IBM research can predict food-borne disease outbreaks
IBM has announced a new system designed to help food retailers, distributors and public health officials predict contaminated food sources and accelerate the investigation of food-borne disease outbreaks.
Using novel algorithms, visualisation, and statistical techniques, the tool can use information on the date and location of billions of supermarket food items sold each week to identify, quickly and with high probability, a set of potentially contaminated products with as few as 10 outbreak case reports.
The research was published recently 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 system uses a combination of two data sources; publicly available health data, and retail data derived from the sale of food. Perhaps surprisingly, retail sales data have never before been used to accelerate the identification of contaminated food. In fact, this data 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.
The system integrates pre-computed retail data with geocoded 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.
Potential risk are automatically identified, contextualised and displayed from multiple sources to help reduce the time to identify the mostly likely contaminated sources by a factor of days or weeks.
James Kaufman, Manager of Public Health Research for IBM Research said: ‘Predictive analytics based on location, content, and context is 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 US to scale this research prototype and begin focusing on the 1.7 billion supermarket items sold each week in the United States.’
To demonstrate the system’s effectiveness, IBM scientists worked with the Department of Biological Safety of the German Federal Institute for Risk Assessment. In this demonstration, the scientists simulated 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. 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 organisations,’ said Dr Bernd Appel, Head of the Department Biological Safety, BfR
Increasingly interconnected supply chains food-related crisis situations have the potential to affect thousands of people, leading to significant healthcare costs, loss of revenue for food companies, and —in the worst cases— death. In the United States alone, one in six people are affected by food-borne diseases each year, resulting in 128,000 hospitalisations, 3,000 deaths, and a nearly $80bn economic burden.