Computer modeling shows strategies to rein in epidemics need to be retooled for rural p...( An infectious disease striking a lar...),Health

An infectious disease striking a large city may seem like a disastrous scenario -- millions of people sharing apartment buildings, crammed on buses and trains and brushing past one another on crowded sidewalks.

A group of Kansas State University engineers is finding that a truly disastrous epidemic scenario could also take place in the wide-open spaces of the Great Plains. Caterina Scoglio, associate professor of electrical and computer engineering, is leading an Epicenter research project called SGER: Exploratory research on complex network approach to epidemic spreading in rural regions.

Scoglio is working with Todd Easton, associate professor of industrial and manufacturing systems engineering; Robert Kooij, adjunct associate professor of electrical and computer engineering; and Walter Schumm, professor of family studies and human services.

With funding from the National Science Foundation, they are using academic models to study the spread of diseases in rural areas. Their goal is to identify optimal strategies to forecast and control disease outbreaks in rural areas.

"What are used as mitigation strategies in cities will not be so effective in rural areas," Scoglio said. "In cities, people have a lot of informal contact with one another but looser ties."

This means that during a disease outbreak, urban residents are less likely to interact with sick neighbors and therefore less likely to contract or spread a disease. On the other hand, Schumm found that 35 percent of rural residents would be willing to visit other people in the community during a major epidemic, citing the results of a rural survey recently conducted as part of the project.

"In a rural setting, you're maybe more likely to watch out for all of your neighbors, and your neighbors may also be your uncles, aunts and other family members," Easton said.

Easton's research group ran a computer simulation on a hypothetical disease outb
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