Comparison of Monte Carlo and Fuzzy Math Simulation Methods for Quantitative Microbial Risk Assessment

2003 ◽  
Vol 66 (10) ◽  
pp. 1900-1910 ◽  
Author(s):  
VALERIE J. DAVIDSON ◽  
JOANNE RYKS
Keyword(s):  
Risk Assessment ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Interval Arithmetic ◽  
Food Systems ◽  
Probability Distributions ◽  
Economic Cost ◽  
Fuzzy Mathematics ◽  
Quantitative Microbial Risk Assessment ◽  
Mean Values

The objective of food safety risk assessment is to quantify levels of risk for consumers as well as to design improved processing, distribution, and preparation systems that reduce exposure to acceptable limits. Monte Carlo simulation tools have been used to deal with the inherent variability in food systems, but these tools require substantial data for estimates of probability distributions. The objective of this study was to evaluate the use of fuzzy values to represent uncertainty. Fuzzy mathematics and Monte Carlo simulations were compared to analyze the propagation of uncertainty through a number of sequential calculations in two different applications: estimation of biological impacts and economic cost in a general framework and survival of Campylobacter jejuni in a sequence of five poultry processing operations. Estimates of the proportion of a population requiring hospitalization were comparable, but using fuzzy values and interval arithmetic resulted in more conservative estimates of mortality and cost, in terms of the intervals of possible values and mean values, compared to Monte Carlo calculations. In the second application, the two approaches predicted the same reduction in mean concentration (−4 log CFU/ml of rinse), but the limits of the final concentration distribution were wider for the fuzzy estimate (−3.3 to 5.6 log CFU/ml of rinse) compared to the probability estimate (−2.2 to 4.3 log CFU/ml of rinse). Interval arithmetic with fuzzy values considered all possible combinations in calculations and maximum membership grade for each possible result. Consequently, fuzzy results fully included distributions estimated by Monte Carlo simulations but extended to broader limits. When limited data defines probability distributions for all inputs, fuzzy mathematics is a more conservative approach for risk assessment than Monte Carlo simulations.

scholarly journals Human health risk assessment using Monte Carlo simulations for groundwater with uranium in southern India

2021 ◽  
Vol 226 ◽  
pp. 112781
Author(s):  
RamyaPriya Ramesh ◽  
Manoj Subramanian ◽  
Elango Lakshmanan ◽  
Anbarasu Subramaniyan ◽  
Gowrisankar Ganesan
Keyword(s):  
Risk Assessment ◽  
Monte Carlo ◽  
Health Risk ◽  
Monte Carlo Simulations ◽  
Human Health ◽  
Health Risk Assessment ◽  
Human Health Risk ◽  
Human Health Risk Assessment ◽  
Southern India

scholarly journals Validation of a Monte Carlo Modelling Based Dosimetry of Extraoral Photobiomodulation

Diagnostics ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2207
Author(s):  
Anna N. Yaroslavsky ◽  
Amy F. Juliano ◽  
Ather Adnan ◽  
Wayne J. Selting ◽  
Tyler W. Iorizzo ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Light Emitting Diode ◽  
Morphological Data ◽  
Transmitted Power ◽  
Light Dosimetry ◽  
Mean Values ◽  
Mri Scans ◽  
The Difference

An in vivo validation study was performed to confirm the accuracy of extraoral photobiomodulation therapy (PBMT) dosimetry determined by modelling. The Monte Carlo technique was utilized to calculate the fluence rate and absorbed power of light delivered through multi-layered tissue. Optical properties used during Monte Carlo simulations were taken from the literature. Morphological data of four study volunteers were acquired using magnetic resonance imaging (MRI) scans. Light emitting diode (LED) coupled to a power meter were utilized to measure transmitted power through each volunteer’s cheek, in vivo. The transmitted power determined by Monte Carlo modelling was compared to the in vivo measurements to determine the accuracy of the simulations. Experimental and simulation results were in good agreement for all four subjects. The difference between the mean values of the measured transmission was within 12% from the respective transmission obtained using Monte Carlo simulations. The results of the study indicate that Monte Carlo modelling is a robust and reliable method for light dosimetry.

Monte Carlo Simulation Model for Predicting Salmonella Contamination of Chicken Liver as a Function of Serving Size for Use in Quantitative Microbial Risk Assessment

2021 ◽  
Author(s):  
Thomas Oscar
Keyword(s):  
Risk Assessment ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Model ◽  
Chicken Liver ◽  
Quantitative Microbial Risk Assessment ◽  
Microbial Risk Assessment ◽  
Microbial Risk ◽  
Serving Size ◽  
Monte Carlo Simulation Model

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.

