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Adapted QRA Model with Epidemic Curve Enhances Root Cause Analysis

By Food Safety Tech Staff
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Researchers found that the adapted Quantitative Risk Analysis model using Epidemic Curve prediction was able to identify the root cause of a 2019 Salmonella outbreak, providing a new tool for investigators.

A new Quantitative Risk Analysis (QRA) model using epidemic curve (EC) prediction (QRA-EC) could become a valuable new tool in root cause analyses of foodborne illness outbreaks. Researchers with the FDA Center for Food Safety and Applied Nutrition developed a QRA-EC model and evaluated it in a case study of a 2019 multistate Salmonella outbreak linked to melons. The study was published in Risk Analysis.

Amir Mokhtari, et al, modified the traditional QRA model to track illness timing (epidemic curves). They then compared the predicted number of illnesses and timing of illnesses identified by their model—the FDA-Melon QRA-EC—with the 2019 Salmonella melon outbreak epidemic curve.

The authors explain that QRA models are traditionally used to predict the number of illnesses associated with given food-pathogen pairs and to simulate previous foodborne illness outbreaks by comparing model estimates with observed outbreak size. The QRA-EC model extends that framework to include prediction on the timeline of illnesses associated with consumption of contaminated food products.

“This additional feature yields two-dimensional risk predictions that provide investigators with a more nuanced quantitative evaluation of potential/hypothesized illness outbreak root cause, as the shape and/or span of epidemic curves can give clues about potential sources and/or patterns of spread for an outbreak,” the authors wrote.

They found that contamination niches on the equipment were the most likely cause of the 2019 outbreak, as illnesses were linked to one processor of fresh-cut melons, while the contaminated melons (linked back to one supplier) had been sent to several processing facilities.

“Compared to the efficiency of whole melon washing, proper sanitation of contamination niches had a more pronounced impact on the predicted epidemic curves,” the authors wrote. Furthermore, the model and case study found that risk was greater when food was exposed to contamination niches on the assembly line versus in scrubbers and bins, likely due to the larger surface area of the assembly line and the potential for several contamination niches along the line.

“Using an Agent-Based Modeling approach, FDA Melon QRA-EC explicitly tracked the temporal and spatial movement of contaminated melons throughout the supply chain, which allowed us to predict both the total number and timeline of illnesses for various scenarios, which enabled us to identify conditions that can lead to an outbreak of certain magnitude and with a certain span of the epidemic curve,” the authors concluded.

While this particular model was developed specifically for Salmonella outbreaks linked to melons, the authors note that their model can be adapted for other food-pathogen pairings.

 

 

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