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| | * [http://www.eclipse.org/stem/ '''STEM Website'''] | | * [http://www.eclipse.org/stem/ '''STEM Website'''] |
| | * [[Disclaimer]] | | * [[Disclaimer]] |
| − |
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| − | == Introduction ==
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| − |
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| − | [[Image:GlobalH1N1As.jpg|frame|right|600px|A Global H1N1 Simulation.]]
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| − | What is the '''Spatiotemporal Epidemiological Modeler''' ('''STEM''')?
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| − | The Spatiotemporal Epidemiological Modeler (STEM) tool is designed to help scientists and public health officials create and use spatial and temporal models of emerging infectious diseases. These models can aid in understanding and potentially preventing the spread of such diseases.
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| − | Policymakers responsible for strategies to contain disease and prevent epidemics need an accurate understanding of disease dynamics and the likely outcomes of preventive actions. In an increasingly connected world with extremely efficient global transportation links, the vectors of infection can be quite complex. STEM facilitates the development of advanced mathematical models, the creation of flexible models involving multiple populations (species) and interactions between diseases, and a better understanding of epidemiology.
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| − |
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| − | '''How does it work?''' The STEM application has built in Geographical Information System (GIS) data for almost every country in the world. It comes with data about country borders, populations, shared borders (neighbors), interstate highways, state highways, and airports. This data comes from various public sources.
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| − | STEM is designed to make it easy for developers and researchers to plug in their choice of models. It comes with spatiotemporal Susceptible/Infectious/Recovered (SIR) and Susceptible/Exposed/Infectious/Recovered (SEIR) models pre-coded with both deterministic and stochastic engines.
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| − | The parameters in any model are specified in XML configuration files. Users can easily change the weight or significance of various disease vectors (such as highways, shared borders, airports, etc). Users can also create their own unique vectors for disease. Further details are available in the user manual and design documentation.
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| − | The original version of [http://www.alphaworks.ibm.com/tech/stem STEM] was available for downloading on IBM's Alphaworks. It contained easy to follow instructions and many examples (various diseases and maps of the world).
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| − | New developers who want to work on STEM can find useful tools, conventions, and design information in the [[Welcome STEM Developers]] article.
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| − | The STEM code repository is hosted on the Eclipse Technology code repository.
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| − |
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| − | == What's New ==
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| − |
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| − | === High Resolution Data ===
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| − | High resolution data is now available for '''Russia''' and '''Mexico'''.
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| − | Data for Mexico goes to Administrative Level 2. Work is underway to map this to zip codes.
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| − |
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| − | === STEM Video Tutorials (English & Spanish) on YouTube(TM) ===
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| − | The following url contains a full length tutorial on STEM. http://www.youtube.com/watch?v=LfiibQX4IFE We are in the process of creating versions in several different languages.
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| − | And here's the Spanish Language Version too http://www.youtube.com/watch?v=3S5DbjCHsx4
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| − | * July 2009...'''POPULATION MODELS'''
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| − | [http://wiki.eclipse.org/STEM#Population_Models Population Models] have been implemented.
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| − | * July 2009...'''STEM SOLVERS ''NOW'' SEPARATE PLUGINS'''
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| − | With the next STEM build, we've turned the Ordinary Differential Equation (ODE) ''Solvers'' that is at the heart of STEM into separate plugins. There are two reasons for this:
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| − | 1. It makes it easy for anybody to write their own numerical ODE Solver (or any other type of solver for that matter) and plug into STEM.
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| − | 2. Before, the solver being used was specified as part of the disease model. This causes problems when there are two or more diseases affecting a population since there's a risk the two diseases become "out-of-sync". For instance, the deaths caused by one of the diseases need to be fed into the model for the second disease (and vice-versa) so we can't use two different ODE solvers to generate results separately for each disease.
