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Difference between revisions of "STEM"

(Publications and Presentations on STEM)
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=== Future STEM Releases (Planned)  ===
 
=== Future STEM Releases (Planned)  ===
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'''V1.2.0 RC 4 is up (04/25/2011)'''
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'''V1.2.1'''
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Planned Date 07/31/2011
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Planned Features:
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* Bug Fixes from V1.2
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* New Earth Science Data (2009) including new data and updated classes
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* Data Plugin creation utility
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* Shape File Import Utility
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* Interventions (Enhanced Triggers, Modifiers, Predicates) 
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'''V1.3.0'''
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Planned Date 10/31/2011
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Planned and Tentative Features:
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* Graphical Editor V2
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* Ten years of Earth Science Data (2000-2010) available as STEM '''Features'''
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* Running Distributed STEM
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* Integrating external models for study of food based transmission
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* New Interaction Graphs
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'''V1.3.1'''
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Tentative Date 1/15/2012
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Tentative Features:
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* Bug Fixes to 1.3.0
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* auto-documenting run parameters
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* parameter sensitivity analysis

Revision as of 12:30, 25 April 2011

STEM TOP BAR.gif

The Spatio-Temporal Epidemiological Modeler (STEM) is a tool designed to help scientists and public health officials create and use models of emerging infectious diseases. STEM uses mathematical models of diseases (based on differential equations) to simulate the development or evolution of a disease in space and time (e.g., avian flu or salmonella). These models could aid in understanding, and potentially preventing, the spread of such diseases. STEM also comes pre-configured with a vast amount of reference or denominator data for the entire world. By using and extending the data and models in STEM it is possible to rapidly prototype and test models for emerging infectious disease. STEM also provides tools to help you compare and validate your models. As an open source project, the ultimate goal of STEM is to support and encourage a community of scientists that not only use STEM as a tool but also contribute back to it. STEM is designed so that models and scenarios can be easily shared, extended, and built upon.

Links

  • Full length STEM Tutorials on YouTubeTM
  1. In English
  2. In Hebrew
  3. In Japanese
  4. In Spanish

STEM Documentation: Getting Started

STEM Documentation: Available Models

STEM Documentation: Tutorials

STEM Documentation: Advanced Guides

What's New

STEM V1.2.0 RC4 is up

Running STEM Headless

See the new documentation on how to run STEM in Headless mode. This is the way you can remotely run massive numbers of simulations!!

Global model for Anopheles Mosquito Risk is now available

This model uses earth science data and was calibrated based on an Anopheles survey in Thailand . In an attempt to identify environmental conditions favoring malaria transmission, a mathematical risk index prediction model for Anopheles was created using four primary factors: rainfall, temperature, elevation, and vegetation. To assess the performance of our model, we compared the predicted risk generated by our model to data from four separate regions around the globe. (a preprint is available on request). Mosquito risk models like this are often used to asses Malaria risk. We hope to use this model as input to a more sophisticated mathematical model for Malaria and as an example of how to use STEM to study Zoonotic disease. click here for downloadable scenario

Import your own Graphs using the Pajek standard format

A new example project is available that demonstrates the new feature Importing a Pajek Graph. To see the example Please see the Downloadable Scenario (New) Pajek Import Example Please also see the documention on how to use Pajek with STEM. The PajekNetGraphGenerator was created and developed by the department of Biological Safety of the Federal Institute for Risk Assessment in Germany.

Multi-Population Disease Model for Download

A new scenario for pulationExample.zip multi-population disease spreading between anopheles mosquitoes and humans on a square lattice is now available on STEM for downloading. Please also see the documentation Multi-Population Example

The classic Macdonald-Ross (Malaria) disease model is now available

For developers who want to check out the code directly from the STEM SVN branch, there are two new new plugins available:

org.eclipse.stem.diseasemodels.vector
org.eclipse.stem.ui.diseasemodels.vector

If you add them to your STEM launch configuration, you'll have a new "Macdonald Ross" disease model available to you. Currently, the model is the most basic possible, an SI model that does not take into account the latent period (both human and vector) as well as immunity. The model comes directly out of the standard Anderson & May textbook, chapter 14.3 (basic model for malaria), the differential equations on page 394. In the future we will add more advanced vector models to this plugin.


