Modeling the Risk of Colistin-Resistant Salmonella Minnesota in Cooked Chicken: A Probabilistic Microbial Assessment
Year: 2026
Type: preprint
Abstract: Background Antimicrobial-resistant Salmonella is an emerging food safety concern worldwide. In Saudi Arabia, published epidemiological data on antimicrobial-resistant Salmonella Minnesota associated with poultry products remain limited. Therefore, this study aimed to estimate the potential public health risks associated with antimicrobial-resistant Salmonella Minnesota in cooked chicken using a probabilistic microbial risk assessment framework. Methods A probabilistic microbial risk assessment model was developed using Monte Carlo simulation with 10,000 iterations. The framework integrated food consumption data, microbiological surveillance data, and a WHO-based dose–response model to estimate exposure, probability of illness, and population-level risk characterization. Results The modeled ingested dose of antimicrobial-resistant Salmonella Minnesota showed a median of 1.07 CFU/g. The estimated probability of exposure demonstrated a median value of 1.13 × 10⁻³, while the modeled probability of illness showed a median value of 8.00 × 10⁻⁵. The estimated annual foodborne illness rate showed a median of 0.20 cases per 100,000 population, ranging from 0.07 to 0.54 cases per 100,000 population across the estimated distribution. Indirect comparison using reported national Salmonella incidence rates between 2015 and 2023 showed general agreement between the modeled outputs and the observed epidemiological pattern. Conclusions This study presents a probabilistic framework for estimating the potential public health risks associated with antimicrobial-resistant Salmonella Minnesota in cooked chicken in Saudi Arabia. The proposed model may support preliminary risk-based surveillance and food safety assessment activities in settings where published epidemiological data remain limited.
Source: Research Square
Institutions: Saudi Food and Drug Authority, University of Alberta
Language: English
Subfield: Food Science
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7204846306