Applied statistician with expertise on Bayesian spatiotemporal models, R-INLA and applications on public health, climate change and childhood health.
In this research, I will develop a framework of analysis using spatiotemporal models to evaluate climate-related disease burden. The methodology of this project builds on Bayesian spatiotemporal models for risk assessment and quantification of associated economic costs, hence model and parameter uncertainty will be naturally propagated across the proposed framework. I will use this approach to estimate the temperature-related respiratory health burden in the UK and communicate results to stakeholders and public health experts to implement relevant public health strategies. This is an interdisciplenariy project with several national and international collaborations including Prof Blangiardo (main supervisor), Prof Baio and Dr Mineli (co-supervisors) but also with Dr Gasparrini, Prof Schuhmacher, Dr Bhatt, Dr Ballester and Dr Vicedo-Cabrera.
I did a 3-month internship at the CEMSE (Computer, Electrical and Mathematical Science and Engineering) division of the King Abdullah University of Science and Technology under the supervision of Prof Haavard Rue. There I worked with him on conducting a simulation study comparing the log-Gaussian Cox processes and the Besag-York-Mollie models for disease mapping and also gained expertise on R-INLA.
My thesis was entitled "Analysis of spatial clustering of childhood cancers" and it was the first systematic investigation of the spatial clustering of childhood cancers in Switzerland. The first part of my PhD was focused on global clustering and cluster detection, whereas the second part on disease mapping and spatial regression. During the second part of my PhD I gained expertise on models commonly used in spatial epidemiology, like log-Gaussian Cox processes and the Besag-York-Mollie model. For the PhD-related publications see bellow. In adition, during my PhD I was involved in a couple of other projects: