Bayesian model for tracing Salmonella contamination in the pig feed chain

Area of Science:

• Veterinary epidemiology
• Food safety
• Risk assessment

Background:

• Salmonella infections in pigs often remain asymptomatic, creating a significant risk for human salmonellosis through pork consumption.
• Contaminated animal feed is a primary transmission route for Salmonella to pigs.

Purpose of the Study:

• To develop and present a Bayesian modeling approach for quantifying the risk of Salmonella contamination in pig feed.
• To estimate the proportion of Salmonella infections in pigs that are attributable to contaminated feed.

Main Methods:

• Utilized a Bayesian model integrating Salmonella surveillance data with information from surveys, reports, registries, statistics, legislation, and literature.
• Probabilistically quantified uncertainties by synthesizing evidence across a structured flow chain of feed ingredients.
• Combined model-based infection probability with Salmonella subtyping data for attribution estimation.

Main Results:

• The study presents a quantitative risk assessment framework for Salmonella in the pig feed chain.
• Estimated the proportion of pig Salmonella infections linked to feed contamination.
• Demonstrated the utility of the model for predicting the impact of changes within the feed chain.

Conclusions:

• The developed Bayesian model provides a robust method for assessing feed-related Salmonella risks in pigs.
• This approach can inform strategies to mitigate Salmonella transmission and enhance food safety.
• The methodology is adaptable for evaluating other animal-pathogen systems and feed chain interventions.