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IBM Researchers ­se Grocery Scanner Data to Speed Investigations During Early Foodborne Illness Outbreaks

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Scanning groceries at checkout.

Researchers at IBM have found retail-scanner data from grocery stores can be used to speed investigations into foodborne illnesses.


IBM researchers have found that analyzing retail-scanner data from grocery stores against maps of confirmed cases of foodborne illness can speed early investigations.

The researchers created a data analytics methodology to review spatio-temporal data, including geographic location and possible time of consumption, for hundreds of grocery product categories. They also analyzed each product for its shelf life, geographic location of consumption, and likelihood of harboring a particular pathogen. The researchers used this data to map the information to the known location of illness outbreaks.

The system also ranked all grocery products by likelihood of contamination in a list from which public health officials could test the top 12 suspected foods for contamination and alert the public accordingly.

"Our study shows that big data and analytics can profoundly reduce investigation time and human error and have a huge impact on public health," says IBM researcher Kun Hun. He notes this method already has been applied to an E. coli outbreak in Norway. With just 17 confirmed cases of infection, public health officials were able to use this method to analyze grocery-scanner data related to more than 2,600 possible food products and create a short-list of 10 possible contaminants.

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Abstracts Copyright © 2016 Information Inc., Bethesda, Maryland, USA


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