Infectious Disease Modelling
This module aims to provide an introduction to the fundamentals of infectious disease modelling and the use of models to support policy decisions for the control of infectious diseases. Simple transmission models will be introduced along with the basic methodology to estimate them from surveillance data. From this foundation, more complex models will be explored with their importance illustrated using specific examples linked to the needs of policy makers.
Module Learning Outcomes:
By the end of the module, students should be able to:
- Use simple SIR transmission models to illustrate and derive the basic reproductive ratio R0 and the herd immunity threshold
- Design and interpret model structures for disease dynamic based on the life-history of infection
- Understand the difference between stochastic and deterministic models, how to simulate (solve) them and when they should be used
- Estimate the basic reproduction ratio using simple SIR models from outbreak and serological data
- Appreciate the relevance of social networks for the transmission of infectious disease, methods of data collection and implications for dynamics
- Estimate simple models using MCMC and develop an appreciation of the more advanced methods for estimating dynamic models
- Appreciate how models are used to support policy for control of infectious disease
Pre-requisites: Principles of Epidemiology, Principles of Biostatistics (or Statistics for HDS), Applied Data analysis. A good understanding of: infectious disease epidemiology; statistical distributions; statistical modelling and methods of estimation, both frequentist and Bayesian.
Teaching Strategy: A combination of lectures, group practical work, problem-based activities and structured reading.
Assessment: Assessed take-home practical involving analysis of dataset(s) and building a simple mathematical model in R.
Module Length: 4 days