scholarly journals Cheese Microbial Risk Assessments — A Review

2016 ◽  
Vol 29 (3) ◽  
pp. 307-314 ◽  
Author(s):  
Kyoung-Hee Choi ◽  
Heeyoung Lee ◽  
Soomin Lee ◽  
Sejeong Kim ◽  
Yohan Yoon
Keyword(s):  
Risk Assessments ◽  
Microbial Risk

Trends in conducting quantitative microbial risk assessments for water reuse systems: A review

2020 ◽  
Vol 16 ◽  
pp. 100132
Author(s):  
Veronika Zhiteneva ◽  
Uwe Hübner ◽  
Gertjan J. Medema ◽  
Jörg E. Drewes
Keyword(s):  
Water Reuse ◽  
Risk Assessments ◽  
Microbial Risk

scholarly journals Case studies in Bayesian microbial risk assessments

2009 ◽  
Vol 8 (Suppl 1) ◽  
pp. S19 ◽  
Author(s):  
Marc C Kennedy ◽  
Helen E Clough ◽  
Joanne Turner
Keyword(s):  
Case Studies ◽  
Risk Assessments ◽  
Microbial Risk

Guidelines for the safe use of wastewater in agriculture: revisiting WHO guidelines

2004 ◽  
Vol 50 (2) ◽  
pp. 31-38 ◽  
Author(s):  
R.M. Carr ◽  
U.J. Blumenthal ◽  
D. Duncan Mara
Keyword(s):  
Health Care ◽  
Drinking Water ◽  
Relevant Information ◽  
Epidemiological Studies ◽  
Risk Assessments ◽  
Who Guidelines ◽  
Microbial Risk ◽  
Scarce Resources ◽  
Health Measures ◽  
Hygiene Promotion

The use of wastewater in agriculture is occurring more frequently because of water scarcity and population growth. Often the poorest households rely on this resource for their livelihood and food security needs. However, there are negative health implications of this practice that need to be addressed. WHO developed Guidelines for the Safe Use of Wastewater in Agriculture in 1989. The Guidelines are currently being revised based on new data from epidemiological studies, quantitative microbial risk assessments and other relevant information. WHO guidelines must be practical and offer feasible risk management solutions that will minimize health threats and allow for the beneficial use of scarce resources. To achieve the greatest impact on health, guidelines should be implemented with other health measures such as: health education, hygiene promotion, provision of adequate drinking water and sanitation, and other health care measures.

An integrated environmental modeling framework for performing Quantitative Microbial Risk Assessments

2014 ◽  
Vol 55 ◽  
pp. 77-91 ◽  
Author(s):  
Gene Whelan ◽  
Keewook Kim ◽  
Mitch A. Pelton ◽  
Jeffrey A. Soller ◽  
Karl J. Castleton ◽  
...  
Keyword(s):  
Environmental Modeling ◽  
Risk Assessments ◽  
Modeling Framework ◽  
Microbial Risk

Quantitative Microbial Risk Assessment for Escherichia coli O157:H7, Salmonella enterica, and Listeria monocytogenes in Leafy Green Vegetables Consumed at Salad Bars, Based on Modeling Supply Chain Logistics

2010 ◽  
Vol 73 (10) ◽  
pp. 1830-1840 ◽  
Author(s):  
S. O. TROMP ◽  
H. RIJGERSBERG ◽  
E. FRANZ
Keyword(s):  
Escherichia Coli ◽  
Supply Chain ◽  
Listeria Monocytogenes ◽  
Salmonella Enterica ◽  
Discrete Event ◽  
Risk Assessments ◽  
Fetal Mortality ◽  
Escherichia Coli O157 ◽  
Microbial Risk ◽  
Supply Chain Logistics

Quantitative microbial risk assessments do not usually account for the planning and ordering mechanisms (logistics) of a food supply chain. These mechanisms and consumer demand determine the storage and delay times of products. The aim of this study was to quantitatively assess the difference between simulating supply chain logistics (MOD) and assuming fixed storage times (FIX) in microbial risk estimation for the supply chain of fresh-cut leafy green vegetables destined for working-canteen salad bars. The results of the FIX model were previously published (E. Franz, S. O. Tromp, H. Rijgersberg, and H. J. van der Fels-Klerx, J. Food Prot. 73:274–285, 2010). Pathogen growth was modeled using stochastic discrete-event simulation of the applied logistics concept. The public health effects were assessed by conducting an exposure assessment and risk characterization. The relative growths of Escherichia coli O157 (17%) and Salmonella enterica (15%) were identical in the MOD and FIX models. In contrast, the relative growth of Listeria monocytogenes was considerably higher in the MOD model (1,156%) than in the FIX model (194%). The probability of L. monocytogenes infection in The Netherlands was higher in the MOD model (5.18 × 10−8) than in the FIX model (1.23 × 10−8). The risk of listeriosis-induced fetal mortality in the perinatal population increased from 1.24 × 10−4 (FIX) to 1.66 × 10−4 (MOD). Modeling the probabilistic nature of supply chain logistics is of additional value for microbial risk assessments regarding psychrotrophic pathogens in food products for which time and temperature are the postharvest preventive measures in guaranteeing food safety.

Food Consumption Data in Microbiological Risk Assessment

2004 ◽  
Vol 67 (9) ◽  
pp. 1972-1976 ◽  
Author(s):  
LEILA M. BARRAJ ◽  
BARBARA J. PETERSEN
Keyword(s):  
Risk Assessment ◽  
Food Consumption ◽  
Risk Assessments ◽  
Current Status ◽  
Microbial Risk Assessment ◽  
Microbial Risk ◽  
Individual Level ◽  
Consumption Data ◽  
Food Consumption Data ◽  
Summary Data

The 1st International Conference on Microbiological Risk Assessment: Foodborne Hazards was held in July 2002. One of the goals of that conference was to evaluate the current status and future needs and directions of the science of microbial risk assessment. This article is based in part on a talk presented at that meeting. Here, we review the types of food consumption data available for use in microbial risk assessments and address their strengths and limitations. Consumption data available range from total population summary data derived from food production statistics to detailed information, derived from national food consumption surveys, about the types and amounts of food consumed at the individual level. Although population summary data are available for most countries, detailed data are available for a limited number of countries and may only be available in summary format. Despite the relatively large amount of detailed information collected by these national surveys, information crucial to microbial risk assessments, such as the specific types of foods, the eating patterns of susceptible populations, or an individual's propensity for consuming high-risk foods (e.g., eating undercooked hamburgers, raw shellfish, or temperature-abused foods), are not collected during these surveys.

Quantitative microbial risk assessments for drinking water facilities: evaluation of a range of treatment strategies

2019 ◽  
Vol 5 (11) ◽  
pp. 1943-1955
Author(s):  
Joshua G. Elliott ◽  
Liz Taylor-Edmonds ◽  
Robert C. Andrews
Keyword(s):  
Drinking Water ◽  
Treatment Strategies ◽  
Risk Assessments ◽  
Microbial Risk

Impact of treatment on pathogen risk.

scholarly journals Simulation of enteric pathogen concentrations in locally-collected greywater and wastewater for microbial risk assessments

2017 ◽  
Vol 5 ◽  
pp. 44-52 ◽  
Author(s):  
Michael A. Jahne ◽  
Mary E. Schoen ◽  
Jay L. Garland ◽  
Nicholas J. Ashbolt
Keyword(s):  
Enteric Pathogen ◽  
Risk Assessments ◽  
Microbial Risk

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.

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