EnABLe: An agent-based model to understand Listeria dynamics in food processing facilities.

Area of Science:

• Food Safety
• Microbiology
• Computational Modeling

Background:

• Routine environmental monitoring (EM) is crucial for detecting pathogens in food processing facilities to prevent foodborne illnesses.
• The complexity of equipment and surfaces in these facilities complicates effective EM program design.
• Listeria spp. (LS) serves as an indicator for conditions conducive to Listeria monocytogenes presence.

Purpose of the Study:

• To develop and present EnABLe (Environmental monitoring with an Agent-Based Model of Listeria), a customizable agent-based simulation.
• To optimize the design of EM programs by understanding Listeria contamination dynamics.
• To identify key factors influencing pathogen presence, level, and persistence on facility surfaces.

Main Methods:

• Developed EnABLe, a detailed agent-based simulation of a built environment for pathogen tracking.
• Parameterized EnABLe using existing literature and expert elicitation.
• Validated the model with historical data from a cold-smoked salmon processing facility.

Main Results:

• Simulations revealed varying contamination dynamics and risks across different equipment surfaces.
• Identified surface connectivity and sanitary design as key predictors of Listeria spp. contamination.
• Demonstrated that grouping surfaces by contamination dynamics aids in risk assessment.

Conclusions:

• EnABLe provides a science-based approach to designing more effective EM programs.
• Considering surface connectivity and sanitary design is essential for optimizing pathogen detection.
• This modeling approach is valuable for the frozen food industry and other complex environments with contamination risks.