Identifying Malaria Hotspots Regions in Ghana Using Bayesian Spatial and Spatiotemporal Models

Area of Science:

• Epidemiology
• Public Health
• Spatial Analysis

Background:

• Malaria poses a significant public health challenge in Ghana, necessitating accurate risk assessment and mapping.
• Understanding geographical variations in malaria risk is crucial for resource allocation and targeted interventions.
• Identifying disease hotspots and controllable risk predictors is essential for effective malaria control.

Purpose of the Study:

• To identify regions with elevated malaria risk in Ghana.
• To map malaria risk hotspots for targeted public health interventions.
• To identify controllable predictors of malaria risk.

Main Methods:

• Utilized laboratory-confirmed malaria case data from 2015-2021.
• Employed Bayesian spatial and spatiotemporal models to analyze malaria risk patterns.
• Mapped regional malaria risk and identified hotspots using correlated and uncorrelated structures.

Main Results:

• Both spatial and spatiotemporal models indicated increased malaria risk in several regions, including North East, Upper West, Bono East, Central, and Ahafo.
• Identified specific regions as malaria risk hotspots.
• Found substantial regional variations in malaria risk, with no statistically significant influence from climatic or economic factors.

Conclusions:

• Malaria risk is clustered and varies significantly across Ghana's regions.
• Several regions are identified as malaria hotspots requiring focused control efforts.
• Climate and economic factors did not show a significant influence on malaria risk in this study, suggesting a need for further investigation into other drivers.