My Ph.D. was in interdisciplinary mathematics, supervised by Thomas House & Michael Tildesley, and was primarily focused on mathematical epidemiology and modelling zoonotic influenza.

This project comprised two main bodies of work, both using a combination of mathematical modelling, statistical fitting to data and computer simulations, with a particular emphasis on Monte Carlo Markov Chain (MCMC) simulations for the determination of model parameters.

First, it is vital to devise new models incorporating zoonotic transmission, and establish region-specific factors enabling both continued transmission within poultry and spillover across the poultry-human divide. We tackled this problem for H5N1 in Bangladesh. We constructed a set of candidate transmission models, with a zoonotic transmission component, parameterised with a Bayesian inference scheme using data from two H5N1 outbreaks in the Dhaka region. Applied at two distinct spatial scales, we uncovered the model considerations that best capture the size and spatial distribution of reported cases. These findings can then aid assessing the predicted impact of interventions designed to reduce H5N1 transmission.

Second, we considered the emergence of influenza strains with pandemic potential from a global viewpoint. Using a Bayesian model selection approach we compared plausible model hypotheses regarding the mechanisms driving influenza pandemic occurrences. Analysing the time periods between probable influenza pandemics since 1700, it is shown the weight of evidence favours influenza pandemic emergence being history-dependent, rather than a memoryless process. Conclusions drawn may allow quantitative predictions for number of expected pandemics in a specified time period to be made.

A downloadable PDF of my thesis entitled "Mathematical modelling approaches for spreading processes : zoonotic influenza and social contagion" can be found here.

Relevant Publications

Peer-reviewed papers