One of the principal characteristics of the group s work is the close interaction between modelling, experimentation and statistical inference to test models and to estimate epidemiological parameters.
We are particularly interested in the role of error structure in epidemic dynamics and in the estimation of variance and higher moments. Examples of recent and current work include:
- Markov chain Monte Carlo techniques to estimate parameters for spatio-temporal models of epidemics;
- Bayesian methods to estimate parameters for replicate epidemics with treatment effects;
- Bayesian methods to estimate parameters for stochastic compartmental models;
- Likelihood methods for estimation of a range of epidemiological parameters.