My primary research interest is the statistical modelling of dependence and extremes, motivated by various humanitarian and environmental issues including public health and disaster risk.
With the rest of the Disaster Analytics for Society team and the wider research community at the Asian School of the Environment and the Earth Observatory of Singapore, I am working on developing statistical methods to improve hazard risk estimation with particular focus on addressing spatial correlation and ordered data. Prof. David Lallemant and I are also collaborating with Prof. Almut Veraart on modelling connected extremes in Southeast Asia, under the Imperial – Nanyang Technological University collaboration grant.
Mapping for public health
While I was at the Malaria Atlas Project (MAP) in Oxford, I was part of the Global Malaria Epidemiology team which developed spatio-temporal modelling infrastructure to estimate global treating seeking rates as well as predict transmission and burden in malaria endemic countries. Our work continues to contribute to the Global Burden of Disease project at the Institute of Health Metrics and Evaluation as well as World Malaria Report produced by the World Health Organisation. Two Lancet papers outline our modelling strategies and results for Plasmodium falciparum and Plasmodium vivax.
In line with our efforts to support control agencies in their decisions, I led a project to model and map malaria seasonality patterns. This research is in collaboration with colleagues at Imperial College London, the National Malaria Control Programme in Madagascar and Institut Pasteur de Madagascar.
During my Ph.D., I studied different spatial and spatio-temporal models which are all centred on the relatively new modelling framework of ambit fields. Without equations, one may describe an ambit field as a random field (that is, random variables indexed by time and space) where the major stochastic part is a stochastic integral. By choosing the kernel function, the shape of the ambit set (i.e. the region over which we integrate), the stochastic volatility term (if required) and the Lévy basis (our integrator), we can vary the characteristics of the model. In many cases, ambit fields can be viewed as solutions to stochastic partial differential equations (SPDEs). Studying ambit fields directly is an alternative approach to constructing SPDEs and finding their solutions.
Recent interest in ambit fields started from the modelling of energy dissipation characteristics in turbulence. It has since been used in for example, financial modelling and tumour growth modelling. There is ongoing research on both the probabilistic and statistical aspects of ambit fields and its sub-models.
My PhD thesis consists of three main projects. Each seeks to develop the theory, simulation and inference methods for particular subclasses of ambit fields. The latter in turn highlight different aspects of the more general ambit field model.