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Detection of pathogens in food processing facilities by routine environmental monitoring (EM) is essential to reduce the risk of foodborne illness but is complicated by the complexity of equipment and environment surfaces. To optimize design of EM programs, we developed  EnABLe  (“ En vironmental monitoring with an  A gent- B ased Model of   L   ist   e   ria ”), a detailed and customizable agent-based simulation of a built environment.  EnABLe  is presented here in a model system, tracing  Listeria  spp. (LS) (an indicator for conditions that allow the presence of the foodborne pathogen  Listeria monocytogenes ) on equipment and environment surfaces in a cold-smoked salmon facility.  EnABLe  was parameterized by existing literature and expert elicitation and validated with historical data. Simulations revealed different contamination dynamics and risks among equipment surfaces in terms of the presence, level and persistence of LS. Grouping of surfaces by their LS contamination dynamics identified connectivity and sanitary design as predictors of contamination, indicating that these features should be considered in the design of EM programs to detect LS. The  EnABLe  modeling approach is particularly timely for the frozen food industry, seeking science-based recommendations for EM, and may also be relevant to other complex environments where pathogen contamination presents risks for direct or indirect human exposure.

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