by | Dec 13, 2023

Pre-Meeting Workshops

Introduction to applied phylodynamic methods for genomic surveillance of infectious diseases

During the last two decades, the rapid growth of pathogens’ genetic data and computational resources increased the applications of phylodynamic methods in animal and human disease surveillance. Using a single Bayesian statistical framework, these methods can account for uncertainties and uniquely integrate complex epidemiological and evolutionary processes in populations. Therefore, this innovative quantitative integration improved disease investigation by untangling novel epidemiological questions about the evolutionary history, spatiotemporal origins, within and between-host transmission, and environmental risk factors for rapidly evolving pathogens. These approaches will provide a robust platform for guiding the allocation of resources within a surveillance system, for example targeting emerging strains with higher evolutionary rates or hosts at high risk of generating new strains, which subsequently will reduce the economic costs of sampling, control, and prevention activities. Phylodynamic methods are implemented in many open-source statistical software packages, while the most popular user-friendly software package is formally known as Bayesian evolutionary analysis by sampling tree (BEAST). In this workshop, we will demonstrate basic principles for building a phylodynamic analytical pipeline, illustrate examples of the impact of gene segment and prior selection on posterior evolutionary inferences, and highlight the prospects of the methods in improving infectious disease surveillance.

 Workshop organizers

Dr. Moh. A. Alkhamis (DVM, MPVM, PhD). College of Public Health, Kuwait University, Kuwait.  

Dr. Ruwini Rupasinghe. (DVM, MPVM, PhDc), college of veterinary medicine, University of California Davis


  • Standard fee – $800 AUD
  • Student fee – $500 AUD

Dates: 9-10 November 2024

Critical Appraisal of Epidemiological Animal Studies with a purpose-built tool

 The appraisal of evidence from observational studies on animal populations, e.g. in the framework of meta-analysis, weight of evidence assessment or risk assessment, requires judgments on the reliability of results from each individual primary study. Available assessments tools/instruments for such evaluations of quality of observational studies in human populations lack important aspects specific for studies on animal populations.

Against this background, at the German Federal Institute for Risk Assessment (BfR) in cooperation with the European Food Safety Authority (EFSA) and experts in the area, we have developed a tool for rapid assessment of Risk of Bias in observational epidemiological veterinary studies (raRoB-vet). The tool will be freely available and is designed for the scientific community to facilitate a transparent and valid critical appraisal of observational animal studies.

The teaching method will be mix of concept presentations and supervised group exercise.

By the end of the workshop, participants will have an overview of the definition of bias, biases associated with study designs, quality of published studies and critical appraisal of epidemiological studies on animal populations. They will also gain hands-on experience with the critical appraisal of a sample study using the freely available raRoB-vet tool.

Workshop organizers:

Narges Ghoreishi

Matthias Greiner


  • Standard fee – $400 AUD
  • Student fee – $200 AUD

Dates: 10 November 2024 

Introduction to Modeling Animal Health and Food Safety Risks Using R

This two-day workshop covers essential risk modeling methods available in animal health and food safety (AHFS). It is well suited to anyone that needs to conduct, provide inputs to, present, interpret, use or critique quantitative risk assessments (RA) in AHFS or One health. It is also ideal for people who have experience in risk modeling using spreadsheets or other modeling languages, but want to transition to the more flexible modeling environment that R offers.

Course participants will learn simulation and calculation methods in R, how to select appropriate distributions, use data and expert opinion, and avoid common modeling mistakes. Example models will help reinforcing the methods and best modeling practices taught in the class. Case studies will illustrate the main steps in putting together a RA model. In addition, to optimize learning, registration to this class includes a fully asynchronous online module on foundational methods to use R for risk modeling, which will be made available prior to the workshop. All attendees must bring laptops pre-installed with R (instructions will be provided in advance)

This workshop is an introduction to our Epidemiology and Food Safety Risk Analysis course which we have delivered for over a decade. Compared to our online class, this workshop offers more direct personal guidance, especially during practical exercises.

EpiX will award a student one free place on this workshop. Post-graduate (Master or PhD) students currently working on a risk analysis related project as part of their thesis are encouraged to apply for this award. Send your application to: before April 30, 2024 for consideration. Award recipient will be announced by June 17, 2024.

