Genomic Analyses of Pathogen Subtypes
Description
The purpose of this use case is to use machine learning (ML) methods to group foodborne bacteria based on patterns in their genes, then connect this information with available health data to evaluate foodborne illness risk to public health.
Detailed example
The model outputs predictions of foodborne illnesses with high public health risk, key genetic markers by importance, and emerging trends.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
b) Pilot – The use case has been deployed in a limited test or pilot capacity.
Expected benefit
Expected benefits include improving our understanding of foodborne illness, identifying key genes and trends, and evaluating those that are important for public health.
Controls / human review
ATO: No; PIA: Not published
Data needed
The data is composed of agency-owned data, including whole genome sequencing (WGS) data from Food Safety and Inspection Service (FSIS).