Modelling the spatial varying relationships between socioeconomic inequalities and COVID-19 mortality in the African subregion

Infectious diseases continue to be a global health menace to society. Admittedly, there have been improvements in public health infrastructure and structural response to these diseases, however, a lot more needs to be done to have a firm grip. This study applied the geographically weighted poisson r...

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Bibliographic Details
Published in:Earth science informatics 2024-08, Vol.17 (4), p.2959-2971
Main Authors: Awuah-Mensah, Yaw K., Aidoo, Eric N.
Format: Article
Language:English
Subjects:
COVID-19
Earth and Environmental Science
Earth Sciences
Earth System Sciences
Global health
Heterogeneity
Infectious diseases
Information Systems Applications (incl.Internet)
Modelling
Mortality
Ontology
Poisson density functions
Public health
Regression models
Simulation and Modeling
Social factors
Socioeconomic factors
Socioeconomics
Space Exploration and Astronautics
Space Sciences (including Extraterrestrial Physics
Structural response
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Summary:Infectious diseases continue to be a global health menace to society. Admittedly, there have been improvements in public health infrastructure and structural response to these diseases, however, a lot more needs to be done to have a firm grip. This study applied the geographically weighted poisson regression (GWPR) model to study the spatial variations in the effects of socioeconomic factors on COVID-19 Mortality in the African subregion. The performance of the GWPR model was compared to the conventional Poisson regression model. The results suggest that accounting for the spatial heterogeneity through the GWPR model (RMSE = 100.604, MAE = 70.582, Pseudo R 2  = 0.773) improves the modelling performance compared to the conventional Poisson regression model (RMSE = 118.574, MAE = 83.054, Pseudo R 2  = 0.530). In addition, the effects of all the socioeconomic variables considered (i.e., educational index, unemployment rates, income index, gross domestic product, and forest area) on COVID-19 mortality were found to be spatial non-stational across the subregion. These results highlight the need to draw preventive and response policies applicable to infectious diseases with greater consideration of the different geographical points on the continent.
ISSN:1865-0473
1865-0481
DOI:10.1007/s12145-024-01321-7