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, Vounatsou P. Long-term air pollution exposure and COVID-19 case-severity: an analysis of individual-level data from Switzerland. Environ Res. 2022;216(Pt 1):114481. DOI: 10.1016/j.envres.2022.114481
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
Beloconi A. Bayesian spatio-temporal modelling of air pollution burden in Europe: integrating data from monitors, satellites and chemical transport models. Basel: Univ. Basel, 2020. Faculty of Science. PhD Thesis, University of Basel, Faculty of Science