Mathematical Modelling in Infectious Disease Epidemiology

Knowledge:

• Compartmental, ODE models of disease dynamics, understanding of the asymptotic behaviour of different models.
• Derivation of basic properties of models.
• Stochastic models, including Reed-Frost models, Branching Processes and the stochastic SIR model.
• Maximum likelihood and Bayesian approaches to inferring epidemiological parameters.

Skills:

• Explain insights obtained from different infectious disease models
• Solve systems of ODEs and simulate stochastic models of epidemics in R
• Perform model fitting for completed and ongoing epidemics.

Competences:

• Design compartmental models to model different epidemic scenarios and interventions and be able to study the properties of these models.
• Fit different models to real data from infectious disease outbreaks and evaluate the outputs.
• Simulate disease outbreaks from stochastic models using different simulation methods.