Strengths and weaknesses of Monte Carlo simulation models and Bayesian belief networks in microbial risk assessment

The first step in quantitative microbial risk assessment (QMRA) is to determine distribution of pathogen contamination among servings of the food at some point in the farm-to-table chain. In the present study, distribution of Salmonella contamination among servings of chicken liver for use in QMRA was determined at meal preparation. A combination of five methods: 1) whole sample enrichment; 2) quantitative polymerase chain reaction; 3) cultural isolation; 4) serotyping; and 5) Monte Carlo simulation were used to determine Salmonella prevalence (P), number (N), and serotype for different serving sizes. In addition, epidemiological data were used to convert serotype data to virulence (V) values for use in QMRA. A Monte Carlo simulation model based in Excel and simulated with @Risk predicted Salmonella P, N, serotype, and V as a function of serving size from one (58 g) to eight (464 g) chicken livers. Salmonella P of chicken livers was 72.5% (58/80) per 58 g. Four serotypes were isolated from chicken livers: 1) Infantis (P = 28%, V = 4.5); 2) Enteritidis (P = 15%, V = 5); 3) Typhimirium (P = 15%, V = 4.8); and 4) Kentucky (P = 15%, V = 0.8). Median Salmonella N was 1.76 log per 58 g (range: 0 to 4.67 log/58 g) and was not affected ( P > 0.05) by serotype. The model predicted a non-linear increase ( P ≤ 0.05) of Salmonella P from 72.5% per 58 g to 100% per 464 g, minimum N from 0 log per 58 g to 1.28 log per 464 g, and median N from 1.76 log per 58 g to 3.22 log per 464 g. Regardless of serving size, predicted maximum N was 4.74 log, mean V was 3.9, and total N was 6.65 log per lot (10,000 chicken livers). The data acquired and model developed in this study fill an important data and modeling gap in QMRA for Salmonella and chicken liver.

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Simulation Modeling for Microbial Risk Assessment

Journal of Food Protection ◽

10.4315/0362-028x-61.11.1560 ◽

1998 ◽

Vol 61 (11) ◽

pp. 1560-1566 ◽

Cited By ~ 51

Author(s):

MICHAEL H. CASSIN ◽

GREG M. PAOLI ◽

ANNA M. LAMMERDING

Keyword(s):

Risk Assessment ◽

Monte Carlo Simulation ◽

Monte Carlo ◽

Complex Function ◽

Quantitative Microbial Risk Assessment ◽

Microbial Risk Assessment ◽

Microbial Risk ◽

Adverse Health Outcomes ◽

Complex Processes ◽

Microbial Environment

Quantitative microbial risk assessment implies an estimation of the probability and impact of adverse health outcomes due to microbial hazards. In the case of food safety, the probability of human illness is a complex function of the variability of many parameters that influence the microbial environment, from the production to the consumption of a food. The analytical integration required to estimate the probability of foodborne illness is intractable in all but the simplest of models. Monte Carlo simulation is an alterative to computing analytical Solutions. In some cases, a risk assessment may be commissioned to serve a larger purpose than simply the estimation of risk. A Monte Carlo simulation can provide insights into complex processes that are invaluable, and otherwise unavailable, to those charged with the task of risk management. Using examples from a farm-to-fork model of the fate of Escherichia coli O157:H7 in ground beef hamburgers, this paper describes specifically how such goals as research prioritization, risk-based characterization of control points, and risk-based comparison of intervention strategies can be objectively achieved using Monte Carlo simulation.

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Quantitative Microbial Risk Assessment for Escherichia coli O157:H7, Salmonella, and Listeria monocytogenes in Leafy Green Vegetables Consumed at Salad Bars

Journal of Food Protection ◽

10.4315/0362-028x-73.2.274 ◽

2010 ◽

Vol 73 (2) ◽

pp. 274-285 ◽

Cited By ~ 64

Author(s):

E. FRANZ ◽

S. O. TROMP ◽

H. RIJGERSBERG ◽

H. J. van der FELS-KLERX

Keyword(s):

Public Health ◽

Escherichia Coli ◽

Risk Assessment ◽

Monte Carlo ◽

Escherichia Coli O157 ◽

Quantitative Microbial Risk Assessment ◽

Microbial Risk Assessment ◽

Microbial Risk ◽

The Public ◽

E Coli

Fresh vegetables are increasingly recognized as a source of foodborne outbreaks in many parts of the world. The purpose of this study was to conduct a quantitative microbial risk assessment for Escherichia coli O157:H7, Salmonella, and Listeria monocytogenes infection from consumption of leafy green vegetables in salad from salad bars in The Netherlands. Pathogen growth was modeled in Aladin (Agro Logistics Analysis and Design Instrument) using time-temperature profiles in the chilled supply chain and one particular restaurant with a salad bar. A second-order Monte Carlo risk assessment model was constructed (using @Risk) to estimate the public health effects. The temperature in the studied cold chain was well controlled below 5°C. Growth of E. coli O157:H7 and Salmonella was minimal (17 and 15%, respectively). Growth of L. monocytogenes was considerably greater (194%). Based on first-order Monte Carlo simulations, the average number of cases per year in The Netherlands associated the consumption leafy greens in salads from salad bars was 166, 187, and 0.3 for E. coli O157:H7, Salmonella, and L. monocytogenes, respectively. The ranges of the average number of annual cases as estimated by second-order Monte Carlo simulation (with prevalence and number of visitors as uncertain variables) were 42 to 551 for E. coli O157:H7, 81 to 281 for Salmonella, and 0.1 to 0.9 for L. monocytogenes. This study included an integration of modeling pathogen growth in the supply chain of fresh leafy vegetables destined for restaurant salad bars using software designed to model and design logistics and modeling the public health effects using probabilistic risk assessment software.

