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Related Experiment Video

Updated: Jun 7, 2026

Modified Most Probable Number Assay to Quantify Salmonella in Raw and Ready-to-Cook Chicken Products

Modified Most Probable Number Assay to Quantify Salmonella in Raw and Ready-to-Cook Chicken Products

Published on: January 31, 2025

Dose-response modeling of Salmonella using outbreak data.

Peter F M Teunis¹, Fumiko Kasuga, Aamir Fazil

¹National Institute of Public Health and the Environment, Antonie van Leeuwenhoeklaan 9, Bilthoven, The Netherlands.

International Journal of Food Microbiology

|November 2, 2010

Summary

This summary is machine-generated.

Salmonella outbreaks are linked to higher doses, increasing illness risk. This study developed dose-response models using outbreak data to predict infection and illness rates, crucial for public health risk assessment.

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Chronic Salmonella Infected Mouse Model

Chronic Salmonella Infected Mouse Model

Published on: May 31, 2010

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Last Updated: Jun 7, 2026

Modified Most Probable Number Assay to Quantify Salmonella in Raw and Ready-to-Cook Chicken Products

Modified Most Probable Number Assay to Quantify Salmonella in Raw and Ready-to-Cook Chicken Products

Published on: January 31, 2025

Chronic Salmonella Infected Mouse Model

Chronic Salmonella Infected Mouse Model

Published on: May 31, 2010

Area of Science:

Microbiology
Epidemiology
Quantitative Risk Assessment

Background:

Salmonella is a major global foodborne pathogen causing widespread illness.
Understanding Salmonella's dose-response relationship is key to assessing infection and illness risks.
Traditional human challenge studies have limitations; outbreak data offers an alternative epidemiological source.

Purpose of the Study:

To develop and apply dose-response models for Salmonella infection and illness using outbreak data.
To quantify the relationship between ingested Salmonella dose and the probability of infection and acute enteric illness.
To provide a tool for quantitative microbiological risk assessment in public health.

Main Methods:

Collected and analyzed data from published Salmonella outbreaks, including ingested dose and attack rates.
Fitted separate multi-level statistical dose-response models for infection and illness (given infection).
Incorporated serotype and susceptibility as covariates, adjusting for exposure heterogeneity.

Main Results:

Both infection risk and illness risk (given infection) increase with ingested Salmonella dose.
The overall dose-response model estimated an infection ID50 of 7 CFU and an illness ID50 of 36 CFU.
Outbreak data revealed higher infectivity and pathogenicity compared to studies with lab-adapted strains.

Conclusions:

Dose-response models derived from outbreak data are valuable for understanding Salmonella epidemiology and risk.
These models can predict human infection and illness rates, aiding in quantitative microbiological risk assessment.
An accessible Excel tool implementing the model is available for practical application.