This is an onsite (in person) class, held over two days, and is typically offered during professional conferences.
This hands-on workshop will cover essential risk modeling methods available in animal health and food safety, particularly emphasizing microbial and trade risk analysis applications. The course is taught using the R statistical language and EpiX Analytics’ reference software ModelAssist®, which are both free of cost.
Course participants will learn simulation and calculation methods in the R statistical language. Participants will also learn how to select appropriate distributions, use data and expert opinion, and avoid common modeling mistakes. Example models from the instructors, including food safety risk assessments, and animal health risk assessments will be used to reinforce the methods and best modeling practices discussed during the class. Case studies will be discussed with participants to illustrate the main steps in putting together a risk assessment model. The topic of the case studies will be selected based on participants’ main interests.
In addition, to optimize learning during the course, registration to this class will include a one-week online module on foundational methods to use R for risk modeling. Course participants will be able to take this class prior to the onsite workshop at the time and pace that is most convenient to them, since it is a fully asynchronous learning module.
This 2-day course will be delivered in English, but instructors can also answer questions and communicate in Spanish, French, Portuguese, and Italian.
EpiX Analytics have been delivering this course to workshop participants for over 20 years while regularly updating its contents to reflect changes in modeling methods.
Testimonials from previous course participants:
“This class is the best class that I ever took. We learned not only conceptual modeling but also programming in R”
“The example problems were challenging/probing and particularly suited to the types of analysis we are expected to conduct in our work. Excellent practical instruction, built on strong statistical theory.”
1. Introduction and Foundational Concepts
(Lecture) Brief introduction to food safety and animal health risk modeling
• Codex & OIE frameworks
• Link between epidemiology and risk assessment
(Lecture and exercises) Introduction to risk modeling
• Commonly used probability distributions
• Presenting and interpreting risk analysis in a coherent way
(Lecture and computer lab) Introduction to risk modeling
• Using the R statistical environment for probability calculations and Monte Carlo simulation (online module refresher)
(Lecture, computer lab, discussion) Basic stochastic processes
• Population and individual state, imperfect diagnostic tests
• Modeling rates with uncertainty
• Combining Poisson and Binomial in a risk assessment
(Computer lab) Exercises – stochastic processes
(Lecture & discussion) Case study
2. Applications and working with data
(Computer lab) Basic stochastic processes
• Further exercises
(Lecture and computer lab) Using data and expert opinion for risk analysis
• Incorporating expert opinion
• Fitting distributions to data
(Computer lab) Data fitting and expert opinion – further exercises
(Lecture and discussion) Case study
(Lecture and discussion) Wrap up:
• Risk assessment checklist
• Modeling epistemic uncertainty – primer and mistakes to avoid
This course is well suited to anyone that needs to conduct, present, or critique quantitative risk analyses in food safety, animal health, or One health, and to professionals providing inputs to risk analyses or those who need to interpret or use risk analysis results. Also, this workshop is ideal for people who have experience in risk modeling using spreadsheets or other modeling languages, but want to learn how to use the more flexible modeling environment provided by R.
No prior R or simulation modeling knowledge is required for registration. Participants will be encouraged to complete an online module ‘Introduction to R for risk modeling’ that will be made available to them at no additional cost. Links to other teaching material on the fundamentals of R programming will also be provided, such as Kelly Black’s R tutorial.
Training material and software:
