Bayesian Modelling and Analysis

The Bayesian Modelling and Analysis group conducts research in advanced statistical modelling and Bayesian computation to support data-driven insights in public and global health. Our research focuses on spatio-temporal modelling and forecasting, and the modelling of multivariate health data, including the prediction and quantification of model uncertainty.

Spatio-temporal modelling is a key area of research for our group. We develop and apply Bayesian statistical methods for disease mapping, risk assessment and forecasting, with applications in malaria, neglected infectious diseases, cancer and mortality. These models generate spatially explicit estimates of disease exposure and burden, identify determinants of space–time disease distribution, and project future disease dynamics to inform public health decision-making.

Latest publications

All Publications

Angelakis A et al. Assessing the impact of climate and control interventions on spatio-temporal malaria dynamics using a stochastic metapopulation model. PLoS Comput Biol. 2026;22(3):e1014004. DOI: 10.1371/journal.pcbi.1014004

Ataba E et al. Spatial distribution of urogenital schistosomiasis in school-aged children in Togo: an oversampling survey in three districts in 2022. Parasit Vectors. 2026(in press). DOI: 10.1186/s13071-026-07357-6

Traoré N, Zabré P, Millogo O, Sié A, Vounatsou P. Assessing the role of interventions and climate on malaria mortality among children under five years of age: insights from two decades of data from the Health Demographic Surveillance System of Nouna, Burkina Faso. J Glob Health. 2026;16:04080. DOI: 10.7189/jogh.16.04080

Alidou S et al. Risk factors associated with urogenital schistosomiasis: a multilevel assessment approach using an Oversampling Schistosomiasis Survey (SOS) community-based, Plateaux region, Togo 2022. BMJ Public Health. 2025;3(1):e001304. DOI: 10.1136/bmjph-2024-001304

Angelakis A, Nyawanda B.O, Vounatsou P. Modeling sparse Rift Valley fever incidence data: a Bayesian perspective on zero-inflated self-exciting and autoregressive models. BMC Infect Dis. 2025;25:1221. DOI: 10.1186/s12879-025-11506-0

Keita B.M et al. Piloting the Schistosomiasis Practical and Precision Assessment approach in five health districts of the N'zérékoré region, Republic of Guinea. PLoS Negl Trop Dis. 2025;19(10):e0013413. DOI: 10.1371/journal.pntd.0013413