Release 1.1.1 Offers New Features

January 27, 2011. In addition to many bug fixes, the 1.1.1 release of STEM contains new global earth science data, including elevation and monthly rainfall, vegetation and temperature. To see an example of how the earth science data can be used to model mosquito densities in Asia, you can download an example scenario here (requires the global earth science models as well as the global geography models):


New Integration tests and framework now available

The plugin org.eclipse.stem.tests.core now contains a test suite called "ScenarioIntegrationTests". Running the suite as a JUnit plugin test does the following:

1. It reads each project stored under the resources folder in the plugin (right now there's only one, "BasicTests")
2. It runs each scenario in the project and checks to make sure the models generate "valid" results. By valid we mean: 1. That the compartment states (e.g. S+I+R) adds up to the total population. 2. That no compartment ever goes negative. 3. 
That the log files generated by the  scenarios agrees with a reference stored under the "Recorded Simulations/IntegrationTest" folder.

The scenarios tested right now includes basic single location SI, SIR SEIR scenarios, scenarios with mixing between two nodes, scenarios with mixing/migration between two nodes, scenarios having air transportation and scenarios including a percentage of the population being inoculated. More tests will be added soon (e.g. multi-population disease models, population model tests) to test all aspects of STEM end-to-end. For developers, please make sure you test your code by running (at least) the integration tests to avoid breaking things.

Automated Experiment Tutorial

September 2010. A tutorial for the Automated Experiment function is now available at http://wiki.eclipse.org/Running_an_Automated_Experiment. The tutorial explains how this function allows the user to sweep through a range of parameter values for a disease model, running and logging the results for every possible combination of parameter values.

This tutorial will be of special interest to those who have downloaded the sample project using this feature at http://www.eclipse.org/stem/download_sample.php?file=AutomatedExperimentExample.zip and read the documentation for that sample project at http://wiki.eclipse.org/Sample_Projects_available_for_Download#Automated_Experiment_Example.

Hypothetical Zombie Pandemic: New (And Whimsical)

August 2010. In a fun test of STEM's ability to rapidly prototype new disease models, T. Parviainen used STEM to enter a 48 hour programming competition. He created a visualization for a hypothetical zombie pandemic: http://teropa.no.de/. To read more about the epidemiology of zombies see also this paper by Philip Munz et al. (http://www.mathstat.uottawa.ca/~rsmith/Zombies.pdf) Follow the zombie thread on the news group. Read comments about Parviainan’s entry at http://nodeknockout.com/teams/teropa.

Earth Science Data

Global Elevation

August 2010. STEM now includes earth science datasets for global surface elevation, total rainfall, land surface temperature, and vegetation. Inclusion of these earth science data sets provides the foundation for simulation of mosquito-borne and other zoonotic diseases. Monthly data is provided for total rainfall, land surface temperature, and vegetation. Calculations provided within the earth science data sets include surface roughness calculations such as average, standard deviation, maximum, minimum, range, skewness, kurtosis, and root mean square.


Global Total Rainfall Animation (2009)
  • Surface Elevation.
    Data for surface elevation (in meters) were obtained from the NOAA Global Land One-Kilometer Base Elevation Project (GLOBE).
    The highest possible elevation was 8,752 meters and the lowest possible global surface elevation was -407 meters.
    Bathymetric elevation data is not included in the global surface elevation data.


Global Land Surface Temperature Animation (2009)
  • Total Rainfall.
    Data for total rainfall (in millimeters) were obtained for each month from the NASA Earth Observatory (NEO).
    The NEO data is derived from the The Tropical Rainfall Measuring Mission (TRMM).
    The highest possible value was 2000.0 millimeters of rainfall and the smallest 1.0 millimeter of total rainfall for each month.
    Total rainfall data are not available for countries and regions that are located above 35 degrees North
    and below 35 degrees South Latitude (as evidenced in the image, places without data are in black).


Global Vegetation Animation (2009)
  • Land Surface Temperature.
    Data for land surface temperature (in degrees Celsius) were obtained for each month from the NASA Earth Observatory (NEO).

The source of the land surface temperature data is the NASA Moderate Resolution Imaging Spectroradiometer Terra/MODIS.
The highest possible temperature is 45.0 degrees Celsius and the lowest is -25.0 degrees Celsius.


