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

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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)

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.

Suggested Citation

  • Kolawole Valère Salako & Akoeugnigan Idelphonse Sode & Aliou Dicko & Eustache Ayédèguè Alaye & Martin Wolkewitz & Romain Glèlè Kakaï, 2024. "Cross-Country Assessment of Socio-Ecological Drivers of COVID-19 Dynamics in Africa: A Spatial Modelling Approach," Stats, MDPI, vol. 7(4), pages 1-15, October.
    • Handle: RePEc:gam:jstats:v:7:y:2024:i:4:p:64-1098:d:1496741
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    References listed on IDEAS

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    1. Samuel O. M. Manda & Timotheus Darikwa & Tshifhiwa Nkwenika & Robert Bergquist, 2021. "A Spatial Analysis of COVID-19 in African Countries: Evaluating the Effects of Socio-Economic Vulnerabilities and Neighbouring," IJERPH, MDPI, vol. 18(20), pages 1-15, October.
    2. Bivand, Roger & Piras, Gianfranco, 2015. "Comparing Implementations of Estimation Methods for Spatial Econometrics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i18).
    3. Luc Anselin & Daniel Arribas-Bel, 2013. "Spatial fixed effects and spatial dependence in a single cross-section," Papers in Regional Science, Wiley Blackwell, vol. 92(1), pages 3-17, March.
    4. Ye Fan & Ming Fang & Xin Zhang & Yongda Yu, 2023. "Will the economic growth benefit public health? Health vulnerability, urbanization and COVID-19 in the USA," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 70(1), pages 81-99, February.
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