As more dead birds were reported in close proximity, the software would generate daily maps of areas at high risk for human infection, providing an early warning to local public health officials. The software, for example, predicted areas as high-risk more than a month before the first human cases arose, on average.
In Sacramento County, location of the largest West Nile virus epidemic in the United States that year, DYCAST helped mosquito control officials target their testing and spraying resources actions that ultimately reduced human illness, Carney said.
After 2005, the department implemented the model throughout the state, although the number of human cases and reported dead birds, along with the model's prediction rates, dropped sharply.
In 2007 Carney enrolled as a master's student at Yale and adapted the DYCAST model to track dengue fever in Brazil, using a version of the software that his CUNY collaborators had converted to an open-source platform. With the specific parameters of that disease, DYCAST was able to predict its spread in the city of Ribero Preto in Brazil, Carney said, citing unpublished data.
Carney has continued his analysis and development of DYCAST and dengue at Brown, where he is a doctoral student of ecology and evolutionary biology. He said the software at its core has potential to be adapted as an early warning system for other infectious diseases or even bioterrorism attacks.
|Contact: David Orenstein|