scholarly journals Measuring the success of an educational program through box-and-arrow diagram: A case study of the Quantitative Microbial Risk Assessment Interdisciplinary Instructional Institute

2018 ◽  
Author(s):  
Umesh Adhikari ◽  
Jade Mitchell ◽  
Julie Libarkin ◽  
Mark Weir
Keyword(s):  
Risk Assessment ◽  
Educational Program ◽  
Quantitative Microbial Risk Assessment ◽  
Microbial Risk Assessment ◽  
Microbial Risk

scholarly journals Improved methods for modelling drinking water treatment in quantitative microbial risk assessment; a case study of Campylobacter reduction by filtration and ozonation

2008 ◽  
Vol 6 (3) ◽  
pp. 301-314 ◽  
Author(s):  
P. W. M. H. Smeets ◽  
Y. J. Dullemont ◽  
P. H. A. J. M. Van Gelder ◽  
J. C. Van Dijk ◽  
G. J. Medema
Keyword(s):  
Risk Assessment ◽  
Drinking Water ◽  
Rare Events ◽  
Monitoring Data ◽  
Average Risk ◽  
Quantitative Microbial Risk Assessment ◽  
Risk Of Infection ◽  
Microbial Risk Assessment ◽  
Microbial Risk

Quantitative microbial risk assessment (QMRA) is increasingly applied to estimate drinking water safety. In QMRA the risk of infection is calculated from pathogen concentrations in drinking water, water consumption and dose response relations. Pathogen concentrations in drinking water are generally low and monitoring provides little information for QMRA. Therefore pathogen concentrations are monitored in the raw water and reduction of pathogens by treatment is modelled stochastically with Monte Carlo simulations. The method was tested in a case study with Campylobacter monitoring data of rapid sand filtration and ozonation processes. This study showed that the currently applied method did not predict the monitoring data used for validation. Consequently the risk of infection was over estimated by one order of magnitude. An improved method for model validation was developed. It combines non-parametric bootstrapping with statistical extrapolation to rare events. Evaluation of the treatment model was improved by presenting monitoring data and modelling results in CCDF graphs, which focus on the occurrence of rare events. Apart from calculating the yearly average risk of infection, the model results were presented in FN curves. This allowed for evaluation of both the distribution of risk and the uncertainty associated with the assessment.

scholarly journals Incorporating parameter uncertainty into Quantitative Microbial Risk Assessment (QMRA)

2011 ◽  
Vol 9 (1) ◽  
pp. 10-26 ◽  
Author(s):  
Margaret Donald ◽  
Kerrie Mengersen ◽  
Simon Toze ◽  
Jatinder P.S. Sidhu ◽  
Angus Cook
Keyword(s):  
Risk Assessment ◽  
Monte Carlo ◽  
Parameter Uncertainty ◽  
Statistical Models ◽  
Risk Assessments ◽  
Quantitative Microbial Risk Assessment ◽  
Microbial Risk Assessment ◽  
Microbial Risk ◽  
Sources Of Uncertainty

Modern statistical models and computational methods can now incorporate uncertainty of the parameters used in Quantitative Microbial Risk Assessments (QMRA). Many QMRAs use Monte Carlo methods, but work from fixed estimates for means, variances and other parameters. We illustrate the ease of estimating all parameters contemporaneously with the risk assessment, incorporating all the parameter uncertainty arising from the experiments from which these parameters are estimated. A Bayesian approach is adopted, using Markov Chain Monte Carlo Gibbs sampling (MCMC) via the freely available software, WinBUGS. The method and its ease of implementation are illustrated by a case study that involves incorporating three disparate datasets into an MCMC framework. The probabilities of infection when the uncertainty associated with parameter estimation is incorporated into a QMRA are shown to be considerably more variable over various dose ranges than the analogous probabilities obtained when constants from the literature are simply ‘plugged’ in as is done in most QMRAs. Neglecting these sources of uncertainty may lead to erroneous decisions for public health and risk management.

A quantitative microbial risk assessment of wastewater treatment plant blending: case study in San Francisco Bay

2016 ◽  
Vol 2 (1) ◽  
pp. 134-145 ◽  
Author(s):  
Edmund Y. Seto ◽  
Jon Konnan ◽  
Adam W. Olivieri ◽  
Richard E. Danielson ◽  
Donald M. D. Gray
Keyword(s):  
Risk Assessment ◽  
San Francisco ◽  
Treatment Plant ◽  
Extreme Rainfall ◽  
Quantitative Microbial Risk Assessment ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Microbial Risk Assessment ◽  
Microbial Risk

Quantitative Microbial Risk Assessment (QMRA) to assess health risk associated with increasing extreme rainfall events and the practice of wastewater blending.

Enteric virus presence in green vegetables and associated irrigation waters in a rural area from Argentina. A quantitative microbial risk assessment

LWT ◽  
2021 ◽  
Vol 144 ◽  
pp. 111201 ◽  
Author(s):  
Prez Verónica Emilse ◽  
Victoria Matías ◽  
Martínez Laura Cecilia ◽  
Giordano Miguel Oscar ◽  
Masachessi Gisela ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Rural Area ◽  
Enteric Virus ◽  
Quantitative Microbial Risk Assessment ◽  
Microbial Risk Assessment ◽  
Microbial Risk ◽  
Irrigation Waters
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