  • Vegetation. Data for vegetation were obtained from the NASA Earth Observatory (NEO).
    The data source for the normalized difference vegetation index [NDVI] (250m) is Terra/MODIS.
    NDVI provides continuity with NOAA’s AVHRR NDVI time series record for historical and climate applications.
    Values for vegetation based on NDVI are also described in wikipedia Normalized Difference Vegetation Index.
    The highest possible index for global vegetation is an NDVI index of 0.9 and the lowest an NDVI index of -0.1.


  • Acknowledgments

1. Elevation data: GLOBE Task Team and others (Hastings, David A., Paula K. Dunbar, Gerald M. Elphingstone, Mark Bootz, Hiroshi Murakami, Hiroshi Maruyama, Hiroshi Masaharu, Peter Holland, John Payne, Nevin A. Bryant, Thomas L. Logan, J.-P. Muller, Gunter Schreier, and John S. MacDonald), eds., 1999. The Global Land One-kilometer Base Elevation (GLOBE) Digital Elevation Model, Version 1.0. National Oceanic and Atmospheric Administration, National Geophysical Data Center, 325 Broadway, Boulder, Colorado 80305-3328, U.S.A. Digital data base on the World Wide Web (URL: http://www.ngdc.noaa.gov/mgg/topo/globe.html) and CD-ROMs.

2. Rainfall, Temperature, and Vegetation: Data is derived from NASA’s Earth Observatory. Thank you NASA!!


Currently, only earth science data for 2009 is built into STEM for total rainfall, land surface temperature, and vegetation. Provided in the thumbnails are animations of these earth science data sets over the year.

New Graph Wizard

June 2010. STEM now supports user creation of custom lattices via the New Graph wizard. The wizard makes use of "Graph Generators," a new concept. A graph generator is a pluggable component that is able to generate a graph (a collection of nodes and edges) either algorithmically or from an external file. Currently, we have implemented an abstract Lattice Graph Generator with a Square Lattice Implementation. A user can specify the size of the lattice as well as several options for how the nearest neighbor (Common Border) are organized. In the future we plan to also support creating a New Graph From File.

Infectors, Inoculators, and Population Initializers Now Support UIDs for Arbitrary Graphs

June 2010. Several wizards (Infector/Inoculator, Population Model, Population Initializer) now use a consolidated location picker dialog that gives users the option of selecting any location within the currently selected project. This allows the user to pick for instance one of the automatically generated grids on the lattice graph. Whatever UIDs exist in the user’s graph can be applied. The dialog also filters down the number of possible locations dramatically from all regions in the world to only the ones that are applicable in the current project.

External Files Implemented for Creating New Infectors and Inoculators

June 2010. There are now several new options available when a new infector or inoculator is created. First, it is possible to create an infector or inoculator by importing data from external files in the form of the comma separated files used by the STEM logger. A collection of infectors/inoculators can either be created from the first, last or any manually specified row from such files. We have found this feature extremely valuable when "boot-strapping" the initial state of a disease from a steady state (e.g., the seasonal flu state of the population in the summer time for northern hemisphere).

News Archives

For past news items, go to the STEM Archives at Older News

About STEM

A Global H1N1 Simulation.

What is the Spatiotemporal Epidemiological Modeler (STEM)? 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.

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.

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.

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.

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.

The original version of 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).

New developers who want to work on STEM can find useful tools, conventions, and design information in the Welcome STEM Developers article.

The STEM code repository is hosted on the Eclipse Technology code repository.


More About STEM

STEM Plugins

STEM takes advantage of Equinox (the Eclipse implementation 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 - if 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 models for specific diseases including a global model for seasonal influenza. Any model in STEM requires a solver to integrate the differential equations. Today we have both Finite Difference and Runge Kutta mathematical solvers for every model. The solvers themselves are provided as plugins.

Data

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 disease 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

Population

World Population Density.

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 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 where we do have admin 2 data, the relative population distributions between subdivisions within the countries were validated against The 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.

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.

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.

Population Models

Even though 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 a specific 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:

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.

2. To create a population model, go to the menu (New -> Population Model).

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

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 two or more diseases are running simultaneously, they will incorporate each other's disease deaths into their calculations.

Scenarios

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 STEM homepage you can find links to some scenarios that you can import, run, and modify in this way.

The STEM Community

STEM LOGO.jpg

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.

How You Can Contribute to STEM

New contributors to STEM are always welcome (please contact the developers). This includes not only 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.

