IBM has had a breakthrough in their research and managed to find a way to use technology to identify likely sources of contamination during a foodborne illness outbreak. Using past computed retail data combined with public health data, a search can be run on the system to look at billions of food items sold in supermarkets to automatically identify the probability of certain suspect products for outbreaks in a given area. Every time there is a report of an outbreak, using statistical techniques and visualization, the algorithm recalculates the probability of each food item that could be causing the illness.
This system is capable accelerating the time that is usually taken to identify contaminated food products which will in turn help reduce the economic losses experienced by food companies as well as healthcare expenses. Dr. Bernd Appel, head of the Department for Biological Safety for the German Federal Institute for Risk Assesment recently stated in a press statement, “The success of an outbreak investigation often depends on the willingness of private sector stakeholders to collaborate pro-actively with public health officials…this research illustrates an approach to create significant improvements without the need for any regulatory changes.”
This program will relieve some of the strain on the public health system in detecting contaminated food to minimize the spread of the foodborne illness. Jaes Kaufman, Manager of Public Health Research for IBM Research states, “Predictive analytics based on location, content, and context are driving our ability to quickly discover hidden patterns and relationships from diverse public health and retail data..we are working with our public health clients and with retailers in the U.S. to scale this research prototype and begin focusing on the 1.7B supermarket items sold each week in the United States.”
To read more about IBM’s research and how this new system works, click here