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 et al. Malaria, climate variability, and interventions: modelling transmission dynamics. Sci Rep. 2023;13(1):7367. DOI: 10.1038/s41598-023-33868-8
Beloconi A, Vounatsou P. Long-term air pollution exposure and COVID-19 case-severity: an analysis of individual-level data from Switzerland. Environ Res. 2023;216(Pt 1):114481. DOI: 10.1016/j.envres.2022.114481
Nyawanda B.O et al. The relative effect of climate variability on malaria incidence after scale-up of interventions in western Kenya: a time-series analysis of monthly incidence data from 2008 to 2019. Parasite Epidemiol Control. 2023;21:e00297. DOI: 10.1016/j.parepi.2023.e00297
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