Hsu said this study also could benefit relief workers sent to help contain foot-and-mouth disease. The K-State network models improve upon existing ones, he said, because they consider such factors as wind, animal grazing and human movements between regions, as well as the number of meat markets in an area.

Scoglio's research group has studied disease outbreaks using computer models of networks before, but this project is different in that it considers a specific disease, she said.

Hsu contributed his research in data mining, which seeks to scour news stories and other online public sources and extract information that could offer clues about disease outbreaks. For this project, Hsu's system crawled and analyzed Web articles from news agencies like the BBC and CNN, as well as such sources as disease control fact sheets from universities.

"Just as Google indexes sites based on authoritativeness and looks for hub sites, we also look to start our crawls of the Web from sites like the World Health Organization and the Centers for Disease Control and Prevention," Hsu said.

At the conference in Athens, Roy Chowdhury also presented a poster on preliminary work the group has done on H1N1 infections. Using temporal models, they generated predictions on when infections would peak and the rate at which they would drop off after that peak. Roy Chowdhury used data from the Centers for Disease Control and Prevention. The group plans to extend this analysis of the H1N1 epidemic using network-based models.

SOURCE Kansas State University