The United States Department of Agriculture, Food Safety and Inspection Service (USDA/FSIS), in collaboration with Virginia Tech, developed an updated user friendly version of an initial risk assessment model (the “Risk Assessment for Listeria monocytogenes in Deli Meat (May 2003).  The updated model – the "In-Plant Deli Meat Model" – is an open source model (see below Downloads & Resources) that evaluates the effectiveness of processing interventions and testing in reducing the risk of listeriosis associated with ready-to-eat meat and poultry products (e.g., deli meats). This risk assessment provided the scientific basis for FSIS' Listeria policies that resulted in industry-wide adoption of more stringent processing controls and a greater focus on identifying and eliminating in-plant environmental sources of Listeria monocytogenes (Lm) that could cross-contaminate ready-to-eat meat and poultry products. These risk-based policies have been attributed to a substantive decline of Lm in ready-to-eat meat and poultry products observed through FSIS's testing programs (Cartwright, 2013).

FSIS makes this fully annotated version of the In-Plant Deli Meat Model available to support reproducibility of results and training in the use of these types of QMRAs (Haas, 2016). The In-Plant Deli Meat model is a dynamic probabilistic model that predicts Lm concentrations at different stages in the food distribution chain, starting from cross-contamination of ready-to-eat meat and poultry products after the cooking step during processing, through retail, to the point of consumption. The 2004 FAO/WHO Lm dose-response model is used to predict the risk of listeriosis among a healthy population and those who are more susceptible (e.g., elderly, immunocompromised, or pregnant individuals).

The In-Plant Deli Meat Model allows users to evaluate the effectiveness of using growth inhibitors, post-lethality interventions, combinations of these interventions, product testing and diversion, and food contact surface testing and sanitation. The model was developed in the statistical programming language R (R Core Team, 2011). Additional resources to support user operation of the model include an overview of the model, a video tutorial on how to use this model, a data dictionary, and input files and output results for comparison.