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| − | As a result of this, you now specify the solver (Finite Difference or Runge Kutta) when you create a new STEM scenario. '''''IMPORTANT: '''''Any scenario created before this change took place will default back to the simple Finite Difference ODE solver. If you want to re-enable Runge Kutta, you need to create a new scenario and drag your model etc. into it.
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| − | * July 2009...'''STEM NOW A TOP-LEVEL ECLIPSE TECHNOLOGY PROJECT'''
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| − | * STEM has now been moved into its new status.
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| − | * April 2009...'''NEW STATUS COMING FOR STEM'''
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| − | * STEM will moving in the next few weeks. Eclipse has approved '''STEM as a top-level Eclipse Technology Project.''' Watch this space for updates!
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| − | * The '''air travel model''' for every US airport is now available to drag in and use.
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| − | * January 2009...'''MOVING FORWARD'''
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| − | * In January, v0.4.0 of STEM became available for download, and the core team at Almaden continued to run very large scale computations using large data sets. A new Innoculator was developed for use in studying the impact of vaccination on disease spread. And, as always, work continued on eliminating bugs.
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| − | * December 2008...'''NEW CORE INTEGRATION ENGINE'''
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| − | * In November we completed a new core integration engine for STEM. Previously we only had a finite difference equation solver. At the core of the disease modeling component in STEM is a numerical differential equation solver that at each cycle determines the state of a disease at each location. Up until now, the solver was using a method based on finite difference which essentially calculates a single delta (or "change") from one cycle to the next. While this method is fast, it performs poorly when a disease is very active such as during the onset of an outbreak. STEM now has a new solver using the Runge Kutta Fehlberg method that takes advantage of an adaptive step size algorithm. Essentially the method carefully treads through periods of increased disease activity and takes great strides when there is little activity going on in the simulation. It is possible to tune the tolerance as part of the disease model, enabling very precise calculations when needed.
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| − | * September 2008...'''ANALYSIS PERSPECTIVE'''
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| − | A new STEM Perspective was made available to support a variety of analysis, fitting, and comparison functions across multiple simulations and data sets. New Analysis and Validation Tutorials were also added.
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| − |
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| − | [[Analysis]]
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| − | :::1. [[Estimating Model Parameters from External Data]].
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| − | :::2. [[Epidemic View|Epidemic Analysis]]
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| − | :::3. [[RMS Comparison between data sets]]
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| − | :::4. [[Lyapunov Analysis]]
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| − |
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| − | '''Model Parameter Estimation View'''
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| − | Given a set of data (SI, SIR, or SEIR) as a function of time, this perspective provides an estimation of the model parameters for a standard compartment model of the corresponding type. The view provides estimates for:
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| − | * beta, the disease transmission rate
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| − | * alpha, the recovery rate
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| − | * epsilon, the incubation rate
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| − | * gamma,the immunity loss rate
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| − |
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| − | '''Dynamical Systems View (Lyapunov Analysis)'''
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| − | This view displays the rate of separation in phase space (I vs. S) of the trajectories representing two different data sets or disease models. The rate of separation is then plotted vs time in a second chart. The rate of spread of any infectious disease defines a dynamical system. The Lyapunov exponent of any dynamical system describes the rate of separation of infinitesimally close trajectories in phase space.
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| − | '''Cross Model Comparison (RMS Compare)'''
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| − | Given a data set and the results of a model (or two model generated data sets), the RMS (Root Mean Square) comparison function shows the RMS difference betweent the two as a function of time.
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| − |
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| − | '''The Epidemic View'''
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| − | This view displays the aggregated data (e.g., S,E,I,R, births, and deaths) as a function of time. It also creates a summary file integrating over the data from all locations in a previously run scenario. It also shows the incidence or "newly infectious count" for the aggregated data.
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| − |
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| − | '''OTHER IMPORTANT FEATURES ADDED IN 2008'''
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| − | In July...'''Scenarios Caching'''
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| − | This new feature stores data from a scenario that was loaded recently to re-use when rerunning the scenario. For example, running a scenario for the United States the first time takes some time to read the data from the file system. Using the caching feature, consecutive runs of the same scenario won't reload the data by using the already initialized scenario from the cache. This feature can be toggled using the STEM preferences (Window->Preferences->STEM->Simulation Management->Use scenarios caching). Default is to use the caching system.
