I originally trained as a vet, but was less interested in treating individual animals, than in improving animal health and welfare in the population as a whole. I therefore moved sideways into veterinary epidemiology. In my present role I use a number of statistical and modelling techniques to make inferences from animal test and disease data, and determine the effect of various test and control strategies.
In a previous EPIC project, I have looked at data on E. coli O157 in calves, liver fluke in cattle, Bovine Viral Diarrhoea (BVD), sheep scab and bovine TB.
In the future I will be looking at using a number of statistical methods to see if we can categorise farms using their animal movement data (SubTopic 2.1.2). We will also be looking to see if these cluster into groups, and whether an individual farm characteristics alter such that the cluster into which it associates changes over time. This knowledge is important when deciding which farms to examine for disease surveillance or control, and whether we can just keep a list of farms, or need to update this regularly.
I will also be looking at refining statistical methods to better support active surveillance (SubTopic 3.2.2). Initially this will look at methods to account for imprecision in diagnostic tests when calculating the risk that we attribute to certain factors using logistic regression. At present this is largely ignored, which leads to lower estimates than the real effect. Later I may be looking at modeling the number of cases in outbreaks in small populations, such as farms, to give better estimates of the transmission dynamics of infectious disease agents.
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