Direct and Indirect Effects of Environmental and Socio-Economic Factors on COVID-19 in Africa Using Structural Equation Modeling

Bissilimou Rachidatou Orounla (), Ayédèguè Eustache Alaye, Kolawolé Valère Salako, Codjo Emile Agbangba, Justice Moses K. Aheto and Romain Glèlè Kakaï
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Bissilimou Rachidatou Orounla: Laboratory of Biomathematics and Forest Estimation, Faculty of Agronomic Sciences, University of Abomey-Calavi, Cotonou 04 BP 1525, Benin
Ayédèguè Eustache Alaye: Laboratory of Biomathematics and Forest Estimation, Faculty of Agronomic Sciences, University of Abomey-Calavi, Cotonou 04 BP 1525, Benin
Kolawolé Valère Salako: Laboratory of Biomathematics and Forest Estimation, Faculty of Agronomic Sciences, University of Abomey-Calavi, Cotonou 04 BP 1525, Benin
Codjo Emile Agbangba: Laboratory of Biomathematics and Forest Estimation, Faculty of Agronomic Sciences, University of Abomey-Calavi, Cotonou 04 BP 1525, Benin
Justice Moses K. Aheto: Department of Biostatistics, School of Public Health, College of Health Sciences, University of Ghana, Legon-Accra P.O. Box LG13, Ghana
Romain Glèlè Kakaï: Laboratory of Biomathematics and Forest Estimation, Faculty of Agronomic Sciences, University of Abomey-Calavi, Cotonou 04 BP 1525, Benin

Stats, 2024, vol. 7, issue 3, 1-15

Abstract: Understanding direct and indirect relationships of environmental, socio-economic and climate variables and the dynamics of epidemics is key to guiding targeted public health policy and interventions. This study investigates the direct and indirect effects of environmental and socio-economic factors on the COVID-19 dynamics in Africa (54 African countries from 2019 to 2021) using SEM approach. Specifically, the study aimed to: (i) assess the performance of two SEM estimation methods (Lisrel and PLS-SEM) in relationship to sample size (100, 200, 500, and 1000) and level of model complexity (No, two, and four indirect effects) and (ii) use the most performing SEM estimation method to examine direct and indirect effects of factors influencing the number of cases and deaths of COVID-19 in Africa. The results highlight a positive spatial correlation between factors such as temperature, humidity, age, the proportion of people aged over 65, and the COVID-19 incidence. Under the control of confounding factors, Lisrel turns out to be the most performing method, identifying climate, demographic and economic factors as the main determinants of COVID-19 dynamics. These factors have a direct and significant impact on the incidence of COVID-19. An indirect relationship was also observed between economic factors and the incidence of COVID-19 through air pollutants. The results highlight the importance of considering these factors in understanding the spread of the virus to avoid further disasters.

Keywords: COVID-19 dynamics; PLS; Lisrel; fit measures; estimation methods; climate (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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