To contribute to STEM, please use the standard Eclipse process. Open a "bug" in our bugzilla (https://bugs.eclipse.org/bugs/) A bug can be more than just a new defect - it can also be a new feature or other contribution. You can attach your contribution as a "patch" to your bug (http://wiki.eclipse.org/index.php/Bug_Reporting_FAQ) Please also feel free to e-mail the STEM development team, many of whom are Eclipse Committers The STEM Development Team. For those interested in joining the project, we also have a weekly phone call and a newsgroup, etc.

STEM Conference Call, Newsgroup, and Forum

The STEM community conference call occurs most Wednesdays at 1PM Eastern Time (10AM Pacific Times). For more information, or if you wish to join, please send mailto:judyvdouglas@verizon.net

To receive email updates, go to STEM Newsgroup

To join in the discussion, go to STEM Forum


The STEM Development Team

  • 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. James is the Eclipse Project Lead for STEM. 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 and a Distinguished Scientist of the ACM. He is one of the original creators of STEM.(kaufman@almaden.ibm.com)
  • Daniel Ford, Ph.D., is a Research Staff Member in the Department of Computer Science at the IBM Almaden Research Center. Daniel is the Eclipse Project Lead for STEM. He designed and implemented the initial versions of STEM, including the core composable graph framework that gives STEM its ability to represent arbitrary models. He received his Ph.D. in Computer Science from the University of Waterloo. (daford@almaden.ibm.com)
  • Stefan Edlund is a senior software engineer in the Healthcare Research team at IBM Almaden developing 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)
  • 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)
  • 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 master's degrees from Northwestern University and the Johns Hopkins Bloomberg School of Public Health and has published extensively in healthcare informatics. (judyvdouglas@verizon.net)
  • Barbara A. Jones, Ph.D., is a theoretical physicist at the IBM Almaden Research Center. She has contributed many mathematical algorithms, and has a long-term interest in applying advanced mathematical and physics methods to problems in epidemiology.
  • Justin Lessler, Ph.D., is in the Epidemiology Department at the Bloomberg School of Public Health at the Johns Hopkins University in Baltimore, Maryland.
  • 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)
  • 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.
  • Arik Kershenbaum works part-time at the IBM Haifa Research Lab and is a doctoral student at the Department of Evolutionary and Environmental Biology at the University of Haifa, Israel (personal website). He is developing add-ons and applications for STEM in the fields of zoonotic disease spread, particularly vector borne diseses. In addition, he is looking at other collaborative applications for the STEM framework in the fields of ecology and zoology, where ecosystems can be represented as a graph network. He has an undergraduate degree in Natural Sciences from the University of Cambridge in England.
  • Werner Keil is freelance IT Architect, Eclipse RCP Developer and Consultant having worked for governments and Global 500 companies worldwide. He has worked for more than 20 years as project manager, software architect, analyst and consultant on leading-edge technologies for Banking, Insurance, Telco/Mobile, Media and Public sector/Healthcare. Werner is committing member of the Eclipse Foundation and Babel Language Champion (German). As well as active member of the Java Community Process, including his role as UCUM/Java Lead, JavaEE 6 EG and Executive Committee Member(SE/EE). (werner.keil@gmx.net)
  • Matthias Filter is a research staff member at the Federal Institute for Risk Assessment(BfR), Germany.
  • Jan Wigger is a research staff member at the Federal Institute for Risk Assessment(BfR), Germany. He studied bioinformatics at the University of Hamburg, Germany and worked in the field of mathematical modelling and molecular dynamics simulations for several years. Jan is an Eclipse Committer and works on modelling food chain safety.
  • Armin A. Weiser, Ph.D. is a research staff member at the Federal Institute for Risk Assessment(BfR), Germany, and is also a committing Eclipse member. He studied mathematics and received his Ph.D. in theoretical biology from the HU Berlin.
  • Dirk Reuter, Ph.D. is a research staff member at the Federal Institute for Risk Assessment(BfR), Germany and also an Eclipse Committer. He studied physics and received a Ph.D. in biochemistry from the University Cologne. He has worked in electrophysiology,

studying properties and behaviour of ion channels. He has also been involved in gene expression profiling projects on the Affymetrix platform.

UNIVERSITY OF PENNSYLVANIA

  • Isabel Fan is a student at the University of Pennsylvania, pursuing Bachelor's Degrees in Computer Science and Biology (Computational Biology Concentration).

