Group | Bayesian Modelling and Analysis

Predicted Risk Map of Malaria Parasite among Children under 5 year in Nigeria

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.

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. 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

Beloconi A, Kamarianakis Y, Chrysoulakis N. Estimating urban PM10 and PM2.5 concentrations, based on synergistic MERIS/AATSR aerosol observations, land cover and morphology data. Remote Sens Environ. 2016;172:148-164. DOI: 10.1016/j.rse.2015.10.017