Vaccination or culling best to prevent foot-and-mouth disease, computer models suggest
Thursday, 02.11.2010, 01:38pm (GMT+3)
Combining technology and
animal health, a group of Kansas State University researchers is
developing a more effective way to predict the spread of foot-and-mouth
disease and the impact of preventative measures. The researchers are
finding that if a foot-and-mouth disease outbreak is not in the epidemic
stage, preemptive vaccination is a minimally expensive way to halt the
disease's spread across a network of animals. But if there's a high
probability of infection, computer models show that culling strategies
are better. "We are trying to do predictive as well as preventative
modeling using a network-based approach," said Sohini Roy Chowdhury, a
master's student in electrical engineering. "First we track how the
infection is spreading in space and time. Then we try to mitigate that
with certain strategies. The novel contribution of this project is that
we considered networks in countries like Turkey, Iran and Thailand that
don't have a highly built database."
Roy Chowdhury is working with Caterina Scoglio, associate professor
of electrical and computer engineering, and William Hsu, associate
professor of computing and information sciences. They presented the work
in December 2009 at the Second International Conference on Infectious
Diseases Dynamics in Athens, Greece. The researchers used mathematical
equations to predict how foot-and-mouth disease spreads over a network
of infected herds. In the network, the nodes are places like stockyards
and grazing lands where animals are held. They are connected in various
ways, such as by animals' grazing movements and by how people and
vehicles move among the herds. Hsu said the researchers' goal is to
increase the accuracy of models that predict disease spread in these
networks over space and time. In the experiments, the researchers ran up
to a week of predictive modeling on a real network and saw how well it
matched data from the actual episode. Roy Chowdhury said they also used
artificial intelligence-based modules to cross compare the model's
accuracy.
The researchers also tested such mitigation strategies as
vaccination, culling and isolation to see how they affected the network.
In real-world outbreaks of foot-and-mouth disease, culling often is
presumed to be the best strategy, but Scoglio said their research could
shed more light on the effectiveness of this practice. "It is the hope
to properly contain a disease like foot-and-mouth disease that is so
infectious while minimizing the economic losses," Scoglio said. 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.
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