Difference between revisions of "OHF STEM"

Revision as of 13:51, 29 September 2008

STEM Contents

• Contents
  • What's New
  • Introduction
  • FAQ
  • Installation Guide
  • STEM Design Document
  • Welcome STEM Developers Guide
  • TUTORIALS
  • User Guide (Future)
  • OHF STEM Website
  • Disclaimer Disclaimer

What's New

Sept 2008...ANALYSIS PERSPECTIVE

The Analysis and Validation Perspective is a new STEM Perspective that supports a variety of analysis, fitting, and comparison functions across multiple simulations and data sets.

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.

July 2008...Scenarios Caching

This new feature caches 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.

May 2008...Several important new features are now implemented.

• Creating running multiple Experiments
  • You can now specify a set or sequence of parameters to run multiple experiments.
  • Users can create a collection of modifiers for a model and link them to a scenario.
  • This makes it possible to 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.
• A new Mixing Model for Transportation builds on STEM’s two transportation network models.
• We fixed a major bug in running continent level scenarios
  • This will 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.
• Several Improvements have been made to the Editors to allow better drag and drop, deletion, etc. (coming soon - email a scenario!!)
• Performance improvement to the graphics and other processes.
• Population data has been revalidated and locations in 37 countries that were missing population data now have better estimates.

Introduction

What is 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 II can find useful tools, conventions, and design information in the Welcome STEM Developers article.

The STEM code repository will be hosted on the Eclipse OHF code repository.

A recent publication on STEM: Ford, D.A., Kaufman, J.H., 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)

About the STEM 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.

• Daniel Ford, Ph.D., is a Research Staff Member in the Healtcare Informatics Department at IBM Almaden and is currently on assignment at the IBM Watson Research Center in New York.

• 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.

• 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 in 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 IBM, Mr. Greenshpan was a member of the Genecards team in Weizmann Institute of Science.

• 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 (UCR).

• Roni Ram is a research staff member in the Healthcare and Life Sciences group, IBM Haifa Research Lab (HRL). She received her B.Sc. and M.Sc. in computer sciences from the Technion, Israel Institute of Technology in Haifa, Israel. Ms. Ram joined IBM HRL in 1996 and 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. Mr. Thomas can be reached by e-mail (jthomas119 @ gmail.com)