Methods for infectious disease modelling using genomics

Discover the concepts, methods, and tools for carrying out infectious disease modelling, as well as for developing models further by leveraging the power of genomics. 

Modelling infectious diseases can help to understand the mechanisms of pathogen evolution and predict the development of epidemics. This had an immense impact on the COVID-19 pandemic and is crucial for the general surveillance of pathogens. 

During the course, you will learn about compartmental models, such as Susceptible-Infectious-Recovered (SIR) models, how to adapt them to a pathogen of your interest, and the differences between stochastic and deterministic models. You'll then apply these models to epidemic settings to predict changes in case numbers and disease severity. 

Additionally, you'll implement and adapt genomics-informed models for pathogen evolution, including multi-strain and Wright-Fisher models, and learn how to combine population genetics with epidemic models. You'll also make use of genomics to create timed phylogenies and reconstruct ancestral states. 

Finally, bringing together transmission modelling and genomic data, we'll study phylodynamic models and how this can help to detect changes in case numbers and pathogenicity earlier and more reliably than with transmission models alone. 

Who is this course for?

This course is for you if you are an early-stage researcher working in the fields of infectious diseases or pathogen evolution, using mathematical modelling. It is also aimed at professionals who carry out this type of research in policy making, charities, or public health settings. 

Basic knowledge of R, the command line, and biostatistics will be essential. We recommend following these or other similar free tutorials prior to the course:

• Introduction to the Unix environment
• Introduction to R 
• Introduction to Biostatistics  

What will I learn?

Course content

During this course, you will learn about: 

• Introduction to infectious disease modelling
• Stochastic and deterministic compartmental models using the programming language STAN
• Models for pathogen evolution using the programming language odin.dust
• Timed phylogenies and ancestral state reconstruction using Beast2
• Introduction to the EMBL-EBI resources: BioModels and Pathogens Portal
• Phylodynamics – combining phylogenetic and transmission models to predict case numbers and pathogenicity  

Learning outcomes

After the course, you should be able to:

• Implement, evaluate, and adapt deterministic and stochastic transmission models 
• Apply modelling tools to sequence data from epidemics
• Evaluate model fits and forecasts
• Adapt models for pathogen evolution
• Combine phylogenetics and transmission models into phylodynamic models
• Demonstrate a working knowledge of open access data resources for pathogen (sequence) data and mathematical models

This course opens applications on 30 March 2027. Click to register your interest using the button on the right-hand side of this course website to hear as the course develops. 

Whilst you wait, we encourage you to read our support page for advice before starting your application.

Once opened, you will need to submit all your documents during the application process by 23:59 UK time on 27 June 2027.

You will then be informed of the status of your application (successful, waiting list, unsuccessful) by 9 July 2027.  

If you have any questions regarding the application process, please contact your Event Organiser, Charlotte Courtney.

The EMBL-EBI Training team is committed to making our events inclusive and accessible to all participants, whether attending onsite or virtually. Please read our dedicated support page to find out how we may be able to support you with any accessibility requirements.