Bissilimou Rachidatou Orounla

The COVID-19 pandemic has spread rapidly across the world and caused several economic, social, and demographic impacts, even though there were strong geographical disparities. This study aims to assess the effect of socio-demographic factors and the use of non conventional medicines on COVID-19 risk perception in West Africa using Structural Equation Modeling (SEM) approach. A quantitative survey was conducted in four countries (Benin, Togo, Ghana and Côte d’Ivoire). Data were collected on demographic characteristics, COVID-19 risk perception (risk feeling and risk analysis), affective attitude, trust predictors and non-conventional medicine. Nominal polychotomous logistic regression, binary logistic regression and partial least squares were used for the data analysis. Among the respondents 59.11% from the in-person survey, 28.08% were from Benin, 32.84% from Côte d’Ivoire, 24.96% from Togo and 14.12% from Ghana. The results showed a very high level of risk perception within the countries. Participants aged between 18 and 40 used less non-conventional medicine. Also, people with a low level of education or no formal education often perceive a higher risk associated with COVID-19 and use more non-conventional medicine than others. The PLS-SEM model’s loadings were higher compared to those of the Consistent PLS (PLSc-SEM), but the Consistent PLS showed robust values in the structural model with lower RMSE than the linear model. Our results also indicated that non-conventional medicine has a positive relationship with COVID-19 risk perception. For decision-makers and health workers, this research underscores the importance of unconventional medicine and the emotional state of local population in managing epidemic.

Structural Equation Modeling (SEM) is a multivariate statistical technique increasingly used in epidemiology to analyze complex causal relationships. This paper focuses on the practical use of the SEM technique and analyze applications in terms of model design, model development, and model evaluation to address epidemics-related problems based on articles published from 2013 to 2022. The selection of articles was based on the PRISMA methodology. The review was based on 111 scientific papers after applying the exclusion criteria. Data on research design, sample size, software, estimation methods and evaluation methods were extracted.Findings reveal a significant rise in SEM applications over the past decade, particularly during the COVID-19 pandemic, with 70.45 % of the studies focused on COVID-19-related issues. Partial Least Squares SEM (PLS-SEM) was the most frequently applied estimation method (50 %), followed by Maximum Likelihood Estimation (29.17 %) and Bayesian approaches (8.33 %). The most commonly used software included AMOS (25.68 %), SPSS (24.32 %), and Smart-PLS (14.86 %). Model evaluation relied on absolute fit indices such as Root Mean Square Error of Approximation (RMSEA; 14.47 %), Chi-square (7 %), and Standardized Root Mean Square Residual (SRMR ; 7.23 %), as well as incremental indices like Comparative Fit Index (CFI; 14.47%) and Tucker-Lewis Index (TLI; 7.55 %). Only 3.85 % of the studies had sample sizes below 100, while 24.03 % had 1,000 or more. An illustrative example of SEM applied to COVID-19 data is provided. This review underscores the growing relevance of SEM in epidemiology, emphasizing both its potential and the methodological considerations needed to ensure robust and interpretable results in public health research.

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.