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

Infectious Diseases & Immunity 2024年第2期4卷 69-78页

作者： Abdul-Karim Iddrisu Dominic Otoo Gordon Hinneh Yakubu Dekongmene Kanyiri Kanimam Yaaba Samuel Cecilia Kubio francis balungnaa dhari veriegh Department of Mathematics and Statistics University of Energy and Natural ResourcesSunyani 233Ghana Department of Statistical Service Ghana Statistical ServiceBono RegionSunyani 233Ghana Department of Mathematics and ICT Gambaga College of EducationSunyani 233Ghana Human Resources Division University of Energy and Natural ResourcesSunyani 233Ghana Department of Biological Science University of Energy and Natural ResourcesSunyani 233Ghana.

Background:Malaria remains a significant public health concern in Ghana,with varying risk levels across different geographical *** affects millions of people each year and imposes a substantial burden on the health care system and *** risk estimation and mapping are crucial for effectively allocating resources and implementing targeted interventions to identify regions with disease *** study aimed to identify regions exhibiting elevated malaria risk so that public health interventions can be implemented,and to identify malaria risk predictors that can be controlled as part of public health interventions for malaria ***:The data on laboratory-confirmed malaria cases from 2015 to 2021 were obtained from the Ghana Health Service and Ghana Statistical *** studied the spatial and spatiotemporal patterns of the relative risk of malaria using Bayesian spatial and spatiotemporal *** malaria risk for each region was mapped to visually identify regions with malaria *** and heterogeneity of disease risks were established using correlated and uncorrelated structures via the conditional autoregressive and Gaussian models,*** estimates from the marginal posterior distribution were estimated within the Integrated Nested Laplace Approximation using the R ***:The spatial model indicated an increased risk of malaria in the North East,Bono East,Ahafo,Central,Upper West,Brong Ahafo,Ashanti,and Eastern *** spatiotemporal model results highlighted an elevated malaria risk in the North East,Upper West,Upper East,Savannah,Bono East,Central,Bono,and Ahafo *** spatial and spatiotemporal models identified the North East,Upper West,Bono East,Central,and Ahafo Regions as hotspots for malaria *** variations in risk were evident across regions(H=104.9,P<0.001).Although climatic and economic factors influenced malaria infection,statistical significance was not

关键词： Malaria Disease hotspot Bayesian modeling Conditional auto-regressive Integrated Nested Laplace Approximation Spatial and spatiotemporal models

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