Dose response modelling of Escherichia coli O157 incorporating data from foodborne and environmental outbreaks

Norval James Colin Strachan, M. P. Doyle, F. Kasuga, Ovidiu Rotariu, Iain D Ogden

Research output: Contribution to journalArticlepeer-review

124 Citations (Scopus)

Abstract

A human dose response model for Escherichia coli O157 would enable prediction of risk of infection to humans following exposure from either foodborne or environmental pathways. However, due to the severe nature of the disease, volunteer human dose response studies cannot be carried out. Surrogate models from Shigella fed to humans and E. coli O157 to rabbits have been utilised but are significantly different to one another. In addition data obtained by animal exposure may not be representative for human beings. An alternative approach to generating and validating a dose response model is to use quantitative data obtained from actual human outbreaks. This work collates outbreak data obtained from global sources and these are fitted using exponential and beta-Poisson models. The best fitting model was found to be the beta-Poisson model using a beta-binomial likelihood and the authors favour the exact version of this model. The confidence levels in this model encompass a previously published Shigella dose response model. The potential incorporation of this model into QMRAs is discussed together with applications of the model to help explain foodborne outbreaks. (c) 2005 Elsevier B.V. All rights reserved.

Original languageEnglish
Pages (from-to)35-47
Number of pages12
JournalInternational Journal of Food Microbiology
Volume103
Issue number1
DOIs
Publication statusPublished - Aug 2005

Keywords

  • dose response
  • E. coli O157
  • food outbreaks
  • environmental outbreaks
  • risk assessment
  • Markov Chain Monte Carlo
  • metropolis algorithm
  • epidemiology
  • QUANTITATIVE RISK-ASSESSMENT
  • HEMOLYTIC-UREMIC-SYNDROME
  • INFECTION
  • CATTLE
  • SHIGELLOSIS
  • PREVALENCE
  • HAMBURGERS
  • SLAUGHTER
  • DIARRHEA
  • FECES

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