Spatiotemporal Epidemiological ModelerA tool for spatiotemporal modeling of infectious agents across the United States.
Date Posted: April 21, 2005
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What is the Spatiotemporal Epidemiological Modeler?
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 could aid
in understanding, and potentially preventing, the spread such diseases.
Policymakers responsible for creating strategies to contain
diseases and prevent epidemics need an accurate understanding of
disease dynamics and the likely outcomes of preventative 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 every county in the
United States. It comes with data about county borders, populations,
shared borders (neighbors), interstate highways, state highways, and
airports. This data comes from the public U.S. census TIGER files.
STEM is designed to make it easy for developers and researchers to plug
in their own 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 the weights of 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.
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| | About the technology author(s):
Daniel Ford is the former Manager of the Web Technologies Department at IBM Almaden and is
currently on assignment at the IBM Watson Research Center in New York. He has a wide
range of interests and research activities, including software engineering, bioinformatics,
and robotics. Dr. Ford received his Ph.D. in computer science from the University of Waterloo.
James H. Kaufman is manager of the Healthcare Informatics
project in the Department of Computer Science at the IBM Almaden
Research Center. He received his B.A. in physics from Cornell
University and his Ph.D. in physics from U.C.S.B. Dr. Kaufman is a
fellow of the American Physical Society. During his career at IBM
Research, he 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.
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.
Iris Eiron joined IBM in January 1998, after receiving her MS.C. in
computer science from the
Technion, the Israeli Institute of Technology. Ms. Eiron worked for the
IBM Israeli Research
Lab for three years. In December 2000, she joined the Almaden Research
Center. Her current interests include development and implementation of
a national health care information infrastructure.
Matthew Hammer is an undergraduate at the University of
Wisconsin. He is majoring in computer science with an interest in the
field of programming languages. Mr. Hammer has worked as an IBM
research intern in the summers of 2003 and 2004. He enjoys working on
interesting software systems for research projects. After graduation,
he plans to study programming languages at the graduate level.
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