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The Application of Quantitative Risk Assessment to Microbial Food Safety

Journal of Food Protection ◽

10.4315/0362-028x-61.5.640 ◽

1998 ◽

Vol 61 (5) ◽

pp. 640-648 ◽

Cited By ~ 60

Author(s):

DAVID JOHN VOSE

Keyword(s):

Risk Assessment ◽

Monte Carlo Simulation ◽

Monte Carlo ◽

Food Safety ◽

Probability Distributions ◽

Simulation Models ◽

Practical Method ◽

Assessment Model ◽

Quantitative Risk Assessment ◽

Suitable Model

Quantitative risk assessment (QRA) is rapidly accumulating recognition as the most practical method for assessing the risks associated with microbial contamination of foodstuffs. These risk analyses are most commonly developed in commercial Computer spreadsheet applications, combined with Monte Carlo simulation add-ins that enable probability distributions to be inserted into a spreadsheet. If a suitable model structure can be defined and all of the variables within that model reasonably quantified, a QRA will demonstrate the sensitivity of the severity of the risk to each stage in the risk-assessment model. It can therefore provide guidance for the selection of appropriate risk-reduction measures and a quantitative assessment of the benefits and costs of these proposed measures. However, very few reports explaining QRA models have been submitted for publication in this area. There is, therefore, little guidance available to those who intend to embark on a full microbial QRA. This paper looks at a number of modeling techniques that can help produce more realistic and accurate Monte Carlo simulation models. The use and limitations of several distributions important to microbial risk assessment are explained. Some simple techniques specific to Monte Carlo simulation modelling of microbial risks using spreadsheets are also offered which will help the analyst more realistically reflect the uncertain nature of the scenarios being modeled. simulation, food safety

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Incorporating parameter uncertainty into Quantitative Microbial Risk Assessment (QMRA)

Journal of Water and Health ◽

10.2166/wh.2010.073 ◽

2011 ◽

Vol 9 (1) ◽

pp. 10-26 ◽

Cited By ~ 7

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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Health Risk from the Use of Roof-Harvested Rainwater in Southeast Queensland, Australia, as Potable or Nonpotable Water, Determined Using Quantitative Microbial Risk Assessment

Applied and Environmental Microbiology ◽

10.1128/aem.00944-10 ◽

2010 ◽

Vol 76 (22) ◽

pp. 7382-7391 ◽

Cited By ~ 85

Author(s):

W. Ahmed ◽

A. Vieritz ◽

A. Goonetilleke ◽

T. Gardner

Keyword(s):

Risk Assessment ◽

Monte Carlo ◽

Threshold Value ◽

Monte Carlo Analysis ◽

Daily Basis ◽

Quantitative Microbial Risk Assessment ◽

Risk Of Infection ◽

Microbial Risk Assessment ◽

Microbial Risk ◽

Southeast Queensland

ABSTRACT A total of 214 rainwater samples from 82 tanks were collected in urban Southeast Queensland (SEQ) in Australia and analyzed for the presence and numbers of zoonotic bacterial and protozoal pathogens using binary PCR and quantitative PCR (qPCR). Quantitative microbial risk assessment (QMRA) analysis was used to quantify the risk of infection associated with the exposure to potential pathogens from roof-harvested rainwater used as potable or nonpotable water. Of the 214 samples tested, 10.7%, 9.8%, 5.6%, and 0.4% were positive for the Salmonella invA, Giardia lamblia β-giardin, Legionella pneumophila mip, and Campylobacter jejuni mapA genes, respectively. Cryptosporidium parvum oocyst wall protein (COWP) could not be detected. The estimated numbers of Salmonella, G. lamblia, and L. pneumophila organisms ranged from 6.5 × 101 to 3.8 × 102 cells, 0.6 × 10° to 3.6 × 10° cysts, and 6.0 × 101 to 1.7 × 102 cells per 1,000 ml of water, respectively. Six risk scenarios were considered for exposure to Salmonella spp., G. lamblia, and L. pneumophila. For Salmonella spp. and G. lamblia, these scenarios were (i) liquid ingestion due to drinking of rainwater on a daily basis, (ii) accidental liquid ingestion due to hosing twice a week, (iii) aerosol ingestion due to showering on a daily basis, and (iv) aerosol ingestion due to hosing twice a week. For L. pneumophila, these scenarios were (i) aerosol inhalation due to showering on a daily basis and (ii) aerosol inhalation due to hosing twice a week. The risk of infection from Salmonella spp., G. lamblia, and L. pneumophila associated with the use of rainwater for showering and garden hosing was calculated to be well below the threshold value of one extra infection per 10,000 persons per year in urban SEQ. However, the risk of infection from ingesting Salmonella spp. and G. lamblia via drinking exceeded this threshold value and indicated that if undisinfected rainwater is ingested by drinking, then the incidences of the gastrointestinal diseases salmonellosis and giardiasis are expected to range from 9.8 × 10° to 5.4 × 101 (with a mean of 1.2 × 101 from Monte Carlo analysis) and from 1.0 × 101 to 6.5 × 101 cases (with a mean of 1.6 × 101 from Monte Carlo analysis) per 10,000 persons per year, respectively, in urban SEQ. Since this health risk seems higher than that expected from the reported incidences of gastroenteritis, the assumptions used to estimate these infection risks are critically examined. Nonetheless, it would seem prudent to disinfect rainwater for use as potable water.

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