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Home » Archived » OHF » STEM Version 0.3.0 now available
STEM Version 0.3.0 now available [message #587504] Wed, 10 September 2008 13:24
Daniel Ford is currently offline Daniel FordFriend
Messages: 148
Registered: July 2009
Location: New York
Senior Member
The latest version, 0.3.0 of STEM is now available for download.

What's new in this version?

Many, many bug fixes and minor enhancements, plus several new significant
a.. Experiments. An Experiment is a combination of a Scenario and a set of
Modifiers. A Modifier is a definition of how to systematically change the
value of some element of a Scenario in a specific manner. The intent is to
be able to specify how to create a sequence of derivative Scenarios from the
base Scenario referenced by the Experiment. These derivative Scenarios are
created by applying the Modifiers to the base Scenario. An Experiment can be
executed like Scenario creating a Batch which then spawns off separate
Simulations from each of the derivative Scenarios. Currently, the
Simulations in a Batch are run sequentially, parallel execution is planned
for a future release.
a.. A preliminary implementation of Triggers (Predicate & Action). A
Trigger is a combination of a Boolean Predicate and an Action. At each cycle
in the simulation, if the Boolean expression is True, then the Action is
"triggered". An action is a "Modifier" and can change arbitrary components
of a running simulation including things like the values of labels in the
graph (e.g., to close an airport) or the parameters of a disease model.
Currently, the predicates are limited to testing the absolute or elapsed
time value. This limitation will be removed in a future release.
a.. Various improvements to the visualizations including display of the
graph edges
a.. A new Analysis and Validation Perspective that supports a variety of
analysis, fitting, and comparison functions across multiple simulations and
data sets. This perspective includes:
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, is 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 Root Mean Square (RMS) comparison function shows the RMS
difference between 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. If also shows the
incidence or "newly infectious count" for the aggregated data. gy/ohf/stem/

Daniel Ford
IBM Almaden Research Center
San Jose, CA
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