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.

Latest Publications

All Publications

Isaiah P.M, Nyawanda B, Okoyo C, Oloo J.O, Steinmann P. Schistosomiasis status and health impact in preschool-aged children in hard-to-reach areas and populations of Homa Bay County, Kenya. Acta Trop. 2025;261(107511). DOI: 10.1016/j.actatropica.2024.107511

Plass D et al. Estimating the environmental burden of disease resulting from exposure to chemicals in European countries - potentials and challenges revealed in selected case studies. Environ Res. 2025(in press). DOI: 10.1016/j.envres.2025.120828

Grolimund C.M et al. Modeling transmission mechanism to infer treatment efficacy of different drugs and combination therapy against Trichuris trichiura. Sci Rep. 2024;14(1):23543. DOI: 10.1038/s41598-024-73164-7

Kim J, Vounatsou P, Chun B.C. Changes in seasonality and sex ratio of scrub typhus: a case study of South Korea from 2003 to 2019 based on wavelet transform analysis. BMC Infect Dis. 2024;24:1066. DOI: 10.1186/s12879-024-09858-0

Massoda Tonye S.G, Wounang R, Kouambeng C, Vounatsou P. The influence of jittering DHS cluster locations on geostatistical model-based estimates of malaria risk in Cameroon. Parasite Epidemiol Control. 2024;27:e00397. DOI: 10.1016/j.parepi.2024.e00397