A generalized Monte Carlo simulation model for decision risk analysis illustrated with a Dutch elm disease control example

1981 ◽  
Vol 11 (2) ◽  
pp. 343-351
Author(s):  
David R. Betters ◽  
James C. Schaefer
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Model ◽  
Control Strategies ◽  
Basic Structure ◽  
Probability Density Functions ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Dutch Elm Disease ◽  
Monte Carlo Simulation Model

Decision making is often performed under conditions of uncertainty and risk. A Monte Carlo simulation model is described which can help analyze alternatives where these conditions exist. The simulation model has a generalized format so that it may be easily applied to a number of different situations. The basic structure of the simulation model is discussed, and then the model is applied to a problem involving two Dutch elm disease (DED) control strategies. The simulation results, including probability density functions and cumulative distribution functions, are described.

scholarly journals Comparison of different control strategies for classical swine fever using emergency vaccination and rapid PCR testing by using a Monte-Carlo simulation model

2013 ◽  
Vol 56 (1) ◽  
pp. 988-1004 ◽  
Author(s):  
J. Brosig ◽  
I. Traulsen ◽  
S. Blome ◽  
K. Depner ◽  
J. Krieter
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Model ◽  
Control Strategies ◽  
Control Measure ◽  
Classical Swine Fever ◽  
Current Control ◽  
Control Measures ◽  
Alternative Control ◽  
Monte Carlo Simulation Model

Abstract. Whenever an outbreak of classical swine fever has occurred in the European Union (EU), the basic control measures have usually been supplemented by preventive culling. This strategy has led to a great number of culled pigs and is discussed by general public and politics from both ethical and economic points of view. Emergency vaccination has been deemed to be an alternative control measure for some time now. PCR testing also provides a possible future strategy, since this method would allow a rapid and reliable testing of pigs in the vicinity of an outbreak farm. In this study, a spatial and temporal Monte-Carlo simulation model was used to compare alternative control strategies based upon these two measures (»Emergency Vaccination«, »Test To Slaughter«, »Test To Control« and »Vaccination in conjunction with Rapid Testing«) with the current control strategy. Two regions for investigation with different farm densities were used in the model. In a region with a low farm density, the basic EU control measures seemed to be sufficient to control an epidemic. In a region with a high farm density, additional measures would be necessary. »Emergency Vaccination« in a 3 km application zone and »Traditional Control« reached the same level of infected farms. Both »Test To Slaughter« and »Test To Control« combined with preventive culling led to a lower number of infected farms compared to the sole preventive culling strategy. The alternative control measures can reduce the number of culled farms significantly compared to »Traditional Control«.

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.

Monte Carlo Simulation Model of Micro Light Beam Solidification

2008 ◽  
Vol 28 (12) ◽  
pp. 2388-2393
Author(s):  
王翔 Wang Xiang ◽  
裴香涛 Pei Xiangtao ◽  
邵鹏 Shao Peng ◽  
黄文浩 Huang Wenhao
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Model ◽  
Light Beam ◽  
Monte Carlo Simulation Model
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