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Revision as of 08:24, 14 July 2009 by (Talk | contribs) (What's New)

STEM Contents

Weekly STEM Conference Call

The STEM community has a weekly conference call. For more information, or if you wish to join, please email

What's New



STEM will moving in the next few weeks. Eclipse has approved STEM as a top-level Eclipse Technology Project. Watch this space for updates!

The air travel model for every US airport is now available to drag in and use.

January 2009...MOVING FORWARD

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.


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.


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.


1. Estimating Model Parameters from External Data.
2. Epidemic Analysis
3. RMS Comparison between data sets
4. Lyapunov Analysis

Model Parameter Estimation View

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:

* beta, the disease transmission rate
* alpha, the recovery rate
* epsilon, the incubation rate
* gamma,the immunity loss rate 

Dynamical Systems View (Lyapunov Analysis)

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.

Cross Model Comparison (RMS Compare)

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.

The Epidemic View

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.


In July...Scenarios Caching

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.

In May...A Host of New Features

Among these new features are ones that allow user to

  • Create and run multiple Experiments
    • Specify a set or sequence of parameters to run multiple experiments
    • Create a collection of modifiers for a model and link them to a scenario
    • Run a simulation from each newly created modifier in a series of simulations, i.e., to run in batch mode.
  • Import data from comma separated variable files and "play back" surveillance data in STEM as an imported disease model
  • Export the results of a simulation to comma separated variable files.

Other work was done to

  • Provide a new Mixing Model for Transportation that builds on STEM’s two transportation network models
  • Fix a major bug in running continent level scenarios
    • 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.
  • Improve the Editors allowing better drag and drop, deletion, etc. (coming soon - email a scenario!!)
  • Improve performance in graphics and other processes.
  • Revalidate population data and provide better estimates for locations in 37 countries previously missing population data.


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 OHF code repository.

More About STEM

The STEM Community

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.


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.

How You Can Contribute to STEM

New Contributors to STEM are always welcome (please contact the developers). This includes researchers interested in disease modeling but also experts in GIS data. We have Data for most of the world including maps, population, area etc.. In some cases our data goes down to admin level 2. In other cases we only have country level (admin 0) data. Data can only be contributed if it is available under terms compatible with the Eclipse Public License ( ).

Please go to This Page for a prioritized list of regions where we would most like higher resolution data.

Publications and Presentations on STEM

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.

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." Proposal under review for presentation at Epidemics 2, Athens, Greece, December 2009.

Kaufman J, Edlund S, Douglas J. 2009 (In press). "Infectious disease modeling: creating a community to respond to biological threats." Statistical Communications in Infectious Disease. The Berkeley Electronic Press.

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." Proposal accepted. HIC 2009, Frontiers of Health Informatics, Canberra, Australia, August 19-21, 2009.

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. "The cost of simplifying air travel when modeling disease spread," PLoS ONE 4(2): e4403. doi: 10.1371/journal.pone.004403.

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.

Ford DA, Kaufman JH, Eiron I, "An extensible spatial and temporal epidemiological modeling system," International Journal of Health Geographics 2006, 5:4 (17Jan2006)

The STEM Development Team

  • James H. Kaufman, Ph.D., is manager of the Healthcare Informatics project in the Department of Computer Science at the IBM Almaden Research Center. He is also a fellow of the American Physical Society. During his career at IBM Research, Dr. Kaufman has made contributions to several fields, including simulation science and magnetic device technology. His scientific contributions include work on pattern formation, conducting polymers, superconductivity, experimental studies of the Moon Illusion, as well as contributions to distributed computing and grid middleware. (
  • Stefan Edlund is a software engineer in the Healthcare Research team at IBM Almaden, interested in technologies for the public health domain. Stefan has over 10 years experience in IBM research, with more recent research focusing on the areas of information and content management. Stefan holds a MS degree in computer science from the Royal Institute of Technology, Stockholm. (
  • Matthew Davis is a member of the Healthcare Research team at IBM Almaden.
  • 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. (
  • Justin Lessler, Ph.D., is in the Epidemiology Department at the Bloomberg School of Public Health at the Johns Hopkins University in Baltimore, Maryland.
  • 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.
  • 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.
  • 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. (
  • 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.
  • 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.
  • 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. (
  • Judith V. Douglas supports the STEM Team in documentation and publication of their work. She holds a master's degree from the Johns Hopkins Bloomberg School of Public Health and has published extensively in healthcare informatics. (

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