Risk assessment for Cryptosporidium: a hierarchical Bayesian analysis of human dose response data.

@article{Messner2001RiskAF,
  title={Risk assessment for Cryptosporidium: a hierarchical Bayesian analysis of human dose response data.},
  author={M. Messner and C. Chappell and P. Okhuysen},
  journal={Water research},
  year={2001},
  volume={35 16},
  pages={
          3934-40
        }
}
Three dose-response studies were conducted with healthy volunteers using different Cryptosporidium parvum isolates (IOWA, TAMU, and UCP). The study data were previously analyzed for median infectious dose (ID50) using a simple cumulative percent endpoint method (Reed and Muench, 1938). ID50s were derived using two definitions of infection: one as subjects having oocysts detected in stool by direct fluorescence assay, and the other by a clinical finding of diarrhea with or without detected… Expand
Predictive Population Dose-Response Assessment for Cryptosporidium parvum: Infection Endpoint
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A hierarchical predictive population dose-response Bayesian assessment for C. parvum is presented for the infection endpoint and results indicate that a dose of 6 × 10 −6 oocysts per exposure corresponds to 10−4 infections per capita year. Expand
Cryptosporidium Infection Risk: Results of New Dose-Response Modeling.
  • M. Messner, Philip Berger
  • Biology, Medicine
  • Risk analysis : an official publication of the Society for Risk Analysis
  • 2016
TLDR
Cryptosporidium human dose-response data from seven species/isolates are used to investigate six models of varying complexity that estimate infection probability as a function of dose, suggesting additional inactivation and removal via treatment may be needed to meet any specified risk target. Expand
Comment on "Cryptosporidium Infection Risk: Results of New Dose-Response Modeling" - Discussion of Underlying Assumptions and Their Implications.
  • P. Schmidt, C. Chappell
  • Biology, Medicine
  • Risk analysis : an official publication of the Society for Risk Analysis
  • 2016
TLDR
It is suggested herein that further consideration is warranted before these new Cryptosporidium dose-response models are adopted in regulatory decision making, because of major concerns about the proposed models. Expand
Demonstrating the Benefits of Predictive Bayesian Dose-Response Relationships Using Six Exposure Studies of Cryptosporidium parvum.
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The adaptable model was applied to an existing data set for Campylobacter jejuni for model validation purposes, which yielded results that demonstrate the ability to analyze a dose-response function with limited data using and update those relationships with new data. Expand
A quantitative risk assessment of waterborne cryptosporidiosis in France using second-order Monte Carlo simulation.
A pragmatic quantitative risk assessment (QRA) of the risks of waterborne Cryptosporidium parvum infection and cryptosporidiosis in immunocompetent and immunodeficient French populations is proposed.Expand
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A conditional parametric dose-response function for gastroenteric illness is proposed here based on simple numerical models of self-organized host-pathogen systems and probabilistic arguments, and use of this form is demonstrated in a predictive Bayesian dose- response assessment for cryptosporidiosis. Expand
Bayesian risk assessment model of human cryptosporidiosis cases following consumption of raw Eastern oysters (Crassostrea virginica) contaminated with Cryptosporidium oocysts in the Hillsborough River system in Prince Edward Island, Canada
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It is suggested that Cryptosporidium oocysts are a potential cause of foodborne infection and illness when consuming raw oysters from Hillsborough River, one of the most important oyster production bays on Prince Edward Island. Expand
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D dose-response parameters for quantitative assessment of the relationship between airborne exposure to the reference A. fumigatus strain and probability of IA in immunocompromized hosts are provided. Expand
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The annual risks of infection from Cryptosporidium met the annual acceptable risk of 10(-4) at all sampling sites, whereas the risks of Giardia infection at the Verde River and the Salt River were 5.70 E-04 and 2.66E-04, respectively. Expand
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TLDR
A seasonal impact was identified among the oocyst counts of infected animals after controlling for age and prior protozoal risk level, and no seasonal impact on the incidence of C. parvum was detected among animals less than 61 days by negative binomial regression. Expand
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This paper presents a meta-analysis of 120 databases relating to Microbial Agents and Their Transmission and their Transmission and the Dose-Response Assessment that were generated during the preparation of this study. Expand
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A SIMPLE METHOD OF ESTIMATING FIFTY PER CENT ENDPOINTS
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