The 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.