Causal Structural Equation Modeling of Climatic and Environmental Factors Influencing Malaria Incidence in Southeastern Senegal

Leontine Ndogou  Bakhoum; Mor Absa  Loum; Mouhamad Mounirou  Allaya; Gouvidé Jean  Gbaguidi; Khady  Ndiaye; Almamy  Youssouf Ly; Mamadou  Bousso; Jean Louis  Abdourahim Ndiaye

doi:10.6000/1929-6029.2026.15.17

Authors

Leontine Ndogou Bakhoum Faculty of Science and Technology, Iba Der Thiam University, Thiès, Thiès, Senegal
Mor Absa Loum Faculty of Engineering Sciences, Iba Der Thiam University, Thiès, Thiès, Senegal
Mouhamad Mounirou Allaya Faculty of Economic and Social Sciences, Iba Der Thiam University, Thiès, Thiès, Senegal
Gouvidé Jean Gbaguidi West African Science Service Centre on Climate Change and Adapted Land Use (WASCAL), Faculty of Human and Social Sciences, University of Lomé, Lomé, Togo, Technology, Engineering and Mathematics of Abomey, National University of Science, Benin, Benin
Khady Ndiaye Faculty of Science and Technology, Iba Der Thiam University, Thiès, Thiès, Senegal
Almamy Youssouf Ly Faculty of Science and Technology, Iba Der Thiam University, Thiès, Thiès, Senegal
Mamadou Bousso Faculty of Economic and Social Sciences, Iba Der Thiam University, Thiès, Thiès, Senegal
Jean Louis Abdourahim Ndiaye Parasitology and Mycology Department, Faculty of Health Sciences, Iba Der Thiam University, Thiès, Senegal

DOI:

https://doi.org/10.6000/1929-6029.2026.15.17

Keywords:

Causal modeling, Structural equation modeling, Exploratory factor analysis, Environmental epidemiology, Malaria, Vector-borne disease, West Africa

Abstract

Introduction: Malaria remains a major public health problem in tropical regions due to complex interactions between climatic, environmental, and epidemiological processes.

Objective: To improve the quantitative understanding of these mechanisms, this study introduces a causal analysis framework based on structural equation modeling (SEM) to elucidate the direct and indirect pathways through which climatic and environmental variables influence malaria incidence.

Methods: We analyzed weekly malaria case data collected in four districts of southeastern Senegal (Kédougou, Salémata, Saraya, and Dianké Makha) over the period 2018–2024 using structural equation modeling and climates factors. Climate predictors included temperature, precipitation, relative humidity, wind speed, atmospheric pressure, cloud cover, and lunar phase, obtained from Visual Crossing, complemented by vegetation indices derived from satellite imagery.

Results: The results revealed significant spatial heterogeneity: rainfall and cloud cover had the strongest total effects on malaria incidence in three districts (standardized total effects ranging from 0.51 to 0.66), while high wind speed emerged as the primary determining factor in Kédougou, with a negative association (−0.51). Wind-related factors showed protective effects against malaria transmission, with notable spatial complexity, and atmospheric pressure exhibited consistent indirect effects (0.18 to 0.42), mediated by other climatic predictors, but without a significant direct association.

Conclusion: These results underscore the importance of considering indirect causal pathways in environmental epidemiology and highlight district-specific climatic profiles that can inform localized malaria control strategies. The SEM framework presented here offers a rigorous statistical approach to integrating multi-source climatic and environmental data to advance causal inference in vector-borne disease research, ultimately supporting early warning systems and targeted vector control interventions at the district level.

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