Spatial clustering between socioeconomic inequalities and COVID-19 mortality rate in Africa
Abstract Understanding the epidemiological patterns of infectious diseases across different regions and periods is essential, as it helps identify areas with elevated risks that require targeted control strategies. This paper aimed to investigate the spatial clustering pattern of COVID-19 at the cou...
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| Format: | Article |
| Language: | English |
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Springer
2025-06-01
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| Series: | Discover Public Health |
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| Online Access: | https://doi.org/10.1186/s12982-025-00755-8 |
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| author | Ropo Ebenezer Ogunsakin Johnson Adedeji Olusola Kemi Funlayo Akeju Adigun Kehinde Abimbola |
| author_facet | Ropo Ebenezer Ogunsakin Johnson Adedeji Olusola Kemi Funlayo Akeju Adigun Kehinde Abimbola |
| author_sort | Ropo Ebenezer Ogunsakin |
| collection | DOAJ |
| description | Abstract Understanding the epidemiological patterns of infectious diseases across different regions and periods is essential, as it helps identify areas with elevated risks that require targeted control strategies. This paper aimed to investigate the spatial clustering pattern of COVID-19 at the country level and explore the association between global development indicators covering the 48 countries in the five regions of Africa. The data utilized were extracted from multiple databases. The data were analysed by applying spatial analysis, including the Moran-I index, the Local Indicator of Spatial Association (LISA), and spatial regression models, to study the spatial variations in the effects of socioeconomic indicators on COVID-19 mortality at the country level. The preliminary test on the spatial correlation of COVID-19 showed a significant result; thus, a spatial regression approach was employed. The spatial clustering relationships between COVID-19 mortality and the socioeconomic indicators were analysed using ordinary least squares (OLS), spatial lag models (SLM), and spatial error models (SEM). We observed the clustering of countries for COVID-19 mortality, signifying spatial correlation within the countries of Africa. Among all the socioeconomic indicators included, the effects of Gross Domestic Product and age dependency ratio on COVID-19 mortality were the most critical indicators that described the pandemic evolution across the subregion. These results highlight the need to draw preventive and response policies applicable to infectious diseases with more significant consideration of the different geographical points in the region. |
| format | Article |
| id | doaj-art-a3c76b8a10d64165add29ced47a94d13 |
| institution | Kabale University |
| issn | 3005-0774 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Public Health |
| spelling | doaj-art-a3c76b8a10d64165add29ced47a94d132025-08-20T03:47:24ZengSpringerDiscover Public Health3005-07742025-06-0122111310.1186/s12982-025-00755-8Spatial clustering between socioeconomic inequalities and COVID-19 mortality rate in AfricaRopo Ebenezer Ogunsakin0Johnson Adedeji Olusola1Kemi Funlayo Akeju2Adigun Kehinde Abimbola3Department of Medicine, Schulich School of Medicine & Dentistry, Western UniversityDepartment of Geography and Planning Science, Ekiti State UniversityDepartment of Economics, Faculty of the Social Sciences, Ekiti State UniversityDepartment of Mathematical and Physical Science, Afe Babalola UniversityAbstract Understanding the epidemiological patterns of infectious diseases across different regions and periods is essential, as it helps identify areas with elevated risks that require targeted control strategies. This paper aimed to investigate the spatial clustering pattern of COVID-19 at the country level and explore the association between global development indicators covering the 48 countries in the five regions of Africa. The data utilized were extracted from multiple databases. The data were analysed by applying spatial analysis, including the Moran-I index, the Local Indicator of Spatial Association (LISA), and spatial regression models, to study the spatial variations in the effects of socioeconomic indicators on COVID-19 mortality at the country level. The preliminary test on the spatial correlation of COVID-19 showed a significant result; thus, a spatial regression approach was employed. The spatial clustering relationships between COVID-19 mortality and the socioeconomic indicators were analysed using ordinary least squares (OLS), spatial lag models (SLM), and spatial error models (SEM). We observed the clustering of countries for COVID-19 mortality, signifying spatial correlation within the countries of Africa. Among all the socioeconomic indicators included, the effects of Gross Domestic Product and age dependency ratio on COVID-19 mortality were the most critical indicators that described the pandemic evolution across the subregion. These results highlight the need to draw preventive and response policies applicable to infectious diseases with more significant consideration of the different geographical points in the region.https://doi.org/10.1186/s12982-025-00755-8AfricaCOVID-19PandemicClusteringSEMSLM |
| spellingShingle | Ropo Ebenezer Ogunsakin Johnson Adedeji Olusola Kemi Funlayo Akeju Adigun Kehinde Abimbola Spatial clustering between socioeconomic inequalities and COVID-19 mortality rate in Africa Discover Public Health Africa COVID-19 Pandemic Clustering SEM SLM |
| title | Spatial clustering between socioeconomic inequalities and COVID-19 mortality rate in Africa |
| title_full | Spatial clustering between socioeconomic inequalities and COVID-19 mortality rate in Africa |
| title_fullStr | Spatial clustering between socioeconomic inequalities and COVID-19 mortality rate in Africa |
| title_full_unstemmed | Spatial clustering between socioeconomic inequalities and COVID-19 mortality rate in Africa |
| title_short | Spatial clustering between socioeconomic inequalities and COVID-19 mortality rate in Africa |
| title_sort | spatial clustering between socioeconomic inequalities and covid 19 mortality rate in africa |
| topic | Africa COVID-19 Pandemic Clustering SEM SLM |
| url | https://doi.org/10.1186/s12982-025-00755-8 |
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