William Hategekimana

Hategekimana William is a doctoral researcher at the University of Burundi Doctoral School, where he is a member of the 7th Cohort of the Interdisciplinary Research Program in Public Health (IRGPH) and conducts his research within the Niyukuri Lab. His doctoral research focuses on the development of advanced Bayesian statistical approaches to understand and predict the spatial and temporal dynamics of malaria risk in Burundi. His doctoral project, entitled "Bayesian Spatio-Temporal Analysis of Malaria Risk in Relation to Climatic Variability and Vector Dynamics in Burundi," investigates how climatic variability and Anopheles vector dynamics interact to shape malaria transmission across the country. The research integrates malaria surveillance data, climatic indicators, environmental variables, and entomological information within a hierarchical Bayesian spatio-temporal modelling framework. His methodological interests include Bayesian statistics, spatial and spatio-temporal statistics, disease mapping, Distributed Lag Non-Linear Models (DLNM), Spatially Varying Coefficient models, BYM2 models, Integrated Nested Laplace Approximation (INLA), INLA-SPDE, and predictive modelling. His work aims to capture nonlinear, delayed, and spatially heterogeneous relationships between climate and malaria risk along Burundi's pronounced altitudinal gradient. Beyond methodological development, his research has a strong translational and public-health orientation. He seeks to transform statistical evidence into actionable tools, including high-resolution malaria risk maps and a Bayesian early warning system (EWS) capable of generating district-level epidemic risk alerts several weeks in advance. The planned system will integrate Bayesian predictions with seasonal climate forecasts through an R Shiny platform to support anticipatory malaria control and evidence-based decision-making. His broader research interests lie at the intersection of applied statistics, epidemiology, infectious disease modelling, climate and health, spatial data science, vector-borne diseases, and health systems research.