Course content
Day 1 - Monday (Risk Managers)
- Introduction to risk analysis
- Valuing risk, and determining objectives
- Seeing risk management as a whole
- Identifying risk mitigation strategies
- The roles of risk managers and risk assessors
- Risk communication
- Evaluating risk management strategies: the views of regulators and industry
- FDA-CVM's guidance 152 and how to respond to it
- Types of risk assessments
- The history and application of international guidelines
- Review of past microbial and antimicrobial risk assessments
- Debate
Day 2 - Tuesday (Risk Managers and Risk Analysts)
- Introduction to risk assessment:
- Defining the problem and scope
- Moving from an intellectual exercise to a useful decision tool
- Identifying the hazard(s) and mitigations
- Establishing risk assessment objectives
- Creating and managing a risk assessment team
- Introduction to microbial and antimicrobial risk analysis
- Critically reviewing available data and methods
- Planning an appropriate risk assessment
- Quality controls for a risk assessment
- Critiquing a risk assessment within the context of the defined objectives
Day 3 - Wednesday (Risk Managers and Risk Analysts)
- Risk attribution
- The meaning of attributing risk
- Data to support risk attribution
- Methods of analysis to determine risk attribution
- Errors and invalid arguments
Day 4 - Thursday (Risk Analysts)
- Risk modeling basics
- Monte Carlo simulation, Crystal Ball or @RISK and Excel
- Calculation vs. simulation
- Monte Carlo vs. Latin Hypercube sampling
- Uncertainty, variability and inter-individual variability
- Probability theory
- Probability concepts
- Graphical representations of risk events: Venn diagrams, fault trees and event trees
- Probability vs. population distributions, relative vs. cumulative, discrete vs. continuous
- Stochastic processes
- Introduction to stochastic processes and their use in antimicrobial risk analysis
- Binomial process
- Problems to solve
Day 5 - Friday (Risk Analysts)
- Stochastic processes continued
- Poisson process
- Hypergeometric process
- Central Limit Theorem
- Problems to solve
Day 6 - Monday (Risk Analysts) Quantifying statistical uncertainty
- Meaning of uncertainty, randomness and variability
- The value of their distinction, modeling techniques
- Types of uncertainty
- Structures of two-dimensional (second order) risk analysis models
- Classical statistics
- Estimation of population mean and standard deviation
- Estimation of population prevalence and Poisson mean
- Bayesian Theorem
- Theory and derivation
- Simple examples
- Algebraic and simulation solutions
- Problems related to microbial and antimicrobial risk analysis to solve
Day 7 - Tuesday (Risk Analysts)
- Bayesian inference with Markov Chain models
- WinBUGS
- Applications and problems to solve
- The Bootstrap
- Non-parametric and parametric Bootstrap techniques
- Applications and problems to solve
- Comparison of Bootstrap, Bayesian and classical statistics results
Day 8 - Wednesday (Risk Analysts)
- Analyzing and using data:
- Difficulties with microbiological data and their reporting
- Checking quality and appropriateness
- How to accept and reject different data sets
- Spotting the traps and filling the gaps in reported data
- Fitting distributions to data
- What it means to fit a distribution
- Assessing validity of data
- First and second order distribution fitting
- Parametric and non-parametric distributions
- Likelihood estimating, Bootstrapping, other methods
- Problems to solve
- Past mistakes
Day 9 - Thursday (Risk Analysts)
- Farm-to-fork (F2F) risk assessments
- The history and application of international guidelines
- Review of past farm-to-fork guidelines
- The mathematical and data needs of a F2F risk assessment
- Predictive microbiology
- Probability models used
- Strength and weaknesses
- Dose-response modeling
- Probability models used
- Strength and weaknesses
- Alternatives to a farm-to-fork approach
Day 10 - Friday (Risk Analysts)
- Presenting risk analysis results to address decision question
- Addressing model uncertainty
- Identifying errors in risk analyses
- Review of material and debate
- Address participants modeling issues
- Spill-over time
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