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| − | In May...'''A Host of New Features'''
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| − |
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| − | Among these new features are ones that allow user to
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| − | *Create and run multiple '''Experiments'''
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| − | **Specify a set or sequence of parameters to run multiple experiments
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| − | **Create a collection of modifiers for a model and link them to a scenario
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| − | **Run a simulation from each newly created modifier in a series of simulations, i.e., to run in batch mode.
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| − | *Import data from comma separated variable files and "play back" surveillance data in STEM as an imported disease model
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| − | *Export the results of a simulation to comma separated variable files.
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| − | Other work was done to
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| − | *Provide a new Mixing Model for Transportation that builds on STEM’s two transportation network models
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| − | *Fix a major bug in running continent level scenarios
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| − | **Allow users to account for both continuous traffic flow and (coming soon) time-delayed “packets” such as airplane or cargo shipments where the disease can spread on the transport node itself.
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| − | *Improve the Editors allowing better drag and drop, deletion, etc. (coming soon - email a scenario!!)
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| − | *Improve performance in graphics and other processes.
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| − | *Revalidate population data and provide better estimates for locations in 37 countries previously missing population data.
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| − |
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| − | == Weekly STEM Conference Call ==
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| − | [[Image:STEM_LOGO.jpg|left|400px]]
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| − | The STEM community has a weekly conference call. For more information, or if you wish to join, please send mailto:judyvdouglas@verizon.net
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| − | === How You Can Contribute to STEM ===
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| − | New Contributors to STEM are always welcome (please contact the developers). This includes researchers interested in disease modeling but also experts in GIS data or any other data that might be important in understanding or modeling the spread of infectious disease. We also welcome input from users and contributions to our documentation.
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| − | To contribute to STEM please use the standard eclipse process. Open a "bug" in our bugzilla (https://bugs.eclipse.org/bugs/)
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| − | A bug can be more than just a new defect - it can also be a new feature or other contribution.
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| − | You can attach your contribution as a "patch" to the your bug (http://wiki.eclipse.org/index.php/Bug_Reporting_FAQ)
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| − | Please also feel free to e-mail the STEM development team many of whom are Eclipse Committers and can help you join the project
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| − | (http://wiki.eclipse.org/STEM#The_STEM_Development_Team). We also have a weekly phone call, newsgroup, etc.
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| − |
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| − | == Publications and Presentations on STEM ==
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| − | Edlund S, Bromberg M, Chodick G, Douglas J, Ford D, Kaufman Z, Lessler J, Marom R, Mesika Y, Ram R, Shalev V, Kaufman J. 2009. "A spatiotemporal model for influenza." HIC 2009, Frontiers of Health Informatics, Canberra, Australia, August 19-21, 2009. http://www.hisa.org.au/system/files/u2233/hic09-2_StefanEdlund.pdf
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| − | Kaufman J, Edlund S, Douglas J. 2009. "Infectious disease modeling: creating a community to respond to biological threats." Statistical Communications in Infectious Diseases, Vol 1, Issue 1, Article 1. The Berkeley Electronic Press. http://www.bepress/scid/vol1/iss1/art1
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| − | Kaufman J, Edlund S, Bromberg M, Chodick G, Lessler J, Mesika Yossi, Ram R, Douglas J, Kaufman Z, Levanthal A, Marom R, Shalev V. 2009. "Temporal and spatial effects of lunar calendar holidays on influenza A transmission in Israel." Accepted for presentation at Epidemics 2, Athens, Greece, December 2009.
