Statistical modelling of spatio-temporal signals: from the Afghan conflict to BOLD-MRI in rats' kidneys

Spatio-temporal data is ubiquitous in most application domains, from social sciences to medicine and environmental sciences. In this talk I will describe how we have tackled two different modelling problems from two very different domain applications. In the first part of the talk, I will review dynamical spatio-temporal statistical modelling, and show how it could be applied to model and predict the behaviour of the Afghan conflict from the Wikileaks data set. I will then move to a biomedical application where we hoped to use similar methodologies to describe the dynamics of blood oxygenation in rat kidneys following administration of drugs. Unfortunately, in this case the data was not sufficient to parametrize fully a dynamical model, hence we resorted to an empirical model which was still able to reveal interesting biological behaviours in response to treatments. References: - Zammit Mangion et al, Point Process Modelling of the Afghan War Diary, Proceedings of the National Academy of Sciences of the USA (PNAS), (2012) - Menzies et al, An anatomically-unbiased approach for analysis of renal BOLD magnetic resonance images, American Journal of Physiology (2013)