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Related Experiment Video

Updated: Jun 3, 2026

Cefoperazone-treated Mouse Model of Clinically-relevant Clostridium difficile Strain R20291

Cefoperazone-treated Mouse Model of Clinically-relevant Clostridium difficile Strain R20291

Published on: December 10, 2016

Development and Application of a Novel Dose-Response Model for the Quantification of Clostridioides difficile

Elizabeth N Paddy¹, M Sohail¹, Oluwasola O D Afolabi¹

¹School of Architecture, Building and Civil Engineering, Loughborough University, Loughborough, Leicestershire, UK.

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

|June 2, 2026

Summary

A new mouse model provides a dose-response relationship for Clostridioides difficile infection (CDI). This advances quantitative microbial risk assessment (QMRA) for managing CDI risks from clinical surfaces.

Keywords:

Clostridioides difficile dose–response model infection quantitative microbial risk assessment risk

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Last Updated: Jun 3, 2026

Cefoperazone-treated Mouse Model of Clinically-relevant Clostridium difficile Strain R20291

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Published on: December 10, 2016

A Protocol to Characterize the Morphological Changes of Clostridium difficile in Response to Antibiotic Treatment

A Protocol to Characterize the Morphological Changes of Clostridium difficile in Response to Antibiotic Treatment

Published on: May 25, 2017

Area of Science:

Microbiology
Risk Assessment
Infectious Diseases

Background:

Effective management of Clostridioides difficile infection (CDI) is hindered by the absence of a human dose-response model.
Existing challenges in risk assessment stem from insufficient human data for C. difficile.

Purpose of the Study:

To develop a novel murine-derived dose-response model for C. difficile that mirrors human CDI.
To apply this model within a quantitative microbial risk assessment (QMRA) framework to evaluate CDI risk from clinical surfaces.

Main Methods:

Evaluated animal dose-response datasets for C. difficile, selecting a mouse model of C. difficile-associated colitis.
Utilized maximum likelihood estimation in R to fit data to beta-Poisson and exponential models.
Assessed model fit using Akaike information criterion, Bayesian information criterion, likelihood tests, and sensitivity analysis.

Main Results:

The beta-Poisson model demonstrated the best fit (α = 0.56, N50 = 2871.56).
Estimated mean doses for 10% and 50% infection rates were 505 and 3994 CFU, respectively.
QMRA application indicated potential annual CDI risks: 33 cases per 100,000 healthcare workers from bed rails, 12 from keyboards, and <1 from door handles.

Conclusions:

The study successfully established a C. difficile dose-response relationship using a murine model.
This provides a foundation for future QMRA research on C. difficile.
Supports the development of context-specific risk assessments for CDI in healthcare settings.