|
|||||
|
|
Leveraging risk assessment for foodborne outbreak investigations: The Quantitative Risk Assessment-Epidemic Curve Prediction Model |
|
| Authors: | Amir Mokhtari Hao Pang Sofia Santillana Farakos Crystal McKenna Cecilia Crowley Vanessa Cranford April Bowen Sheena Phillips Asma Madad Donald Obenhuber Jane M Van Doren |
| Institution: | 1. Food and Drug Administration, Center for Food Safety and Applied Nutrition, 5001 Campus Drive, College Park, Maryland, 20740 USA
Both authors contributed equally to this work. Current address: Amir Mokhtari Booz Allen Hamilton Bethesda, Maryland, USA. Cecilia Crowley Food and Drug Administration, Center for Biologics Evaluation and Research, Silver Spring, Maryland, USA. Vanessa Cranford 6725 Thornhill Cir, Windermere, Florida, USA.;2. Food and Drug Administration, Center for Food Safety and Applied Nutrition, 5001 Campus Drive, College Park, Maryland, 20740 USA;3. Food and Drug Administration, Center for Food Safety and Applied Nutrition, 5001 Campus Drive, College Park, Maryland, 20740 USA Current address: Amir Mokhtari Booz Allen Hamilton Bethesda, Maryland, USA. Cecilia Crowley Food and Drug Administration, Center for Biologics Evaluation and Research, Silver Spring, Maryland, USA. Vanessa Cranford 6725 Thornhill Cir, Windermere, Florida, USA. |
| Abstract: | Root cause analysis can be used in foodborne illness outbreak investigations to determine the underlying causes of an outbreak and to help identify actions that could be taken to prevent future outbreaks. We developed a new tool, the Quantitative Risk Assessment-Epidemic Curve Prediction Model (QRA-EC), to assist with these goals and applied it to a case study to investigate and illustrate the utility of leveraging quantitative risk assessment to provide unique insights for foodborne illness outbreak root cause analysis. We used a 2019 Salmonella outbreak linked to melons as a case study to demonstrate the utility of this model (Centers for Disease Control and Prevention CDC], 2019). The model was used to evaluate the impact of various root cause hypotheses (representing different contamination sources and food safety system failures in the melon supply chain) on the predicted number and timeline of illnesses. The predicted number of illnesses varied by contamination source and was strongly impacted by the prevalence and level of Salmonella contamination on the surface/inside of whole melons and inside contamination niches on equipment surfaces. The timeline of illnesses was most strongly impacted by equipment sanitation efficacy for contamination niches. Evaluations of a wide range of scenarios representing various potential root causes enabled us to identify which hypotheses, were likely to result in an outbreak of similar size and illness timeline to the 2019 Salmonella melon outbreak. The QRA-EC framework can be adapted to accommodate any food–pathogen pairs to provide insights for foodborne outbreak investigations. |
| Keywords: | foodborne illness outbreak quantitative risk assessment root cause analysis |