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| − | Edlund S, Kaufman J, Douglas J, Bromberg M, Kaufman A, Chodick G, Marom R, Shalev V, Lessler J, Mesika Y, Ram R, Leventhal A. 2009. "A study of two spatiotemporal models for seasonal influenza." Paper submitted and in peer review process for publication in a special issue of ACM TOMAS on health care simulations.
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| − | Ford DA, Kaufman JH, Mesika Y. 2009 (In press). "Modeling in space and time: a framework for visualization and collaboration." In D. Zeng et al. (eds), Infectious Disease Informatics. New York: Springer.
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| − | Lessler J, Kaufman JH, Ford DA, Douglas JV. 2009. "The cost of simplifying air travel when modeling disease spread," PLoS ONE 4(2): e4403. doi: 10.1371/journal.pone.004403.
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| − | Kaufman JH, Conant JL, Ford DA, Kirihata W, Jones B, Douglas JV. "Assessing the accuracy of spatiotemporal epidemiological models," in D. Zeng et al. (Eds): BioSecure 2008, LNCS 5354, pp. 143-154, 2008. Also presented at BioSecure 2008, Biosurveillance and Biosecurity Workshop, Raleigh, NC, Dec 2, 2008.
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| − | Ford DA, Kaufman JH. 2008. "The spatiotemporal epidemiological modeller (STEM)." Presentation at the Joint Session Homeland/Humanitarian Preparedness for Pandemic Influenza, Washington, DC, Oct 13, 2008.
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| − | Kaufman JH, Conant JL, Ford DA, Kirihata W, Douglas JV, Jones BA. 2008 (December). "Assessing the accuracy of spatiotemporal epidemiological models. In D Zeng et al. (eds): BioSecure 2008, LNCS 5354, pp. 143-154.
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| − | Kaufman JH, Ford DA, Mesika Y, Lessler J. 2008 (December). Modeling disease spread in space and time: extending and validating an open source tool for public health. Epidemics (EPID2008): First International Conference on Infectious Disease Dynamics. Asilomar, CA, December 1-3, 2008.
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| − | Ford DA, Kaufman JH, Eiron I, "An extensible spatial and temporal epidemiological modeling system," International Journal of Health Geographics 2006, 5:4 [http://www.ij-healthgeographics.com/content/5/1/4 http://www.ij-healthgeographics.com/content/5/1/4] (17Jan2006)
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| − |
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| − | == More About STEM ==
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| − | ===STEM Plugins===
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| − | STEM takes advantage of Equinox (the Eclipse implemenation of OSGI) to make all of its components available as Eclipse Plugins. This means that both the models and data that come with STEM are reusable, exchangeable, extendable, and - it you don't like them - they are replaceable. The Disease Models in STEM are based on standards compartment models at the level of Anderson and May. Today we have both stochastic and deterministic implementations of SI, SIR, and SEIR models. We are also developing a models for specific diseases including a global model for seasonal influenza. Any model in STEM requires a solver to integrate the differential equaitons. Today we have both Finite Difference and Runge Kutta mathematical solvers for every model. The solvers themselves are provided as plugins.
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| − | ===Data===
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| − | Regardless of the computational approach used to predict the future state of a disease, any simulation or model requires "denominator" data. The numerator data usually comes from Public health data on specific reportable conditions. You can think of numerator data as an “initial condition” or input into a model. Historic (numerator) data can be used to validate a model. However, for every model there is a need for a large variety of denominator data. This includes information on the population itself as well as information on the vectors of diease transmission. Depending on the disease of interest these vectors might include a transportation model or even a model of a migrating wild animal population. The Eclipse Framework gives users a simple “plug and play” software architecture with a drag and drop interface so when composing a new model or scenario users can drag specific data of interest into their model and literally "compose" a new scenario. The data sets distributed with STEM include geography, transportation systems (e.g., roads, air travel), and population for the 244 countries and dependent areas defined by International Standards Organizations. We make no guarantees about the accuracy of the data provided and users may certainly chose to plug in their own data replacing what comes with STEM. The data provided comes from open public sources including:
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| − | * [https://www.cia.gov/cia/publications/factbook/geos/aa.html The CIA Fact Book]
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| − | * [http://www.grid.unep.ch/data/data.php?category=human_related United Nations Environment Programme (UNEP)]
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| − | * [http://www.census.gov/geo/www/tiger/ The U.S. Census Bureau Tiger files]
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| − | * National Population Census Facts can also be found through http://www.census.gov/aboutus/stat_int.html
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| − | ====Population====
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| − | [[Image:WorldPopulationDensityS.jpg|frame|left|600px|World Population Density.]]
