March 23, 2019

Scientists Use Machine Learning to Find Source of Salmonella

Scientists at the University of Georgia Center for Food Safety has developed a new approach to identify the animal source of some types of Salmonella outbreaks. The researchers have developed a machine learning approach.

Machine Learning

The study is published in the January 2019 issue of Emerging Infectious Diseases. Researchers Xiangyu Deng and Shaokang Zhang, along with a team of colleagues, used more than a thousand genomes to predict the animal sources of Salmonella Typhimurium. The project used experts from the Centers for Disease Control and Prevention, the Food & Drug Administration, the Minnesota Department of Health, and the Translational Genomics Research Institute.

Almost 3,000 outbreaks of food poisoning were reported in the U.S. from 2009 to 2015. Ninety percent of those outbreaks were caused by different strains of Salmonella bacteria.

The scientists trained the machine, an algorithm they named Random Forest, with more than 1,300 Salmonella Typhimurium genomes with known sources. The machine then “learned” how to predict the animal sources of different genomes.

Deng said in a statement, ‘We used this big collection of Typhimurium genomes as the training set to build the classifier.┬áThe classifier predicts the source of the Typhimurium isolate by interrogating thousands of genetic features of its genome.”

The system predicted the animal source of the bacteria with 83% accuracy. It works best with Salmonella from poultry and swine. The machine also detects whether its prediction is precise or imprecise.

According to Deng, the machine does have some limitations. It can’t predict seafood as a source of Salmonella yet, and has trouble predicting Salmonella strains that can pass among animals.

This program, if developed and used by public health departments, could help trace the outbreak strain of Salmonella bacteria back to the source more quickly than traditional methods such as traceback. Identifying the source of n outbreak helps limit the number of patients and will prevent more illnesses. And this program could give public health officials confidence to implicate the source of the outbreak.

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