The supply chain of cilantro was modeled for growth and die-off of Escherichia coli (E. coli) O157:H7 from infield and harvesting, transportation and storage and ultimately consumption at home. Using Visual Basic for Applications (VBA) macros and @RISK software, a simulation model was developed for exposure and estimation of illnesses. Test scenarios were modeled to determine the relative importance of different factors on the risk of illness. The developed model was simulated using Monte Carlo technique and Latin Hypercube sampling for 100,000 iterations.

Results showed an increase in the mean E. coli O157:H7 concentration along the supply chain for cilantro grown in both winter and summer weather conditions. In the winter, the mean pathogen concentration increased from 5.6×10-5 CFU/g to 24.7 CFU/g from after harvest to after home storage, respectively. In summer conditions, the mean pathogen concentration increased from 3.2×10-4 CFU/g to 5.2×10-2 CFU/g. The inner quartile ranges (IQRs) for the same model conditions showed a decrease in E. coli O157:H7 concentration along the supply chain for cilantro grown in both winter and summer weather conditions. This indicates a majority of situations result in a decrease in E. coli O157:H7 concentration along the supply chain however rare situations can occur where the concentration will increase greatly. With a prevalence of 0.1% E. coli O157:H7 contamination for cilantro post-harvest used for illustration, the model predicted the mean number of illnesses per year due to the consumption of E. coli O157:H7 contaminated cilantro in the United States as 86 and 164 for cilantro grown during winter and summer conditions, respectively.

Sensitivity analysis results indicated that transportation temperatures and quality of irrigation water had the largest impact on the number of illnesses per year. Scenario testing results for different risk factors demonstrated the importance of limiting and reducing cross contamination along the production chain, especially at higher initial prevalence levels and preventing temperature abuse during transportation from farm to retail, when reducing overall risk of illness. The developed risk model can be used to estimate the microbiological risks associated with E. coli O157:H7 in cilantro and determine areas along the supply chain with the most effect on the final concentration per serving for future mitigation strategies.