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| − | The population in STEM is based on census data for the administrative divisions that correspond to the polygons visible on the STEM maps. Where possible administrative data was included down to [http://www.iso.org/iso/english_country_names_and_code_elements ISO-3166] administrative level 2 (equivalent of Counties in the U.S.). In some cases we were only able to obtain data at administrative level 0 (country level data). For those instances were we do have admin 2 data, the relative population distribution between subdivision within the countries were validated against The [http://www.ornl.gov/sci/landscan/ ORNL LandScan 2007(TM)/UT-Battelle, LLC] data set. ''The LandScan 2007™ High Resolution Global Population Data Set copyrighted by UT-Battelle, LLC, operator of Oak Ridge National Laboratory under Contract No. DE-AC05-00OR22725 with the United States Department of Energy. The United States Government has certain rights in this Data Set. NEITHER UT-BATTELLE, LLC NOR THE UNITED STATES DEPARTMENT OF ENERGY, NOR ANY OF THEIR EMPLOYEES, MAKES ANY WARRANTY, EXPRESS OR IMPLIED, OR ASSUMES ANY LEGAL LIABILITY OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR USEFULNESS OF THE DATA SET.''
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| − | STEM itself '''''does not''''' include the LandScan(TM) data. In fact, ''in all cases'' the total population at the national level is based on national census facts by country ''and not based on LandScan(TM)''. Almost all the census Facts are referenced to (or scaled to) year 2006. For users interested in creating, on their own, a new STEM population set referenced to LandScan(TM) itself, LandScan(TM) Dataset licenses are available free of charge for U.S. Federal Government, for United Nations Humanitarian efforts, and educational research use. A user defined data set could, if desired, also replace the administrative divisions in STEM with a user defined grid of arbitrary resolution.
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| − | Development of STEM is supported, in part by the U.S. Air Force Surgeon General’s Office (USAF/SG) and administered by the Air Force District of Washington (AFDW) under Contract Number FA7014-07-C-0004. Neither Eclipse, the United States Airforce, IBM, nor any of their employees, nor any contributors to STEM, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of the stem data sets. The Air Force has not accepted the products depicted and issuance of a contract does not constitute Federal endorsement of the IBM Almaden Research Center.
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| − | ====Population Models====
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| − | Even thought the population data is based on a particular set of facts derived or interpolated from a census in some year, you may want to do a simulation in a different year. Even if you replace the data included in STEM with your own data set you may want to study years other than the year in which that data set was "considered valid". For this reason, population in STEM is not represented as a static label value. Population is, instead, represented by a '''population model'''. The purpose of a population model is to handle general effects on a population that are not caused by specific a disease outbreak. Right now, there is only one Population Model available that allows you to define a background birth and death rate for your scenario. Some things to note:
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| − | 1. Background birth rate and death rate are no longer available within the standard disease models. They have been moved to the Population Model. So for old scenarios where you had your birth/death rate defined you need to create a new population model, specify your birth/death rate and drag it into your scenario.
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| − | 2. To create a population model, go to the menu (New -> Population Model). '
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| − | 3. A population model is just like a disease model (it ends up under decorators in the project explorer) and it will store its own log files under its name in the log folder
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| − | 4. Disease models and population models should work well together and synchronize up background birth/deaths and disease deaths among each other each iteration. If you don't have a population model in your scenario, the background birth/death rate is 0. When 2 or more diseases are running simultaneously, they will incorporate each other's disease deaths into their calculations.
