Joint Spatio-Temporal Modelling of Malaria Incidence and Mortality in Kenya

Abstract

Understanding how malaria incidence and mortality vary across space and time is important for strengthening malaria surveillance and supporting evidence-based planning of control interventions. This study examined the joint spatial and temporal patterns of malaria incidence and mortality in Kenya, identified high-risk counties and assessed whether the two outcomes shared common distributions. County-level malaria incidence and mortality data for 2013 to 2024 were analysed using a joint Bayesian hierarchical model based on a spatio-temporal conditional autoregressive framework, which accounted for spatial dependence among neighbouring counties and temporal correlation across successive years. Model performance was compared with a multivariate spatio-temporal modelling framework to evaluate shared and outcome-specific variation. Integrated Nested Laplace Approximation was used for Bayesian inference. Weighted adjacency matrices were constructed, and structured and unstructured spatial effects were incorporated to account for spatial heterogeneity, temporal dependence and unexplained variability. The results showed persistent spatial clustering of both outcomes throughout the study period. Counties in the lake-endemic region, particularly Kisumu, Siaya and Busia, consistently had higher spatial risks for malaria incidence and mortality than most other counties. The spatial distributions of incidence and mortality were positively associated, indicating that counties with higher incidence tended to have higher mortality. Temporal patterns differed between the outcomes, with incidence showing broader fluctuation and mortality showing less pronounced change. The findings indicate that joint spatio-temporal modelling can support malaria surveillance and guide spatially targeted control interventions in Kenya.