a Population Decorator can be used to initialize or define a population at the associated graph nodes. Specific Disease Decorators can be generated to define disease states (compartments) and their associated parameters that can as well be assigned specifically to different populations. To accomplish this, models are organized hierarchically inside of other models. Scenarios in turn may contain a variety of additional components, but they consist at least of a Model, a Time
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Sequencer, and a Solver for the differential equations. In most cases, scenarios contain a Disease Initializer that defines where and when the disease outbreak originates. Advanced components such as Inoculators, customizable Loggers, and Triggers allow users to easily create, run, visualize, and record multiple ‘‘what if’’ experiments. STEM provides detailed documentation on all of these features on their wiki (http://wiki.eclipse.org/index.php/STEM). Additional community resources include a newsgroup and bugzilla through which users may send new feature requests and report problems.
Results The following new features were programmed because they were considered essential to improve STEM’s applicability in exposure assessment and scenario simulation tasks before and during bioterrorist crisis situations:
An import functionality for proprietary geo-coded data files (Shapefile Graph Generator); An import functionality for Pajek-formatted files15 describing time-discrete transportation events (Pajek Net Graph Generator);
A graphic user interface (GUI) for visualization and editing of STEM graphs and node/edge properties (Graph Editor); and A Transformation Decorator functionality allowing modeling of food production and processing events (Transformation Decorator).
Shapefile Graph Generator Shapefiles (*.shp) are a popular data standard in the field of geographic information science. Shapefiles can describe points (eg, volcanoes, hot spots), polylines (eg, rivers or roads), or polygons (eg, administrative boundaries, lakes, islands) together with information associated with these structures. Shapefiles are widely used to exchange GISrelated information between different GIS software solutions and are therefore an ideal import format for data relevant for epidemiologic modeling tasks. Even though STEM already contains a large library of built-in GIS and human population data, it is crucial to be able to integrate other custom high-resolution maps and associated data into epidemiologic simulations in a crisis situation. The import of data from shapefiles into STEM is facilitated by the Shapefile Graph Generator plug-in (Figure 1). Via a GUI, the user can select 1 or more shapefiles for
Figure 1. Shapefile Graph Generator: Configuration of a railroad track (a) and graphic output (b).
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Figure 1b.
(Continued)
import. In case of a polyline shapefile, the user needs to specify whether STEM should create a migration or a road edge into the STEM representation of the GIS structure. Additionally, it is possible to import a shapefile into an existing STEM graph structure. In this way, the maps already included in STEM can be enriched with the user’s own (proprietary) data in a straightforward way.
Pajek Net Graph Generator A Pajek Net file is a standard text file format used in the area of network analysis science for, for example, description of large network structures.15 The original Pajek file format consists of the 6 data objects: Networks (*.net), Partitions (*.clu), Permutations (*.per), Clusters (*.cls), Hierarchies (*.hie), and Vectors (*.vec). The *.net file contains the network structure data and can be read by many software programs for network analysis (eg, by NetMiner16 or UCINET17). As part of this research, an extended *.net file format was created that gives the user the flexibility to apply population-specific numbers to nodes or time stamps to edges. The STEM Pajek Net Graph Generator plug-in S138
reads in both types of *.net formatted files and creates a STEM graph representation based on the information given. Nodes are represented as squared box shapes, and edges are represented as lines with an associated time point and a label showing properties (eg, quantity). Thus it is possible to integrate information on commodity transportation events or specific population migration events into the STEM graph. In order to provide users with an even more comfortable interface, a Microsoft Excel and an Open Office macro have been created, allowing the generation of *.net files from simple spreadsheets. With the help of these tools, the integration of transportation events into STEM can be accomplished in 2 ways: Either a built-in STEM node is identified (eg, AT4 = Upper Austria), or the specific coordinates (coordinate system WGS84) of the nodes are entered into the spreadsheet table. The following lines represent an example of migration edges in the *.net file format. *Vertices 3 1 Slaughter1 10.3 53.65 popID animal popCount 0 popID meat popCount 0 Biosecurity and Bioterrorism: Biodefense Strategy, Practice, and Science
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2 Slaughter2 9.44 51.8 popID animal popCount 0 popID meat popCount 0 3 Farm1 9.59 53.01 popID animal popCount 2400 popID meat popCount 0 *Edges 3 1 popID animal rate 0 date 2012-02-01 rate 400 3 2 popID animal rate 0 date 2012-02-01 rate 400 3 1 popID animal rate 0 date 2012-04-01 rate 400 The vertices in the *.net file are defined by the following elements (first vertex in brackets as example):
Vertex number (1), Vertex name (Slaughter1, meaning slaughterhouse no. 1), Geographic coordinates (10.3 53.65 meaning 1030¢ east, 5365¢ north), Population identifier (animal), Population count (0, meaning no animals are in the slaughterhouse), Additional population identifier (meat), Population count (0, meaning no meat is in the slaughterhouse).