Probabilistic High-Cycle Fatigue Risk Assessment of an Integrally Bladed Rotor

2011 ◽  
Author(s):  
Benjamin D. Hall ◽  
Lauren Gray
Keyword(s):  
Risk Assessment ◽  
Monte Carlo ◽  
High Cycle Fatigue ◽  
Probability Distributions ◽  
Direct Monte Carlo Simulation ◽  
Model Parameters ◽  
Cycle Fatigue ◽  
Turbine Engine ◽  
Engineering Models ◽  
Mcmc Simulation

A fully probabilistic high-cycle fatigue (HCF) risk assessment methodology for application to turbine engine blades is described. The assessment uses the Bayesian paradigm of probability theory in which probability distributions are used to encode states of knowledge. Multi-level (or hierarchical) models are employed to capture engineering knowledge of the factors important for assessing HCF risk. This structure allows us to use standard probability distributions to adequately represent uncertainties in model parameters. The model accounts for engine-to-engine, run-to-run, and blade-to-blade variability as well as uncertainty in material capability, usage (flight conditions, time at resonance), and steady and vibratory stresses. Markov chain Monte Carlo (MCMC) simulation is used to fit observed data to the engineering models, then direct Monte Carlo simulation is used to assess the HCF risk.

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

2010 ◽  
Vol 73 (2) ◽  
pp. 274-285 ◽  
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.

The Application of Quantitative Risk Assessment to Microbial Food Safety

1998 ◽  
Vol 61 (5) ◽  
pp. 640-648 ◽  
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

Risk assessment modeling of bio-based chemicals economics based on Monte-Carlo simulations

2020 ◽  
Vol 163 ◽  
pp. 273-280
Author(s):  
Endrit Dheskali ◽  
Apostolis A. Koutinas ◽  
Ioannis K. Kookos
Keyword(s):  
Risk Assessment ◽  
Monte Carlo ◽  
Monte Carlo Simulations

scholarly journals SINH-ACCELERATION: EFFICIENT EVALUATION OF PROBABILITY DISTRIBUTIONS, OPTION PRICING, AND MONTE CARLO SIMULATIONS

2019 ◽  
Vol 22 (03) ◽  
pp. 1950011 ◽  
Author(s):  
SVETLANA BOYARCHENKO ◽  
SERGEI LEVENDORSKIĬ
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Regime Switching ◽  
Probability Distributions ◽  
General Scheme ◽  
Characteristic Exponent ◽  
Heston Model ◽  
Characteristic Functions ◽  
Quadratic Term ◽  
Quadratic Term Structure

Characteristic functions of several popular classes of distributions and processes admit analytic continuation into unions of strips and open coni around [Formula: see text]. The Fourier transform techniques reduce calculation of probability distributions and option prices in the evaluation of integrals whose integrands are analytic in domains enjoying these properties. In the paper, we suggest to use changes of variables of the form [Formula: see text] and the simplified trapezoid rule to evaluate the integrals accurately and fast. We formulate the general scheme, and apply the scheme for calculation probability distributions and pricing European options in Lévy models, the Heston model, the CIR model, and a Lévy model with the CIR-subordinator. We outline applications to fast and accurate calibration procedures and Monte Carlo simulations in Lévy models, regime switching Lévy models that can account for stochastic drift, volatility and skewness, the Heston model, other affine models and quadratic term structure models. For calculation of quantiles in the tails using the Newton or bisection method, it suffices to precalculate several hundreds of values of the characteristic exponent at points on an appropriate grid (conformal principal components) and use these values in formulas for cpdf and pdf.

The Effect of Parameter Uncertainties in an Age–Length Relationship upon Estimating the Age Composition of Catches

1983 ◽  
Vol 40 (3) ◽  
pp. 272-280 ◽  
Author(s):  
J. Majkowski ◽  
J. Hampton
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Probability Distributions ◽  
Bluefin Tuna ◽  
Parameter Uncertainties ◽  
Age Classes ◽  
Age Composition ◽  
Length Relationship ◽  
Southern Bluefin Tuna

A simple, but frequently applied, procedure for decomposing fish length frequencies into age-classes is considered. This decomposition consists of converting fish lengths into ages using an age–length relationship. A method for assessing the effect of parameter uncertainties in this relationship upon estimates of the age composition of catches is presented. It is assumed that the parameter uncertainties can be described by probability distributions. Our aim is to determine probability distributions of age composition estimates resulting from these uncertainties. This is done using a stochastic sensitivity analysis technique involving Monte Carlo simulations and/or a first-order theory if such a theory is valid in the case under consideration. The method is illustrated by its application to data from the southern bluefin tuna (Thunnus maccoyii) fishery. It is found that simulated (Monte Carlo) catch estimates for age-classes 3 (fish at age 2–3 yr) to 13 (fish at age 12–13 yr) are normally distributed. The coefficients of variation of these estimates are less than 12%. Simulated catch estimates for age-classes 1, 2, and 14–20 deviate considerably from normality and their ranges bounded by the 2.5 and 97.5 percentiles are extremely wide; they include values different by up to 810% from the best deterministic catch estimates.Key words: catch, age composition, uncertainties, sensitivity analysis, Monte Carlo simulations, southern bluefin tuna

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