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| − | ===Scenarios===
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| − | User designed scenarios include selected denominator data, initial conditions, geographic data (a region of the world or the entire world), mathematical disease models, a solver, start and stop dates, etc. These scenarios themselves are represented in the Eclipse framework so they too can be build upon and exchanged. So STEM is intended to provide a common collaborative platform that enables sharing; the import and export of models that they can be easily exchanged among researchers. A researcher who has developed a detailed country model that includes population demographics can import a component with specialized disease mathematics from another researcher and combine the two. The new combination can then be re-exported (with descriptive metadata) for others to use. The design goal of STEM is for the country sub-model to be a standardized community resource maintained and refined by many different contributors. Over time, the country model will become more accurate, detailed, and valuable. With data available as plug-ins, researchers will be free to contribute data in their field of expertise. Thanks to Eclipse, researchers will find it easier to compare and share different models because their underlying components will be the same. On the [http://www.eclipse.org/stem/ STEM homepage] you can find links to some scenarios that you can import, run, and modify in this way.
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| − | === The STEM Community ===
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| − | Realizing the potential of STEM as an open source tool depends upon the involvement of researchers across settings. STEM developers are working closely with early leaders in the STEM community to provide them with the tools they need.
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| − | '''VERMONT'''
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| − | Researchers in Vermont are using STEM to model disease outbreaks in the state. They have created a model at the town/city level and are using transportation corridors (interstates and highways) as the pathways for disease spread. Currently they are investigating the potential spread of Pandemic Flu (based on Spanish Flu disease parameters) in a variety of scenarios and are examining how various interventions would mitigate the spread of the disease. In the future, they hope to look at how environmental changes will affect the emergence and spread of zoonotic infections.
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| − | The Vermont researchers are
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| − | *Joanna "Jo" Conant. Jo graduated from Middlebury College in 2003 and is now a medical student at the University of Vermont College of Medicine. She is considering a career in Public Health, though also exploring other specialties. An avid skier, Jo moved from the deserts of Phoenix, Arizona, to the mounts of Vermont, where she enjoys skiing 100+ days each year. She now lives in Warren, Vermont, with her husband and dog.
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| − | *Charles "Chuck" Hulse. Chuck graduated from Bucknell University in 1982, received his PhD in Chemistry from the University of Virginia in 1989 and his MD from the University of North Carolina at Chapel Hill in 1995. He completed his family medicine residency at the Department of Family Medicine of the University of Vermont College of Medicine in 1998. After serving as chief resident, he joined the facult and is now an Associate Professor of Family Medicine. A native of Eastern Long Island, Chuck has an intense interest in nature and is an aspiring nature photographer. He lives with his family in the beautiful Champlain Islands where he raises heirloom vegetables, fruits and berries, bees, chickens, goats, and sheep.