 Workshop Organizers: Dr. Francisco J. Zagmutt, Dr. Solenne Costard, Dr. Huybert Groenendaal


Standard fee: $1,600 AUD

Student fee: $1,300 AUD

Tea/coffee breaks, snacks and lunches will be provided

Dates: 9-10 November 2024

Approaches to inferential modelling of high dimensional (wide) data: Use of regularisation, variable stability and triangulation to enhance interpretation of results

Inferential epidemiological modelling increasingly involves identification of potentially causal factors from within high dimensional data spaces; examples include genetics, sensor-based data capture and large-scale questionnaires. The selection of ‘significant’ or important variables from within such a high dimensional space is challenging because conventional stepwise selection procedures are known to fail, resulting in inflated coefficients, downward biased errors and over fit models with incorrect variables selected.

The aim of this workshop is to provide practical solutions for inferential modelling of high dimensional data. The objectives are to allow delegates to i) appreciate the problems associated with inferential modelling of high dimensional, wide data including why conventional approaches fail ii) understand and implement methods to overcome problems when modelling high dimensional data, including regularisation techniques (e.g. lasso, elastic net, minimax convex penalty), stability analysis, the ‘knockoff’ filter, the ‘Cox exploratory approach’ and multiple method triangulation. Delegates will have the opportunity to conduct and compare analyses using these methods – all code and datasets will be freely available via an online platform and all code provided in R statistical software.

 Workshop organisers: Prof Martin Green, Dr Naomi Prosser, Dr Jake Thompson & Dr Luke O’Grady,

Fees: $80.00 AUD  

Workshop dates:10 November 2024

Machine learning - applications in epidemiology and animal health

Machine learning and artificial intelligence is rapidly increasing in popularity and utility globally, but is only now starting enter the thinking of practitioners of animal health and veterinary epidemiology.

This course aims to provide animal health professionals and veterinary epidemiologists with a foundational understanding of machine learning concepts, algorithms and techniques applicable to epidemiological data analysis.

The topics covered will include:

  1. What is machine (or statistical) learning?
  2. Models (regression and classification)
  3. Resampling methods
  4. Model selection and regularization
  5. Model evaluation
  6. Other models (non-linear and non additive)
  7. Unsupervised learning
  8. Introduction to neural network models

Our teaching methods will include:

  1. Lectures and practical exercises in R (majority of the course)
  2. Chat GPT to improve R skills
  3. Conceptually extending to Python
  4. Introduction to Kubeflow, Tensorflow, Sage to provide context on the wider machine learning landscape and allow easier application of the concepts learnt in R

Participants should have some familiarity with R and be able to implement linear and logistic regression models in R before undertaking the course.

By the end of the course you will be able analyse a dataset with machine learning techniques in R for example using cross validation/fine tuning so that you can predict outcomes from your populations of interest.

Workshop Organisers – Brendan Cowled (Ausvet), James Torpy (Ausvet) and Tom Brownlie (Ingenum)

Fees: $1600 AUD

Dates: 6-9 November 2024 inclusive

Introduction to practical disease modeling

This 4-day pre-conference workshop targets participants with some experience of coding in R, who are eager to code their own disease spread model. Employing spiral learning, the course accommodates diverse starting points and gradually expands participants’ knowledge of disease modelling. Commencing with fundamental concepts, we will end with hands-on creation of a versatile template for a general class of infectious disease models. Some previous participants in this workshop have gone on to successfully publish their modelling studies.

We combine theory with practical R-based modeling. Key learning objectives include understanding infectious disease principles, utilizing simulation models in varied contexts, constructing dynamic model frameworks in R, validating model characteristics, investigating research questions, and effectively presenting model output.

Attendees must bring laptops pre-installed with requisite software (instructions will be distributed in advance).

Workshop organisers: Professor Matthew Denwood (UCPH), Professor Michael Ward (USYD), Senior Researcher Carsten Kirkeby (UCPH).


