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{bio,medical} informatics

Wednesday, August 03, 2005

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find related articles. powered by google. National Geographic News "Bird Flu" Could Be Slowed at the Source, Study Says

"Experts from U.S. universities and Thailand's Ministry of Health used a computer model to simulate an outbreak in rural Southeast Asia—the most likely place for a new, more deadly avian influenza strain to emerge."

"Anthony Fauci directs the National Institute of Allergy and Infectious Diseases at the National Institutes of Health in Bethesda, Maryland. He called the model valuable but noted that "any model is only as good as the assumptions put into that model, and these really are only assumptions.""

find related articles. powered by google. HHMI News Computer Model Could Help Prevent Flu Pandemic

" In the scientists' computer model, a single resident of a rural village in Thailand was infected with a mutated H5N1 virus capable of human-to-human transmission. Scientists say that an avian flu pandemic would likely begin in southeast Asia, and the researchers chose Thailand because the type of national data they needed was easily accessible. Using information on past influenza pandemics, they calculated the number of secondary cases that would occur from the original infected individual, the normal incubation period of the illness, and the speed with which the pandemic would spread.

They then added demographic information such as regional and national population size and age; numbers of households, schools and large companies; and distances that people travel to work and school. This gave them a map of how the virus might spread."

redux [04.28.04]
find related articles. powered by google. The Oregonian Mathematical supermodels refine epidemic predictions

"Most modern disease models were developed about 100 years ago, she said, in response to malaria epidemics. They use statistics and a series of equations to define in general terms how epidemics progress. In graphic form, Crandall said, the models draw smooth, continuous curves.

The Reed model is notably different. Technically speaking, it relies on parametric relationships and fractals, not differential equations and curves, Crandall said. Simply put, the new model is "discrete," not "continuous." It considers millions of interactions event by event -- as represented by each tiny green speck in the virtual forest fire.

The difference is notable in disease outbreaks."

redux [04.17.03]
find related articles. powered by google. Stanford Medical Informatics Preprint Archive A Modular Framework for Automated Space-Time Surveillance Analysis of Public Health Data

"Public health surveillance is changing in response to concerns about bioterrorism, which have increased the pressure for early detection of epidemics. Rapid detection necessitates following multiple non-specific indicators, accounting for spatial structure, and quickly characterizing aberrancies. A single analytic method cannot meet these requirements, but there is no existing framework for the interoperation of surveillance methods. In this paper, we present such a framework and report on a preliminary implementation. Our framework consists of a decomposition of the surveillance analysis task into sub-tasks, and an ontology of surveillance analysis methods, which automate the sub-tasks. As an initial implementation, we use methods developed according to this framework to analyze 911 dispatch data from San Francisco."

redux [06.29.01]
find related articles. powered by google. EurekAlert GIS, bioinformatics collaborations offer promising new perspectives

"The merits of linking two fields seemingly as disparate as geographic information systems (GIS) and bioinformatics might not seem obvious, but Virginia Tech's recent symposium linking the twoaeand its roster of renowned participants from both fieldsaehas raised expectations "Applications of GIS to Bioinformatics" was the first major public forum to cross-pollinate the disciplines, helping to fortify a relatively new, yet highly promising investigative area."

""As a result of new dialog between the fields, as we've had at this conference, we are gaining an important mechanistic link between individual-level processes tracked by genomics and proteomics and population-level outcomes tracked by GIS and epidemiology. This will allow us to do a far better job of monitoring, quantifying, and predicting human-health consequences associated with the environment. The potential payoff in related fields such as those looking at climate change, emerging and resurgent infectious diseases, and environmental health is enormous.""

find related articles. powered by google. Applications of GIS to Bioinformatics Symposium Proceedings

"The meeting brings together researchers in two of the most dynamic analytical technologies-GIS and bioinformatics. The value of GIS analytical systems and data structures to bioinformatics are only now being recognized. Similarly, the methodologies used in bioinformatics can inform GIS scholars of new approaches to pattern recognition and analysis. The purpose of the symposium is to explore the potentials for using GIS as an analytical methodology in bioinformatics and to understand the opportunities bioinformatics presents to the GIS research community. The symposium, the first to focus on the interface between these two research areas, will afford scholars the opportunity to establish new research directions in both fields of investigation."

[ rhetoric ]

Bioinformatics will be at the core of biology in the 21st century. In fields ranging from structural biology to genomics to biomedical imaging, ready access to data and analytical tools are fundamentally changing the way investigators in the life sciences conduct research and approach problems. Complex, computationally intensive biological problems are now being addressed and promise to significantly advance our understanding of biology and medicine. No biological discipline will be unaffected by these technological breakthroughs.


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