Research in Epidemic models
The understanding and control of infectious diseases is of considerable importance to society. How a disease spreads and/or how infectious a disease is, has tremendous implications upon the health and wealth of a community.
Dr Neal is therefore interested in both the probabilistic and statistical analysis of infectious diseases. From a probabilistic perspective, we look to answer questions as:
- What is the probability that a disease takes hold within a community?
- How many individuals are ultimately infected of the disease?
This involves developing novel probabilistic techniques to answer these questions for realistic population models such as the household and random graph models. Dr Neal is working with Professor Frank Ball of Nottingham University on such models.
Alternatively, having observed an epidemic we can propose a model for the disease spread and estimate the model parameters. However, often the disease data are "incomplete" and novel statistical methods, in particular, Markov Chain Monte Carlo (MCMC) are required to analyse the data. We aim to answer questions concerning the adequacy of the model and the predictive capabilities of the model for the future epidemic outbreaks.
In addition Dr Neal is interested in developing MCMC algorithms for a range of statistical problems and more recently ABC (Approximate Bayesian Computation).