EnABLe: An agent-based model to understand Listeria dynamics in food processing facilities
Authored by Claire Zoellner, Rachel Jennings, Martin Wiedmann, Renata Ivanek
Date Published: 2019
DOI: 10.1038/s41598-018-36654-z
Sponsors:
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Platforms:
NetLogo
Model Documentation:
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Abstract
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 ({''}Environmental monitoring with an Agent-Based Model of
Listeria{''}), 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.
Tags
contamination
systems
growth
Urban
Variability
Survival
Environmental monitoring programs
Monocytogenes
Meat