Communications of the ACM
Software Predicted Virus Risk in California Epidemic
Its not just tracking the geography of actual cases. DYCAST is based on biological parameters, says Ryan Carney.
Photo courtesy of Brown University
Brown University researchers have developed DYCAST, a computerized epidemiological model that was able to predict the spread of the West Nile virus in California in 2005 with 81.6 percent accuracy. It also defined high-risk areas where the infection rate turned out to be 39 times higher than in low-risk areas.
"One of the things that really differentiates DYCAST from other approaches is that it's based on biological parameters," says Brown graduate student Ryan Carney.
Since the system used biology to define the geographic and temporal attributes of the model instead of county or census tract borders, the DYCAST system enabled the California Department of Public Health to provide early warnings to a wide section of the state.
In 2007, Carney adapted the DYCAST model to an open source platform to track dengue fever in Brazil. With the specific parameters of that disease, DYCAST was able to predict its spread in the city of Riberao Preto in Brazil. Carney says the software can be adapted for use as an early warning system for other infectious diseases or bioterrorism attacks.
From Brown University
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