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Duration: 5 days
This 5-day course will cover the key principles of quantitative risk analysis in epidemiology and the most important risk modeling principles, methods and techniques available. In addition, the course will discuss the theory and practical methods to model the spread of diseases among populations. The course will help the participants comfortable with risk analysis modeling environments (in this case @RISK with Excel and a few examples with the statistical software R, but the lessons apply equally well to other modeling environments). The focus of the course is however on how to conduct accurate and effective quantitative risk analyses, including best practices of risk modeling, selecting the appropriate distribution, using data and expert opinion, and avoiding common mistakes. The course will also cover essential probability and statistics theory and various stochastic processes to provide course participants with a solid understanding of quantitative risk analysis.
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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.
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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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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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Participants are required to bring laptops loaded with Microsoft Word, Microsoft Powerpoint and Microsoft Excel. Trial copy of @RISK is available free of charge from the Palisade web-site but these should not be installed too early as trial versions run out after 10 days.
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This course aims to help participants understand rather than just learn the steps to do a risk analysis. This can only be achieved in a relaxed, informal and interactive environment using 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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ModelAssist from Vose Consulting is a comprehensive risk analysis training and reference software tool. ModelAssist for Crystal Ball and ModelAssist for @RISK provide an in-depth explanation of many risk analysis concepts, techniques and methods and will therefore certainly greatly complements the modeling issues discussed during this course. It is particularly helpful as a reference for participants of the material that has been presented during the course.
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Day 1
Introduction to risk analysis
- Background of risk analysis and risk management
- Risk analysis as a team effort
- Dealing with the limits of current knowledge
- Difficulties in modeling biological systems
Introduction to basic 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 basic probability distributions
Introduction to risk modeling
- Monte Carlo simulation, Excel add-ons (@Risk) and more advanced simulation tools (R, S-Plus)
- Calculation vs. simulation
- Typical risk analysis results, their presentation and interpretation
- Practical problems to solve
Some simple probability distributions Day 2
Basic stochastic processes
- Binomial Process
- Binomial, beta, negative binomial and geometric distributions
- Poisson Process
- Poisson, gamma, and exponential distributions
- Practical problems to solve
Day 3
Basic stochastic processes (continued)
- Extra Binomial and Poisson problems
- Hypergeometric process
- Hypergeometric and inverse Hypergeometric distributions
- Practical problems to solve
Good practices in risk modeling
Common mistakes and how to prevent them
Day 4
Analyzing and using data for risk analysis in epidemiology
- Statistical and 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
- Quick introduction to alternatives to standard tests - Bayesian and non-parametric methods
- Determining distributions from data
- Assessing validity of data
- Distribution fitting
Day 5
Disease spread (epidemic) simulation modeling
- Introduction to disease spread modeling
- The dynamics of infectious diseases in populations, state transition diagrams, and basic disease parameters
- Difference and differential equations, "agent-based" simulation models
- The simple SIR and SEIR models
- Extensions to the simple models: stochastic, spatially explicit models, multiple species/epidemiological populations
- Hands-on development of a stochastic model using Excel and @Risk
Example risk analyses based on epidemiological data (if time permits)
Wrap up and course evaluation top |
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©Copyright 1997-2008 Vose Consulting. All Rights Reserved.
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