Foodborne Illness Source Attribution
Description
The Interagency Food Safety Analytics Collaboration (IFSAC) - a partnership between the Centers for Disease Control and Prevention (CDC), the U.S. Food and Drug Administration (FDA), and the Food Safety and Inspection Service (FSIS) - has used computer-based methods to predict the likely sources of foodborne illnesses in humans.
Detailed example
The model outputs predictions of likely sources of foodborne human illness cases, along with a confidence score of how probable it is that the illness came from the predicted source.
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 what foods contribute the most to illnesses, which can help in creating measures and policies to reduce illnesses and improve food safety.
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).