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.

Plass D et al. Estimating the burden of disease due to lead, PFAS, phthalates, cadmium, pyrethroids and bisphenol A using HBM4EU data – test of feasibility and first results for selected countries: European Topic Centre on Human Health and the Environment, 2024

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

Beloconi A, Vounatsou P. Revised EU and WHO air quality thresholds: where does Europe stand?. Atmos Environ (1994). 2023;314:120110. DOI: 10.1016/j.atmosenv.2023.120110

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