UMD Researchers Receive USDA Funding to Use Big Data Analytics and Machine Learning to Integrate Microbial Genomics with Food Safety Risk Assessment
Summary
With this new funding, UMD is paving the way to a more robust food safety risk assessment model that combines computational techniques, genomic and microbial data, and machine learning to improve the management of foodborne illness and better protect public health. QMRA models incorporate uncertainties and variabilities in different steps of the farm-to-fork pathway in order to obtain an accurate indicator of the risk posed by the foodborne pathogen. “The sheer abundance of information by including molecular and genomic data available should increase the robustness of disease risk estimates by reducing the sources of uncertainty and variability in the QMRA model. Salmonella enterica specifically is a subspecies of this pathogen that causes about 1.2 million cases of foodborne illness each year and consists of over 2,500 serovars with highly variable characteristics. “The idea is to connect that genetic information with the characteristics of the pathogen to bridge the gap between the genes and the food safety aspects for consumers,” says Pradhan.