All vertices are defined in the same way. The edges describe the connections between vertices and contain information about transport of ‘‘populations’’ between them. They are defined as follows (first line as example):
Vertex from where the population originates (vertex 3 is the farm) Vertex to where population migrates (vertex 1 is slaughterhouse no. 1) Population that migrates or is transported (animals are transported from the farm to the slaughterhouse) Date of migration or transport (February 1, 2012) Number of individuals migrating or being transported (400 animals)
Thus, the first edge can be translated as, ‘‘On February 1, 2012, 400 animals are transported from farm no. 1 to slaughterhouse no. 1.’’ The resulting graph is shown in Figure 2.
Graph Editor The Graph Editor plug-in provides functionalities to visualize and manually edit STEM graphs or models including node and edge properties from within the STEM designer perspective. This feature is of utmost importance as it enables users to visually inspect and verify information represented in a STEM model without having to start the simulation. Although the hierarchical structure of STEM scenarios is advantageous with respect to modularity and reusability, it can aggravate visualization of information. This new Graph Editor feature allows users to easily and
Figure 2. Representation of the given *.net file as a STEM graph. It shows a farm (vertex 3, square) distributing animals to 2 different animal processing nodes (vertices 1 and 2, stars). From there meat is transported to retail (hollow circles) and then to the human population in the administrative regions. For visualization purposes, transport from retail to consumer is represented by edges to the center of the administrative regions (filled circles).
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Figure 3.
Graph Editor: Visualizing graphs and adding new edges with the Graph Editor plug-in
intuitively add nodes (eg, a new source of contamination) or edges (eg, a migration or a transportation edge) including their associated properties. In addition, the user has the opportunity to view and edit property labels of nodes and edges (Figure 3).
Transformation Decorators The requirement to model bioterrorist foodborne disease outbreak scenarios triggered the development of a so-called Transformation Decorator plug-in. The rationale behind this is that certain food manufacturing and production processes (like the slaughter of animals) cannot simply be represented by a standard Population Model, as would be done for processes like growth or spoilage of crops. Modeling food production requires a feature to describe the transformation of livestock (eg, an animal) and/or plantderived raw products (eg, a head of cabbage) into food commodities (eg, a steak or a salad) along with means to express how a contaminant or pathogen will be transmitted or introduced into the production process. To support this, a new type of decorator called a ‘‘Transformation Decorator’’ was created that can do the following:
Transform one ‘‘population’’ into another (eg, cattle into beef); Handle mapping of compartment models associated with 1 population (eg, SIR in cattle) into compartS140
ment models associated with another population (eg, Non-Contaminated/Contaminated in meat); Represent transmission of the agent by mechanisms other than contact or the commodity flow (eg, an infected animal can contaminate a whole batch and in cases of poor hygiene in the processing unit crosscontaminate consecutive batches); Allow nonintegratable (instantaneous) transformations (eg, transformation of cattle into meat). Transformation Decorators already implemented include Food Producers (eg, a slaughterhouse) and a Food Consumer instance that can be created to model a population consuming the product generated by the Food Producer. These existing components can be easily extended and built upon. Computationally, the Transformation Decorators differ from standard Decorators only in how the numerical solver engine handles the transformation. STEM allows users to choose from a set of industry standard numerical integration engines (as well as a finite difference solver). Real numerical integration requires computation at variable time steps (and the time step is dynamically reduced to ensure the computation converges). Instantaneous or discontinuous transformations (like meat production) might be modeled after 1 time step has converged (and before the next time step is started). A developer can choose to use Transformation Decorators where transformations must be Biosecurity and Bioterrorism: Biodefense Strategy, Practice, and Science
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made discontinuously or instantaneously. In all cases, STEM’s use of multicore parallelism makes such computations fast and efficient.
human consumption. All model parameters (except map polygons) were artificially created to avoid misuse.
