This EMBO Practical course will introduce 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.

In this course, participants will learn about compartmental models, such as Susceptible-Infectious-Recovered (SIR) models, how to adapt them to a pathogen of their interest, and the differences between stochastic and deterministic models. Taking this further, participants will apply these models to epidemic settings in order to predict changes in case numbers and disease severity.

Additionally, the participants will learn how to implement and adapt genomics-informed models for pathogen evolution, and they will learn how to make use of genomics to create timed phylogenies and reconstruct ancestral states.

Finally, bringing together transmission modelling and genomic data, participants will learn about 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 aimed at early-stage researchers and others who are working in the fields of infectious diseases or pathogen evolution and will use mathematical modelling, and for professionals who carry out this type of research in policy making, charities or public health settings.

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

Introduction to the Unix environment – https://swcarpentry.github.io/shell-novice/index.html
Introduction to R – https://swcarpentry.github.io/r-novice-inflammation/
Introduction to Biostatistics – https://www.ebi.ac.uk/training/online/courses/biostatistics-introductio…

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 of pathogen (sequence) data and mathematical models

During this course you will learn about:

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