Cross-Country Assessment of Socio-Ecological Drivers of COVID-19 Dynamics in Africa: A Spatial Modelling Approach

Kolawole Valère Salako, Akoeugnigan Idelphonse Sode, Aliou Dicko, Eustache Ayédèguè Alaye, Martin Wolkewitz and Romain Glèlè Kakaï ()
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Kolawole Valère Salako: Laboratoire de Biomathématiques et d’Estimations Forestières, Faculté des Sciences Agronomiques, Université d’Abomey-Calavi, Cotonou 04 BP 1525, Benin
Akoeugnigan Idelphonse Sode: Laboratoire de Biomathématiques et d’Estimations Forestières, Faculté des Sciences Agronomiques, Université d’Abomey-Calavi, Cotonou 04 BP 1525, Benin
Aliou Dicko: Laboratoire de Biomathématiques et d’Estimations Forestières, Faculté des Sciences Agronomiques, Université d’Abomey-Calavi, Cotonou 04 BP 1525, Benin
Eustache Ayédèguè Alaye: Laboratoire de Biomathématiques et d’Estimations Forestières, Faculté des Sciences Agronomiques, Université d’Abomey-Calavi, Cotonou 04 BP 1525, Benin
Martin Wolkewitz: Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, 79104 Freiburg, Germany
Romain Glèlè Kakaï: Laboratoire de Biomathématiques et d’Estimations Forestières, Faculté des Sciences Agronomiques, Université d’Abomey-Calavi, Cotonou 04 BP 1525, Benin

Stats, 2024, vol. 7, issue 4, 1-15

Abstract: Understanding how countries’ socio-economic, environmental, health status, and climate factors have influenced the dynamics of COVID-19 is essential for public health, particularly in Africa. This study explored the relationships between African countries’ COVID-19 cases and deaths and their socio-economic, environmental, health, clinical, and climate variables. It compared the performance of Ordinary Least Square (OLS) regression, the spatial lag model (SLM), the spatial error model (SEM), and the conditional autoregressive model (CAR) using statistics such as the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Root Mean Square Error (RMSE), and coefficient of determination ( R 2 ). Results showed that the SEM with the 10-nearest neighbours matrix weights performed better for the number of cases, while the SEM with the maximum distance matrix weights performed better for the number of deaths. For the cases, the number of tests followed by the adjusted savings, Gross Domestic Product (GDP) per capita, dependence ratio, and annual temperature were the strongest covariates. For deaths, the number of tests followed by malaria prevalence, prevalence of communicable diseases, adjusted savings, GDP, dependence ratio, Human Immunodeficiency Virus (HIV) prevalence, and moisture index of the moistest quarter play a critical role in explaining disparities across countries. This study illustrates the importance of accounting for spatial autocorrelation in modelling the dynamics of the disease while highlighting the role of countries’ specific factors in driving its dynamics.

Keywords: coronavirus; cases; deaths; climate; spatial regression (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2024
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