Population Model Sample Scenario and Simulation As a proof-of-concept, a sample scenario has been created, demonstrating the applicability of STEM to estimate the effects of a potential bioterrorist foodborne disease scenario originating from a contamination on an animal farm and considering production of meat, meat distribution, and
The example population model consists of graphs representing several artificially generated farms, animal processing units (slaughterhouse with attached meat processing facility), retail stores, and administrative units. The geographical positions of these buildings were imported via the Pajek Net Graph Generator. The map is from the STEM internal geographical library and was supplemented with
Figure 4. STEM representation of an artificially designed bioterrorist sample scenario. The figure highlights all elements necessary to run the scenario.
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shapes imported with the Shapefile Graph Generator. There are artificially generated trade events for animals and meat (each of which is a separate population) represented by edges connecting either the corresponding farms or farms with the animal processing units or the animal processing units with retail stores. These edges were mostly defined in the Pajek file but in part also edited with the Graph Editor. From the retail stores, meat is transferred immediately into the administrative unit nodes, which are inhabited by the human population. The consumption of meat is described with a Food Consumption Decorator (Figure 4).
Disease Models The spread of a hypothetical agent inside the animal farm population is modeled as a SIR compartment model (Equation 1) using the standard disease model library available within STEM. The definition of a SIR model is shown in Equation 1, where the total population P is the addition of susceptible (S), infectious (I), and recovered (R) people (Equation 2). The model parameters are the transmission rate b, the recovery rate g, the immunity loss rate a, and the death/birth rates m. These were set to sample values of b = 0.2, g = 0.1, a = 0.05, and m = 0. As the immunity loss rate is greater than zero, it is actually a SIRS model, meaning that after some time the population becomes susceptible again (here: after 20 days). DS ¼ b (S=P)I þ aR þ l (P S) DI ¼ b (S=P)I cI lI DR ¼ cI aR lR
P ¼ (S þ I þ R)
(1)
(2)
The animal population model is defined such that on the targeted farm a population of 2,400 animals is housed in the facility. It is further assumed that 10% of this animal population is infected with the agent at the beginning of the simulation, for example, through in-trade of new bought animals. In the created scenario, animals are kept for 1 month before they are transported to the animal processing facility. To represent the agent transmission from animals to meat in the animal processing facility, the artificial transmission matrix shown in Table 2 was applied. This was achieved with a Transformation Decorator called Food Transformer Animal (see also Figure 4). The Food Transformation Decorator of the animal processing facility was configured such that each animal was processed to 200 units of meat. In combination with Table 2, this means that, according to this artificial scenario, from each infected animal 12.5% of the 200 units of meat ( = 25 units) are contaminated. S142
Table 2. Cross-tabulation for the Carry-over of a Pathogen from Animal to Meat in an Animal Processing Facility Meat Animal
Noncontaminated
Contaminated
S I R
0.991 0.875 0.991
0.009a 0.125 0.009a
a
Pathogen-positive meat, eg, due to cross-contamination.
On the human population side, a simplified doseresponse model has been created that assumes that 5% of the people eating contaminated meat get sick from consumption, with an incubation period of 2 days. The subsequent spread of the disease caused by human-to-human contact is then modeled as standard SI model with sample values of b = 0.0025 and g = 0.05.
Transportation Model The implemented model assumes that the contaminated farm delivers to 2 independent animal processing facilities 400 animals each (Figure 5A). This is done at 3 time points during the simulation time (in months 2, 4, and 6). Transportation itself was modeled to last 1 day. From the animal processing facility nodes, all meat is transported to retail stores located in the northern and central administrative units (no waste considered) at constant specific transportation rates (Figure 5B). Growth of pathogens (eg, due to inappropriate storage during transportation) was not taken into account in this model. The model has been further designed such that meat is sold at a rate of 20% per day to the consumer. Every day all sold meat is fully consumed by the population members living in that region (1 unit per person).
Scenario Simulation Figures 5A-C show different perspectives on the scenario map view during simulation of the artificial scenario. While figure 5A displays animal transport events at a discrete time point, figure 5B shows the meat distribution from the 2 animal processing facilities via different retail nodes (squares) into the human population (indicated by arrows directing into the center of each region). Figure 5C demonstrates the status of disease spreading in the human population 18 days after the meat has been sold and consumed. The hypothetical disease is also transmitted from human to human and spreads to adjacent regions in which contaminated meat is not sold (Figure 5C). Darker colored regions represent a greater proportion of the population being infected. Simulation results were saved as *.csv files and are supplied in the supplements together with the map view images (Supplementary Materials are available online at www.liebertpub.com/bsp). Biosecurity and Bioterrorism: Biodefense Strategy, Practice, and Science
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Figure 5. Spread of an agent due to a deliberate contamination of a farm. The infected animals are transported from the farm to 2 animal processing units (a). (Contaminated) meat is produced and distributed to retail shops (b). The human population buys and consumes (contaminated) meat and may become infected. The dimension of the resulting outbreak 18 days after the meat has been sold and consumed is shown in (c). Darker color regions represent a greater proportion of the population being infected.
