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Related Experiment Video

Updated: May 19, 2026

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles

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The Bayesian microbial subtyping attribution model: robustness to prior information and a proposition.

J M David¹, D Guillemot, N Bemrah

¹Anses, BP 90203 Fougères, F-35302, France.

Risk Analysis : an Official Publication of the Society for Risk Analysis

|August 14, 2012

Summary

This study refines the Hald Bayesian attribution model for foodborne illnesses. Optimizing parameterization enhances model robustness and accuracy for public health policy.

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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

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Published on: December 7, 2021

Area of Science:

Food safety
Epidemiology
Statistical modeling

Background:

Attributing foodborne illnesses to sources is crucial for public health policy.
The Hald Bayesian microbial subtyping attribution model is advanced but faces overparameterization challenges.
Previous methods addressed overparameterization by setting parameters to constant values.

Purpose of the Study:

To investigate the impact of parameterization choices on the Hald model's robustness.
To propose and evaluate an alternative parameterization for the Hald model.
To improve the accuracy and reliability of foodborne illness source attribution.

Main Methods:

Analysis of parameterization choices in the Hald model.
Development and application of an alternative parameterization using data-based values.
Comparison with Mullner's modified Hald model and a simple deterministic model.
Illustration using the 2005 French non-typhi Salmonella dataset.

Main Results:

Setting bacterial type parameters specific to unique sources and using data-based values improved model convergence and adequacy.
Attribution estimates became consistent with results from other models.
Type and source parameter estimates were coherent with Mullner's model.
The Hald model demonstrated high sensitivity to parameterization.

Conclusions:

The proposed parameterization enhances the robustness and reliability of the Hald attribution model.
This improved approach enables successful application of the model with real-world data.
Optimized parameterization is key for accurate foodborne illness source attribution and effective public health interventions.