LAUSR

The complex environment of a produce packinghouse can facilitate the spread of pathogens such as Listeria monocytogenes in unexpected ways. This can lead to finished product contamination and potential foodborne disease cases. There is a need for simulation-based decision support tools that can test different corrective actions and are able to account for a facility’s interior cross-contamination dynamics. Thus, we developed agent-based models of Listeria contamination dynamics for two produce packinghouse facilities; agents in the models represented equipment surfaces and employees, and models were parameterized using observations, values from published literature and expert opinion. Once validated with historical data from Listeria environmental sampling, each model’s baseline conditions were investigated and used to determine the effectiveness of corrective actions in reducing prevalence of agents contaminated with Listeria and concentration of Listeria on contaminated agents. Evaluated corrective actions included reducing incoming Listeria, modifying cleaning and sanitation strategies, and reducing transmission pathways, and combinations thereof. Analysis of Listeria contamination predictions revealed differences between the facilities despite their functional similarities, highlighting that one-size-fits-all approaches may not always be the most effective means for selection of corrective actions in fresh produce packinghouses. Corrective actions targeting Listeria introduced in the facility on raw materials, implementing risk-based cleaning and sanitation, and modifying equipment connectivity were shown to be most effective in reducing Listeria contamination prevalence. Overall, our results suggest that a well-designed cleaning and sanitation schedule, coupled with good manufacturing practices can be effective in controlling contamination, even if incoming Listeria spp. on raw materials cannot be reduced. The presence of water within specific areas was also shown to influence corrective action performance. Our findings support that agent-based models can serve as effective decision support tools in identifying Listeria-specific vulnerabilities within individual packinghouses and hence may help reduce risks of food contamination and potential human exposure.