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Flexible risk process models to quantify residual risks and the impact of interventions


We propose to build an adaptable supply chain risk model with steps universally applicable to most produce, parameterize this model with academic and industry data and expert opinion, and then distribute the model for both research into specific supply chains and as an adaptable stakeholder tool. Specifically, we will use previously published language to describe a generally applicable produce supply chain. We will then mathematically describe unit operations that can affect the safety of the produce, incorporating analyses to determine the marginal food safety gains of different operations. In the first year, we will build a supply chain risk model (SCRM) for Shiga-toxin producing Escherichia coli (STEC) and Listeria monocytogenes (LM) simultaneously in leafy greens. Major outputs will be a comprehensive model of residual risk for STEC and LM from current practices from preharvest to consumer, and sensitivity analysis of factors influencing risk to guide focus of future work. To advance to year 2, CPS stakeholders suggest “what-if?” scenarios for modelling additional commodities, hazards, and practices. In the second year, we will improve the STEC and LM model with feedback from CPS stakeholders and adapt the SCRM to accommodate additional pathogens and commodities. A major output will be the ranked risk posed by what-if scenarios, beyond the initial STEC and LM in leafy greens scope. To advance to year 3, CPS stakeholders suggest factors to focus on to reduce uncertainty in revised models and generate lists of candidate under-determined factors to assess with future research. In the third year, we will define an adaptable SCRM system for assessing the importance of previously unmodeled candidate risk factors. A major output will be ranked risks from both explicitly modeled factors and the aggregate risk posted by other factors not explicitly modeled at each step. At this point, stakeholders will have a system to bound the influence of newly identified risk factors, such as a newly uncovered harborage site. One could assess that risk using a what-if scenario. If important model outputs, such as residual risk of illness, were highly sensitive to this change, that increased risk is important in the context of the other known risks. Also critical, if model outputs show low sensitivity, the current supply chain likely has sufficient downstream risk reduction strategies to manage the increased risk, or this specific risk is minor.

Matthew Stasiewicz, Ph.D.; Martin Wiedmann, Ph.D.
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