Abstract-Most environmental-epidemiological researches emphasize modeling as the causal link of different events (e.g., hospital admission, death, disease emergency). There has been a particular concern in the use of the Generalized Linear Models (GLMs) in the field of epidemiology. However, recent studies in this field highlighted the use of non-parametric techniques, especially the Generalized Additive Models (GAMs). The aim of this work is to compare performance of both methods in the field of epidemiology. Comparison is done in terms of sharpening the relation between the predictors and the response variable as well as in predicting outbreaks. The most suitable method is then adopted to elucidate the impact of bioclimatic factors on the emergence of the zoonotic cutaneous leishmaniasis (ZCL) disease in Central Tunisia. Monthly epidemiologic and bioclimatic data from July 2009 to June 2016 are used in this study. Akaike information criterion, R-squared and F-statistic are used to compare model performance, while the root mean square error is used for checking predictive accuracy for both models. Our results show the potential of GAM model to provide a better assessment of the nonlinear relations and to give a high predictive accuracy compared to GLMs. The results also stress the inaccurate estimation of risk factors when linear trends are used to model nonlinear structured data.