Discussion The work presented in this article is the result of nationally and internationally funded research efforts that fall in line with recommendations of the EU CBRN task force stating that the exchange of methodologies for scenarios and for modeling between the member states should be strengthened (Recommendation 128). This is closely connected to recommendation 129,18 which suggests that models need to be developed for different biological agents considering distribution, infectious dose, and stability. As bioterrorist threats originate not only from the direct ‘‘application’’ of agents to humans but also from indirect contamination of Volume 11, Supplement 1, 2013
food, water, animals, or the environment,19,20 this research aimed at developing a community resource for modeling and simulation that is capable of representing all these different scenarios. Fulfilling the abovementioned CBRN task force recommendations requires a software solution that is opensource, available for free use, with a proven track record of real-world application and a history of maintenance and community support. The rationale is that only software complying with the open-source philosophy can guarantee the transparency and free community developments necessary in this highly sensitive research area. To the best of our knowledge, STEM is currently the only generic S143
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epidemiologic software simulation framework that complies with these requirements. Consequently, the research presented here aimed at extending the STEM framework so that it becomes applicable to a broader range of bioterrorist crisis scenarios. Additionally, the broad applicability of the software to a wide range of scientific research scenarios and questions will contribute to the necessary data and model infrastructure needed in crisis situation application. This includes the new software features for GIS-data integration as well as a feature for the integration of discrete transportation events. The generic structure for representation of epidemiologic data, transport processes, disease models, and contamination scenarios supports efficient information exchange between stakeholders involved in crisis situations. The framework allows the execution and simulation of user-defined intervention scenarios that could support decision making by risk managers. The part of the work that has to be coordinated by the responsible national authorities becomes the generation of a model library for relevant scenarios. This model library would encompass dose-response, disease, population, and transportation models as well as data and models on food production and food consumption. The distinction—free open-source software but restricted information on models and data—is to our knowledge the only reasonable solution to achieve efficient knowledge and information exchange in crisis situations on the national, European, and global levels. This strategy additionally guarantees that these technologies, developed to support preparedness and action in crisis situations, cannot be misused by third parties because of the lack of relevant data. Moreover, this common software framework could pave the way for real European interoperability of national epidemiologic models and scenarios. Now that STEM has achieved the status of an official Eclipse top-level technology project, the established project infrastructure (website, wiki, code repository, news group, issue tracking system, legal framework, etc) will support such initiatives. Together with the commitment of the STEM community members, this project has demonstrated once more that open-source community projects might well overcome missing financial incentives that would be available in commercial software development projects.
and the European Commission cannot be held responsible for any use that may be made of the information contained therein. Dr. Alexander Falenski and Matthias Filter contributed equally to the writing of this article.
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We gratefully acknowledge the constructive comments from the editor and 3 reviewers. In particular, we thank 1 of the reviewers who helped to improve the clarity of the central concept of our manuscript. This research was supported by/executed in the framework of the EU project AniBioThreat (Grant Agreement: Home/2009/ISEC/AG/ 191) with financial support from the Prevention of and Fight against Crime Programme of the European Union, European Commission—Directorate General Home Affairs. This publication reflects the views only of the authors,
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FALENSKI ET AL. 15. Batagelj V, Mrvar A. Pajek—Program for Large Network Analysis. Connections 1998;21(2):47-57. 16. NetMiner. Version 4.0. Seoul, Korea: Cyram Inc.; 2011. 17. Borgatti SP, Everett MG, Freeman LC. Ucinet for Windows: Software for Social Network Analysis. Harvard, MA: Analytic Technologies; 2002. 18. CBRN. Recommendation for action 129. Report of the European Task Force 2009;49. 19. Riedel S. Biological warfare and bioterrorism: a historical review. Proc Bayl Univ Med Cent 2004;17(4):400-406. 20. Ackerman GA, Giroux J. A history of biological disasters of animal origin in North America. Rev Sci Tech 2006;25(1):83-92.
Volume 11, Supplement 1, 2013
Manuscript received December 14, 2012; accepted for publication June 3, 2013. Address correspondence to: Matthias Filter Department Biological Safety Federal Institute for Risk Assessment Max-Dohrn-Straße 8-10 10589 Berlin Germany E-mail:
[email protected]
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