Acta tropica · 2026
Environmental drivers and spatio-temporal heterogeneity of Cutaneous Leishmaniasis in Ouarzazate Province, Southeastern Morocco: A 15-year municipal-level analysis (2009-2024).
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Ouzyou Boujemaa, Hakem Adnane, El Alaoui Omar, El Khiat Abdelaati, Talbi Fatima Zahra
Аннотация
INTRODUCTION: Cutaneous leishmaniasis (CL) is a major public health problem in Morocco, particularly in the arid and semi-arid areas of the southeast. This study aims to analyze the epidemiological and spatio-temporal evolution of CL in the province of Ouarzazate over the period 2009-2024, and to identify the ecological factors associated with its distribution. Additionally, the study will compare the application of the ARIMA model and Random Forest model in predicting the number of CL cases.
METHODS: This is a retrospective descriptive study based on parasitologically confirmed cases recorded by the Provincial Health Delegation. The average annual and cumulative incidences were calculated at the municipal level. Statistical analyses were performed using RStudio, and spatial analyses were performed using ArcGIS 10.7. The Friedman test evaluated temporal variations, while Spearman's correlation examined the association between incidence and environmental factors. The ARIMA and Random Forest models were constructed using monthly case numbers of CL from January 2009 to December 2024. The performance of the models was evaluated using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE).
RESULTS: A total of 5326 cases were reported. The disease mainly affects children under the age of 10 (41.4%) and is most prevalent in rural areas (64.8%). The dominant species is Leishmania major. The temporal evolution shows an oscillating profile, with an epidemic peak in 2018 (244.87 cases/100,000 inhabitants), followed by a gradual decline. Spatial analysis reveals marked heterogeneity, with hyperendemic foci located in eastern municipalities, notably Idelsane, Skoura Ahl El Oust, and Toundoute. A significant negative correlation was observed between altitude and incidence (ρ = -0.576; p = 0.0155), suggesting a limiting role for high-altitude areas. In the predictive analysis, both models captured the general trend of CL incidence. However, the Random Forest model demonstrated better performance in short-term forecasting compared to the ARIMA model.
CONCLUSIONS: CL in the province of Ouarzazate has a heterogeneous spatial and temporal distribution, strongly influenced by ecological factors and land use. Identifying high-risk municipalities is an essential tool for guiding surveillance and targeted control strategies. Furthermore, the forecasting results provide an early warning tool for public health authorities to take preventive measures against CL.
METHODS: This is a retrospective descriptive study based on parasitologically confirmed cases recorded by the Provincial Health Delegation. The average annual and cumulative incidences were calculated at the municipal level. Statistical analyses were performed using RStudio, and spatial analyses were performed using ArcGIS 10.7. The Friedman test evaluated temporal variations, while Spearman's correlation examined the association between incidence and environmental factors. The ARIMA and Random Forest models were constructed using monthly case numbers of CL from January 2009 to December 2024. The performance of the models was evaluated using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE).
RESULTS: A total of 5326 cases were reported. The disease mainly affects children under the age of 10 (41.4%) and is most prevalent in rural areas (64.8%). The dominant species is Leishmania major. The temporal evolution shows an oscillating profile, with an epidemic peak in 2018 (244.87 cases/100,000 inhabitants), followed by a gradual decline. Spatial analysis reveals marked heterogeneity, with hyperendemic foci located in eastern municipalities, notably Idelsane, Skoura Ahl El Oust, and Toundoute. A significant negative correlation was observed between altitude and incidence (ρ = -0.576; p = 0.0155), suggesting a limiting role for high-altitude areas. In the predictive analysis, both models captured the general trend of CL incidence. However, the Random Forest model demonstrated better performance in short-term forecasting compared to the ARIMA model.
CONCLUSIONS: CL in the province of Ouarzazate has a heterogeneous spatial and temporal distribution, strongly influenced by ecological factors and land use. Identifying high-risk municipalities is an essential tool for guiding surveillance and targeted control strategies. Furthermore, the forecasting results provide an early warning tool for public health authorities to take preventive measures against CL.