An Agent-Based Model for Pathogen Persistence and Cross-Contamination Dynamics in a Food Facility

Area of Science:

• Food Safety Science
• Microbial Ecology
• Computational Modeling

Background:

• Microbial contamination in food facilities poses significant public health risks.
• Understanding pathogen dynamics is crucial for effective risk management.

Purpose of the Study:

• To develop and utilize an agent-based modeling (ABM) framework to simulate microbial persistence and spread in food facilities.
• To aid risk managers in identifying effective mitigation strategies for reducing contamination.

Main Methods:

• Developed a mathematical model within an agent-based modeling (ABM) framework.
• Simulated a spatially explicit dynamic system considering food handler hygiene, activities, and facility interactions.
• Created a hypothetical case study to compare risk mitigation strategies for Listeria monocytogenes.

Main Results:

• Identified non-food contact areas (e.g., restrooms) as potential contamination niches that can recontaminate food contact areas.
• Demonstrated that food handler behaviors significantly impact microbial persistence and spread to food products.
• Highlighted the utility of ABM for predicting emergent system-level responses in complex food safety scenarios.

Conclusions:

• Agent-based modeling provides a valuable bottom-up approach to assess food safety management practices.
• The developed model can be adapted for various pathogens, food types, and activities to evaluate mitigation strategies.
• This framework aids in quantifying contamination reductions and optimizing food safety interventions.