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.

From the beginning of the COVID-19 pandemic, researchers assessed the impact of the disease in terms of loss of life, medical load, economic damage, and other key metrics of resiliency and consequence mitigation; these studies sought to parametrize the critical components of a disease transmission model and the resulting analyses were informative but often lacked critical parameters or a discussion of parameter sensitivities. Using SARS-CoV-2 as a case study, we present a robust modeling framework that considers disease transmissibility from the source through transport and dispersion and infectivity. The framework is designed to work across a range of particle sizes and estimate the generation rate, environmental fate, deposited dose, and infection, allowing for end-to-end analysis that can be transitioned to individual and population health models. In this paper, we perform sensitivity analysis on the model framework to demonstrate how it can be used to advance and prioritize research efforts by highlighting critical parameters for further analyses.