The Bayesian Modelling and Analysis group carries out research in advanced statistical modelling and Bayesian computation. The main thematic areas of our research are spatio-temporal modelling and forecasting, modelling of multivariate data, including prediction of model uncertainty.
Spatio-temporal modelling is a focal research area of the group. It involves developing data-driven statistical methods and applications for malaria, neglected infectious diseases, cancer and mortality to obtain spatially explicit estimates of disease exposures and its associated burden, assess determinants of the space-time disease distribution and project disease dynamics.
Latest PublicationsAll Publications
Beloconi A, Probst-Hensch N, Vounatsou P. Spatio-temporal modelling of changes in air pollution exposure associated to the COVID-19 lockdown measures across Europe. Sci Total Environ. 2021;787:147607. DOI: 10.1016/j.scitotenv.2021.147607
Beloconi A, Vounatsou P. Substantial reduction in particulate matter air pollution across Europe during 2006-2019: a spatiotemporal modeling analysis. Environ Sci Technol. 2021;55(22):15505-15518. DOI: 10.1021/acs.est.1c03748
Beloconi A, Vounatsou P. Bayesian geostatistical modelling of high-resolution NO2 exposure in Europe combining data from monitors, satellites and chemical transport models. Environ Int. 2020;138:105578. DOI: 10.1016/j.envint.2020.105578
Dlamini S.N, Beloconi A, Mabaso S, Vounatsou P, Impouma B, Fall I.S. Review of remotely sensed data products for disease mapping and epidemiology. Remote Sensing Applications: Society and Environment. 2019;14:108-118. DOI: 10.1016/j.rsase.2019.02.005
Beloconi A, Chrysoulakis N, Lyapustin A, Utzinger J, Vounatsou P. Bayesian geostatistical modelling of PM10 and PM2.5 surface level concentrations in Europe using high-resolution satellite-derived products. Environ Int. 2018;121(Pt 1):57-70. DOI: 10.1016/j.envint.2018.08.041