Climate change and cutaneous leishmaniasis in the province of Ghardaïa in Algeria: A model-based approach to predict disease outbreaks

BACKGROUND: Cutaneous leishmaniasis (CL) is a vector-borne disease prevalent in Algeria since 2000. The disease has significant impacts on affected communities, including morbidity and social stigma. OBJECTIVE: Investigate the association between environmental factors and the incidence of CL in the province of Ghardaïa and assess the predictive capacity of these factors for disease occurrence. DESIGN: Retrospective SETTING: The study area included both urban and rural communities. METHODS: We analyzed a dataset on CL in the province of Ghardaïa, Algeria, spanning from 2000 to 2020. The dataset included climatic variables such as temperature, average humidity, wind speed, rainfall, and the normalized difference vegetation index (NDVI). Using generalized additive models, we examined the relationships and interactions between these variables to predict the emergence of CL in the study area. MAIN OUTCOME MEASURES: The identification of the most significant environmental factors associated with the incidence and the predicted incidence rates of CL in the province of Ghardaïa, Algeria. SAMPLE SIZE AND CHARACTERISTICS: 252 monthly observations of both climatic and epidemiological variables. RESULTS: Relative humidity and wind speed were the primary climatic factors influencing the occurrence of CL epidemics in Ghardaïa, Algeria. Additionally, NDVI was a significant environmental factor associated with CL incidence. Surprisingly, temperature did not show a strong effect on CL occurrence, while rainfall was not statistically significant. The final fitted model predictions were highly correlated with real cases. CONCLUSION: This study provides a better understanding of the long-term trend in how environmental and climatic factors contribute to the emergence of CL. Our results can inform the development of effective early warning systems for preventing the transmission and emergence of vector-borne diseases. LIMITATIONS: Incorporating additional reservoir statistics such as rodent density and a human development index in the region could improve our understanding of disease transmission.