Notice: this Wiki will be going read only early in 2024 and edits will no longer be possible. Please see: https://gitlab.eclipse.org/eclipsefdn/helpdesk/-/wikis/Wiki-shutdown-plan for the plan.
STEM TEMP
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.
Contents
STEM User Guides
- STEM FAQ
- Installation Guide
- TUTORIALS
- STEM Design Document
- Welcome STEM Developers Guide
- Introduction to Compartment Models
- STEM Website
- Disclaimer
Introduction
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.
What's New
High Resolution Data
High resolution data is now available for Russia and Mexico.
Data for Mexico goes to Administrative Level 2. Work is underway to map this to zip codes.
STEM Video Tutorials (English & Spanish) on YouTube(TM)
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.
And here's the Spanish Language Version too http://www.youtube.com/watch?v=3S5DbjCHsx4
- July 2009...POPULATION MODELS
Population Models have been implemented.
- July 2009...STEM SOLVERS NOW SEPARATE PLUGINS
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:
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.
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.
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.
- July 2009...STEM NOW A TOP-LEVEL ECLIPSE TECHNOLOGY PROJECT
- STEM has now been moved into its new status.
- April 2009...NEW STATUS COMING FOR STEM
- 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.
- December 2008...NEW CORE INTEGRATION ENGINE
- 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.
- September 2008...ANALYSIS PERSPECTIVE
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.