Bayesian Spatio-Temporal Modeling and Hotspot Mapping of Malaria Risk in Ghana

Abstract

Purpose: This study developed a Bayesian hierarchical spatio–temporal modeling framework to analyze factors and trends in malaria risk across Ghana′s 16 administrative regions from 2020 to 2024. The aim was to identify statistically significant areas with elevated or persistent malaria risk, to inform targeted intervention planning and support the National Malaria Elimination Program. Methods: This study utilized malaria incidence data from the Ghana Health Service′s District Health Information Management System–II covering the years 2020 to 2024. Meteorological data were sourced from the Visual Crossing Weather Data, and regional population estimates were obtained from the Ghana Statistical Service. To analyze the data, a Bayesian hierarchical spatiotemporal model with a Negative Binomial (NB) likelihood was implemented using Integrated Nested Laplace Approximation to account for overdispersion. The model included Conditional Autoregressive priors for structured spatial effects, first–order random walk priors for temporal dependence, and spatio-temporal interaction terms. Additionally, Local Indicators of Spatial Association (LISA) analysis with 999 conditional permutations was conducted to identify statistically significant spatial clusters, including high–high hotspots and low-low cold spots. Results: The NB model significantly outperformed the Poisson model, leading to a reduction in the dispersion statistic from 9,227.55 to 1.11. Humidity with a 1–month lag showed the strongest positive association with malaria risk, while the ultraviolet index had the greatest protective effect. Predictive relative risk maps identified persistent high–risk clusters in the northern and northwestern regions, specifically Upper West, Upper East, Bono, Ahafo, and Western North. LISA analysis indicated that Bono–Ahafo has been a stable high–high cluster from 2020 to 2023, while Ashanti has remained a consistent low-high anomaly. Additionally, Greater Accra and Central regions formed a significant low-low cluster in 2024. Conclusion: The Bayesian hierarchical spatio-temporal framework effectively characterized the complex transmission dynamics of malaria in Ghana. It revealed significant spatial dependence, temporal correlation, and interactions between these factors. By identifying persistent high–risk clusters and statistically significant spatial associations, this framework provides essential evidence to guide resource allocation. These findings support Ghana′s National Malaria Elimination Program Strategic Plan (2024–2028) by enabling targeted interventions in hotspots and optimizing the use of limited resources to sustain progress in low–transmission areas.