I obtained a PhD in Statistics from Heriot Watt University, Edinburgh following a Masters in Mathematics at the African Institute for Mathematical Sciences in Cape Town. I joined EPIC in March 2012 to develop and apply novel methods for parameter estimation and model selection in stochastic epidemic process models. These tools are applicable to a wide range of diseases and allow the development of models that are correctly structured (set up) and parameterised in a way that acknowledges uncertainty in our knowledge about them. This uncertainty derives from the inevitably limited nature of available data describing the spread of pathogens in the real world, and the stochastic (i.e. structured but somewhat random) nature of the processes involved in the spread of disease. In collaboration with other EPIC scientists I am currently working on the practical application of these ideas to a number of livestock disease issues. For example, to estimate disease transmission within herds using routine diagnostic testing data. I am also developing methods to reliably extract information from small historic outbreaks of (re)emerging diseases to inform risk assessment of between farm spread in potential future incursions e.g. for Classical Swine Fever.
Small outbreaks provide limited data but may be precursors to much larger outbreaks and it is therefore important, yet statistically challenging, to extract the maximum information from such data sets. We are currently extending these ideas to using data from the early stages of an ongoing outbreak. My overall goal within EPIC is to improve statistical methods for model choice and parameter inference in order to ensure that better models are available to inform advice on livestock disease control.
Vulnerability of the British swine industry to classical swine fever. T Porphyre, C Correia-Gomes, ME Chase-Topping, K Gamado, HK Auty, I Hutchinson, A Reeves, GJ Gunn, MEJ Woolhouse
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