Standard fee: $1200 AUD 

Student fee: $1000 AUD

Workshop dates: 5-8 November 2024

Post-Meeting Workshops

Bayesian latent class models for prevalence estimation and diagnostic test evaluation when there is no gold standard

Bayesian methods have been widely applied in veterinary and epidemiological research. In particular, Bayesian latent class variable models (LCM) have been shown to be useful for estimating disease prevalence or diagnostic test accuracy (sensitivity and specificity) in the absence of a perfect reference test (i.e. a gold standard). This 3-day workshop will introduce these methods and illustrate their application with veterinary examples.

The material will be suitable for graduate students or professionals, with an interest in evaluating diagnostic tests or estimating the prevalence of a health condition in the absence of a perfect reference test. The level of the course will be appropriate for graduate students who have already taken introductory courses in epidemiology and statistics and for professional epidemiologists. Participants are not expected to have any previous Bayesian training.

The format of the workshop will include theoretical parts and laboratory sessions giving participants the opportunity to learn how to apply these methods using R and the R2JAGS package. At the end of the workshop, participants will know how to estimate true disease prevalence and diagnostic test accuracy when gold standard tests are not available and for various situations where ≥ two imperfect tests are applied to ≥ one population.

Workshop organizers: Simon Dufour and Juan Carlos Arango Sabogal, Université de Montréal


Standard fee: $1200 AUD

Student fee: $800 AUD

Dates: 18-20 November 2024

Qualitative research methods in veterinary epidemiology

This workshop covers the principles and application of qualitative research methods in veterinary epidemiology. The workshop will be presented in four sessions: 1) a general overview of qualitative research in veterinary epidemiology, 2) elements of study design and approaches to sampling, 3) thematic analysis (a presentation of coding reliability, codebook, and reflexive thematic analysis, with a focus on Braun and Clarke’s reflexive thematic analysis), and 4) approaches for evaluating the quality and reporting of qualitative research. Each session will include a mixture of theoretical presentations and practical activities, giving participants the opportunity to draft an example research plan, practice using NVivo for thematic analysis, and evaluate qualitative research papers.

This workshop is targeted at participants with an interest in applying qualitative research methods in their research. Participants are encouraged to bring examples of their own work to discuss during the workshop sessions, but examples and templates will also be provided. At the end of this workshop, participants should have obtained skills in developing a qualitative research plan, the capacity to rationalise choices made in qualitative study design and methodology, an understanding of the different approaches to thematic analysis and evaluation of qualitative research, and knowledge of available resources for further development of their qualitative research skills.

 Workshop Organisers: Mathilde Paul and Rebecca Hibbard, École Nationale Vétérinaire de Toulouse.


Standard fee:  $1,000 AUD

Student fee: $850 AUD 

Dates: 16-17 November 2024

Building better causal evidence using directed acyclic graphs

Workshop organisers: Victoria Brookes (University of Sydney), Annette O’Connor (Michigan State University), John Morton (Veterinary Epidemiological Consultant, Jemora), Charles Caraguel (University of Adelaide) and Mark Stevenson (University of Melbourne).

This 5-day workshop is for participants who want to develop skills in supporting causal inference with directed acyclic graphs (DAGs). DAGs are becoming popular as powerful tools for variable selection when designing and developing statistical models from observational studies. The workshop comprises lectures, hands-on tutorials, and discussions to build participants’ knowledge and skills as we teach participants how to use DAGs to:

  • represent their research hypotheses, knowledge, and assumptions about a system,
  • identify routes and mechanisms of bias, and
  • identify appropriate variable adjustment strategies for causal analyses.

Our experienced team will guide participants using examples from animal and human health. Software used during course will be freeware (R [code provided] and DAGitty) and all materials will be available during and following the workshop on an open-access website (

By the end of this course, participants will be able to:

  • describe the differences between bias and non-systematic variation in how they affect effect estimates from a study,
  • draw a DAG for an observational study of their choice, and understand how to elicit information to inform the DAG,
  • use a DAG to inform variable adjustment strategies, and describe the implications of inappropriate choices, and
  • use DAGs when critically appraising observational studies.


Standard fee:$1,100 AUD

Student fee: $900 AUD

Workshop days: 18- 22 November 2024