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Animal Health Risk Analysis
and the role of Epidemiology
Duration: 3 days
 The enthusiasm and level of preparation in the course certainly justified the large contingent of international participants who came to this course from all over the world. Particularly useful, even for those who do not expect to do formal risk analysis, is the time spent within the course on understanding the probability distributions that are fundamental to risk analysis.
Dr. R. Humphry
SAC Scotland
Course participant November 2005
Je vous remercie pour ces trois jours de cours sur l'analyse q uantitative du risque. J'ai vraiment apprécié l'expérience "intellectuelle" et humaine. Cette formation m'a apporté de nouvelles connaissances et une nouvelle façon de réfléchir.
Dr. Delphine Doctrinal Creton
Ecole Nationale Vétérinaire de Lyon
Course participant, Exeter April 2006
Training material We will provide lecture notes both in hardcopy and on CD. This CD also contains all model files produced for the course. Any extra models developed during the course are downloadable from a private course website. top |
Course format
The course runs from 9:00 am to 5:00 pm each day. Course will run with a minimum of 8 and a maximum of 20. Places will be given on a first come first served basis. Tea, coffee and lunch are provided.
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Who should attend
Anyone working in animal health or veterinary epidemiology who needs to conduct, present or critique risk analyses and needs to understand the relationship between Epidemiology and risk analysis.
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Laptops
Participants are required to bring laptops loaded with Microsoft Word, Microsoft Powerpoint and Microsoft Excel. Trial copies of Crystal Ball and @RISK are available free of charge from Decisioneering and Palisade web-sites but these should not be installed too early as trial versions run out after 7 days for Crystal Ball and 10 days for @RISK. We can arrange copies of @RISK at a 20% discount should you wish to purchase.
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Teaching philosophy
All of our course aim to help participants understand (rather than 'learn') risk analysis, which can only be achieved through a relaxed, informal and interactive environment, through plenty of examples and hands-on exercises where students apply and adapt what they have learned. We believe that:
When you hear something, you forget it.
When you see something, you remember it.
But not until you do something will you understand it
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Course content
Day 1
- Introduction to animal health risk analysis
- Background of risk analysis in animal health
- How epidemiology relates to risk assessment:
- Risk analysis is a team effort
- Role of epidemiologists in risk analysis
- Going from data to knowledge to a useful decision tool
- Dealing with the limits of current knowledge
- Introduction to statistical descriptors
- Mean, mode, standard deviation, skewness, kurtosis, percentiles
- Introduction to probability theory
- The use of distributions: uncertainty, variability and inter-individual variability
- Probability concepts
- Graphical representations of risk events: Venn diagrams, fault trees and event trees
- A look at some simple probability distributions
- Introduction to risk modelling
- Monte Carlo simulation, Crystal Ball/@RISK and Excel
- Calculation vs. simulation - the pros and cons of Monte Carlo
- Typical risk analysis results, their presentation and interpretation
- Practical problems to solve:
- Example to estimate costs of disease in country
Day 2
- Some stochastic processes - the basis of risk analysis
- Binomial Process
- Binomial, beta, negative binomial and geometric distributions
- Population state with imperfect test
- Poisson Process
- Poisson, gamma, and exponential distributions
- Modelling random outbreaks
- Hypergeometric process
- Hypergeometric and inverse Hypergeometric distributions
- Herd state estimate with imperfect diagnostic test
Day 3
- Modelling example for correlated health indicators
- Time-series correlation to determine optimal testing strategy
- Introduction to analyzing and using data for risk analysis:
- Statistical techniques
- Epidemiological techniques
- Why we need uncertainty distributions not confidence intervals in risk analysis
- Creating uncertainty distributions with standard tests
- t-tests, z-tests, Chi-squared tests
- Examples of estimation of population mean and standard deviation
- Example risk analyses based on epidemiological data:
- Danish Salmonella model
- FDA/CVM antimicrobial resistance model
- Belgian dioxin scare
- Wrap up and review of course material
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Additional information
Course fee includes all course-materials, tea, coffee and lunch.
Please, contact  for booking information.
You can email to  for any other questions about the course.
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