A team of scientists led by researchers at the University of Georgia Center for Food Safety in Griffin has developed a machine-learning approach that could lead to quicker identification of the animal source of certain Salmonella outbreaks. In the research, published in the January 2019 issue of Emerging Infectious Diseases, Xiangyu Deng and his colleagues used more than a thousand genomes to predict the animal sources, especially livestock, of Salmonella Typhimurium. Deng, an assistant professor of food microbiology at the center, and Shaokang Zhang, a postdoctoral associate with the center, led the project, which also included experts from the Centers for Disease Control and Prevention, the U.S. Food and Drug Administration, the Minnesota Department of Health and the Translational Genomics Research Institute. According to the Foodborne Disease Outbreak Surveillance System, close to 3,000 outbreaks of foodborne illness were reported in the U.S. from 2009 to 2015. Of those, 900 -- or 30 percent -- were caused by different serotypes of Salmonella, including Typhimurium, Deng said.

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. 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.