A multi-agent quantitative microbial risk assessment of raw milk cheeses: from farm-level practices to consumer risk.

Authors

Laurent Guillier, Estelle Chaix, Frédéric Auvray, Lucie Collineau, Noémie Desriac, Narjes Mtimet, Eric Oswald, Florence Dubois-Brissonnet, Jeanne-Marie Membré

Published in

Journal of food protection. Pages 100913. Sep 05, 2026. Epub Sep 05, 2026.

Abstract

Ensuring the microbial safety of raw milk cheeses demands a robust, quantitative understanding of contamination pathways and risk mitigation strategies. In this study, we developed an innovative agent-based Quantitative Microbial Risk Assessment (QMRA) model that explicitly represents farms, animals, cheese batches, and consumers as individual agents. This approach enables a realistic simulation of microbial transmission throughout the entire production chain and facilitates the assessment of the effectiveness of control measures. Grounded in extensive knowledge of the French raw milk cheese sector, the model simultaneously addresses three major pathogens of concern: Shiga toxin-producing Escherichia coli (STEC), Salmonella, and Listeria monocytogenes. By incorporating stochastic Monte Carlo simulations, the model captures variability in microbial shedding at the farm level, contamination dynamics during milk collection and cheese processing, as well as exposure scenarios at the consumer level. This multi-agent framework provides a structured approach that enhances the interpretation of risk scenarios and facilitates risk communication, particularly for risk managers aiming to implement evidence-based food safety policies or consumer guidelines. By explicitly modeling each component of the system, this methodology bridges the gap between theoretical risk assessment and practical decision-making. The approach presented in this article serves as a flexible and transparent tool for assessing microbial risks associated with STEC, Salmonella, and L. monocytogenes in bovine raw milk cheeses, and can be adapted to other foodborne hazards and production systems.

PMID:
42700798
Bibliographic data and abstract were imported from PubMed on 06 Sep 2026.

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