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| − | === The STEM Development Team ===
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| − | *James H. Kaufman, Ph.D., is manager of the Public Health Research project in the Department of Computer Science at the IBM Almaden Research Center. He received his B.A. in Physics from Cornell University and his PhD in Physics from U.C.S.B. He is a Fellow of the American Physical Society. During his research career Dr. Kaufman has made contributions to several fields ranging from simulation science to magnetic device technology. His scientific contributions include work on pattern formation, conducting polymers, diamond like carbon, superconductivity, experimental studies of the Moon Illusion, and contributions to distributed computing, privacy protection, and grid middleware. His current research interests include Public Health, Interoperable Health Information Infrastructure, Electronic Health Records, and Epidemiological Modeling. His group is currently working on open source tools for public health including the Spatiotemporal Epidemiological Modeler (STEM) project available through the Eclipse Foundation. He is one of the original creators of STEM. (kaufman@almaden.ibm.com)
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| − | *Stefan Edlund is a senior software engineer in the Healthcare Research team at IBM Almaden develoing new technologies related to the public health domain. Stefan has over 10 years experience in IBM, having worked on a broad area of technologies such as DB2 query visualization, intelligent personal calendars, exploratory Lotus applications, location based services and more recently in content management and content replication as well as development of an email search and discovery product (IBM eDiscovery Manager). Stefan's current research interests include development of new STEM disease models including diseases involving multiple populations and multiple serotypes. Stefan holds a MS degree in computer science from the Royal Institute of Technology in Stockholm. He currently holds over 15 US patents. (edlund@almaden.ibm.com)
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| − | *Matthew Davis is a graduate of the University of Oklahoma where he earned both his BS and MS in computer science. During his time at OU, he spent a significant amount of time developing a series of web portals with the aim of aiding in building student communities. These applications were later open sourced and adopted by several universities across the United States. Additionally, he spent time teaching in the computer science department at OU, primarily as an instructor for a senior-level computer graphics course. During the summer of 2006, Matt participated in the IBM Extreme Blue internship program at Almaden. He later joined IBM Research where, as an Eclipse Committer on the OHF project he helped develop the Eclipse Open Healthcare Framework (OHF) Bridge. The OHF Bridge is a web services platform that enables access to OHF actor profiles from non-Java applications. Matt is currently working on a server side implementation of STEM. (mattadav@us.ibm.com)
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| − |
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| − | *Judith V. Douglas is the lead technical writer for STEM. She coordinates STEM documentation and is coauthor on many of the STEM scientific publications. She holds a master's degree from the Johns Hopkins Bloomberg School of Public Health and has published extensively in healthcare informatics. (judydouglas@comcast.net)
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| − |
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| − | *Daniel Ford, Ph.D., is a Research Staff Member in the Healthcare Informatics Department at IBM Almaden and is currently on assignment at the IBM Watson Research Center in New York. (daford@almaden.ibm.com)
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| − | *Justin Lessler, Ph.D., is in the Epidemiology Department at the Bloomberg School of Public Health at the Johns Hopkins University in Baltimore, Maryland.
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| − | *Iris Eiron was a researcher at the IBM Almaden Research Lab before relocating to the IBM Research Lab in Haifa, Israel, where she continues to contribute to the development and implementation of a national health care information infrastructure.
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| − | *Ohad Greenshpan is part of the Healthcare and Life Sciences group at the IBM Haifa Research Labs. Mr. Greenshpan is an MSc student for Bioinformatics in Ben-Gurion University, concentrating on Protein Folding algorithms and Structural Bioinformatics. Prior to joining IBM, Mr. Greenshpan was a member of the Genecards team in Weizmann Institute of Science.
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| − | *Yossi Mesika is a research staff member in Healthcare and Life Sciences, at the IBM Haifa Research Lab in Haifa, Israel. He received his B.Sc. in Computer Engineering from Technion, the Israel Institute of Technology in Haifa. He joined IBM in 2003 and has contributed to several Healthcare projects that deal with interoperability. An Eclipse committer, he has also contributed to the WADO component in the Eclipse Open Health Framework. (mesika@il.ibm.com)
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| − | *Nelson A. Perez is a software engineer for the Healthcare Informatics Research Group at IBM Almaden. Nowadays, Nelson is mostly interested in software engineering, distributed computing, social computing, and web technologies. He holds an MS degree in computer science from the University of California at Riverside.
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| − | *Roni Ram is a research staff member in the Healthcare and Life Sciences group, IBM Haifa Research Lab. She received her B.Sc. and M.Sc. in computer sciences from the Technion, Israel Institute of Technology in Haifa, Israel. Since joining IBM in 1996, she has worked on several projects involving user interfaces and IP telephony. For the last three years, she has been working on interoperability among health care organizations with focus on the public health domain.
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| − | *John Thomas is a Java developer for IBM. He was previously one of the lead programmers for the IBM Almaden TSpaces project and also a member of the OptimalGrid Project at the Almaden Research Center. (jthomas119@gmail.com)
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