Manuscript type: Research Paper Development of a modeling tool to assess and reduce regulatory and recall risks for cold-smoked salmon due to Listeria monocytogenes contamination

While public health risk assessments for Listeria monocytogenes (Lm) have been published for different foods, firm-level decision making on interventions targeting Lm involves considerations of both public health and enterprise risks. Smoked seafood is a ready-to-eat product with a high incidence of Lm contamination and associated with several recalls. We thus used cold-smoked salmon as a model product to develop a decision support tool (the Regulatory and Recall Risk [3R] Model) to estimate (i) baseline regulatory and recall (RR) risks (i.e., overall risks of a lot sampled and tested positive [e.g., by food regulatory agencies]) due to Lm contamination and (ii) the RR risk reduction that can be achieved through interventions with different underlying mechanisms: (i) reducing the prevalence and/or level of Lm and (ii) retarding or preventing Lm growth. Given that a set number of samples (e.g., 10) are tested for a given lot, the RR risk equals the likelihood of detecting Lm in at least one sample. Under the baseline scenario, which assumes a 4% Lm prevalence and no interventions, the median predicted RR risk for a given production lot was 0.333 (95% credible interval: 0.288, 0.384) when 10 25-g samples were collected. Nisin treatments, which reduce both the prevalence and initial level of Lm, reduced RR risks in a concentration-dependent manner to 0.109 (0.074, 0.146; 5 ppm), 0.049 (0.024, 0.083; 10 ppm), and 0.017 (0.007, 0.033; 20 ppm). In general, more effective reduction in RR risks can be achieved by reducing Lm prevalence, compared to retarding Lm growth, as the RR risk was reduced to 0.182 [0.153, 0.213] by a 50% prevalence reduction, while only to 0.313 [0.268, 0.367] by bacteriostatic growth inhibitors. In addition, sensitivity analysis indicates that prevalence and initial level of Lm, as well as storage temperature have the largest impact on predicting RR risks, suggesting that reliable data for these parameters will improve model performance.