IBM has created a tool that uses sales data to track down contaminated food sources that cause outbreaks of food poisoning.
Using novel algorithms, visualisation, and statistical techniques, the tool can use information on the date and location of the 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.
The tool could help food retailers, distributors and public health officials identify the source of outbreaks far quicker than current methods, which can take anywhere from days to weeks – vital to minimise the spread of illness, healthcare costs and loss of revenue for food companies.
In the USA alone, IBM says one in six people are affected by food-borne diseases each year, resulting in 128,000 hospitalizations, 3,000 deaths, and a nearly $80bn economic burden.
“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,” said James Kaufman, Manager of Public Health Research for IBM Research.
“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 1.7 billion supermarket items sold each week in the United States.”
The system is described in research published today in the peer-reviewed journal PLOS Computational Biology together with collaborators from Johns Hopkins University, Purdue University and the German Federal Institute for Risk Assessment (BfR).
The data used by the system already exists as part of the inventory systems used by retailers and distributors today, which manage up to 30,000 food items at any given time with nearly 3,000 of them being perishable.
IBM scientists built a system that automatically identifies, contextualises and displays data from multiple sources to help reduce the time to identify the mostly likely contaminated sources by a factor of days or weeks.
It integrates pre-computed retail data with geo-coded public health data to allow investigators to see the distribution of suspect foods and, selecting an area of the map, view public health case reports and lab reports from clinical encounters.
The algorithm effectively learns from every new report and re-calculates the probability of each food that might be causing the illness.
To demonstrate the system’s effectiveness, IBM scientists worked with the Department of Biological Safety of the BfR, simulating 60,000 outbreaks of food-borne disease across 600 products using real-world food sales data from Germany.
“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,” said Dr Bernd Appel, head of the Department Biological Safety at BfR.
“This can be achieved by combining innovative software technology with already existing data and the willingness to share this information in crisis situations between private and public sector organizations.”
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