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Novel learning system combines petabytes of retail data with
public health case reports to identify contaminated food sources, shorten
intervention times and limit the spread of disease outbreaks
IBM) today announced a first-of-a-kind system that is
designed to help food retailers, distributors and public health officials
predict the most likely contaminated food sources and accelerate the
investigation of food-borne disease outbreaks. Using novel algorithms,
visualization, and statistical techniques, the tool can use information on the
date and location of billions of supermarket food items sold each week to
quickly identify with high probability a set of potentially “guilty” products
with in as few as 10 outbreak case reports.