VERMONT'

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.

The Vermont researchers are

  • 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.
  • 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.

The STEM Developers Emeritus

  • 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. Together with Matthew Hammer and James Kaufman, Iris was one of the creators of the original version of STEM.
  • Matthew Hammer was an undergraduate at the University of Wisconsin. He is majoring in computer science with an interest in the field of programming languages. Mr. Hammer worked as an IBM research intern in the summers of 2003 and 2004. Together with Iris Eiron and James Kaufman, Matthew was one of the creators of the original version of STEM.
  • 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.
  • Nelson A. Perez was 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.
  • 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)

Eclipse Project Mentors

Our Eclipse Project Mentors are: Ed Merks mailto:ed.merks@gmail.com and Chris Aniszczyk mailto:zx@eclipsesource.com

Publications and Presentations on STEM

Talks Online Featuring a recent talk at Epidemics 2009

Ahued, J, Morales, J., Renly, S., Edlund, S., Kaufman, J. "Improving Disease Surveillance Capabilities through a Public Health Information Affinity Domain", accepted ACM Digital Library and 1st ACM International Health Informatics Symposium, November 11-12, 2010 Arlington, VA

Edlund, S., Davis, M., Kaufman, J. "The Spatiotemporal Epidemiological Modeler", accepted ACM Digital Library and 1st ACM International Health Informatics Symposium, November 11-2, 2010 Arlington, VA

Renly, S., Kaufman, J., Ram R., "Improving Disease Surveillance Capabilities in IHE Health Information Exchanges", IADIS International Conference e-Health, June 2009, Algarve, Portugal.

Edlund, S. et al. "The Spatiotemporal Epidemiological Modeler", Workshop on Frontiers in the computational modeling of disease spreading, ICCS 2010, Amsterdam, May 31 - June 2, 2010

Keil WP. "Spatio-Temporal Epidemiologic Modeler (STEM)", Eclipse DemoCamp 2009, Vienna, Austria, November 2009. [1]

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

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. [2]

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.

Hulse, C. L., Conant, J. L., Kaufman, J. H., Edlund, S. B., Ford, D. A., “Development and Utilization of a Spatial and Temporal Modeling System to Investigate Disease Outbreaks in Vermont”, PHIN 2009 [3]

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." submitted eJHI.

Keil WP. 2009. "The spatiotemporal epidemiological modeller (STEM)." Presentation at epicenter 2009 Conference, Dublin, Ireland, Aug 28, 2009.

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." in preparation.

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.

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.

Kaufman, J.H., Edlund S, Ford, D.A. "Spatio-Temporal Epidemiologic Modeler: Application in Middle Eastern Countries", PHIFP colloquium at U.S. Centers For Disease Control, Atlanta. May 8, 2009

Kaufman, J.H., Edlund S, Ford, D.A., Renly, S., R. Ram, Y. Messika, et al. "Spatio-Temporal Epidemiologic Modeler: Evaluation of 10 years of Influenza Data from Maccabi Healthcare". Presentation at Israel Centers for Disease Control, Tel Aviv, Israel. April 16, 2009

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.

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.

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.

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.

Keil WP, Ford DA, Kaufman JH. 2007 (In press), "Eclipse OHF STEM", Eclipse Magazin, S&S Verlag, July 2007

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 (17Jan2006)

Keil WP. 2005. "Eclipse Open Health Framework (OHF)." Presentation at JavaPolis 2005 Conference, Antwerp, Belgium, Dec 12, 2005.


Future STEM Releases (Planned)

V1.2.0 RC 4 is up (04/25/2011)

V1.2.1

Planned Date 07/31/2011

Planned Features:

  • Bug Fixes from V1.2
  • New Earth Science Data (2009) including new data and updated classes
  • Data Plugin creation utility
  • Shape File Import Utility
  • Interventions (Enhanced Triggers, Modifiers, Predicates)


V1.3.0

Planned Date 10/31/2011

Planned and Tentative Features:

  • Graphical Editor V2
  • Ten years of Earth Science Data (2000-2010) available as STEM Features
  • Running Distributed STEM
  • Integrating external models for study of food based transmission
  • New Interaction Graphs


V1.3.1

Tentative Date 1/15/2012

Tentative Features:

  • Bug Fixes to 1.3.0
  • auto-documenting run parameters
  • parameter sensitivity analysis

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