1. Steverding D. The history of leishmaniasis. Parasit Vectors. 2017; 10: 82. 2. Norouzinezhad F, Ghaffari F, Norouzinejad A, Kaveh F, Gouya MM. Cutaneous leishmaniasis in Iran: Results from an epidemiological study in urban and rural provinces. Asian Pacific Journal of Tropical Medicine. 2016; 6: 614-619. 3. Torres-Guerrero E, Quintanilla-Cedillo MR, Ruiz-Esmenjaud J, Arenas R. Leishmaniasis: a review. F1000Research. 2017; 6: 750. 4. Salimi M, Saghafipour A, Hamidi Parsa H, Khosravi M, Shirzadi

Background and Objectives: Environmental conditions in different geographical areas provide a basis for the spread of some diseases. Cutaneous leishmaniasis is a serious threat to public health and is one of the arthropod-borne diseases. The prevalence and distribution of this disease is affected by environmental and climatic factors. The aim of this study was to model the Spatio-temporal variations in the incidence rate of this disease based on environmental and ecological criteria. Methods: The northeast of Iran was selected as the study area. The data used in this study included vegetation, surface temperature, precipitation, evapotranspiration, soil moisture, digital elevation model and sunny hours. The artificial neural network method was used to model the spatio-temporal changes of cutaneous leishmaniasis. Results: Spatial variations in the incidence of the disease had a north-south trend and decreased from north to south. In addition, two foci were identified in the medium altitude areas in North and South Khorasan provinces. Temporal variations in the incidence of disease in the study period showed that the incidence rate decreased in the two identified foci from 2011 to 2016. Conclusion: The modeling results showed that the estimated regression coefficient was 0.92 for neural network based on all three types of data (training, validation, test) indicating good quality of constructed neural network. In addition, sensitivity analysis results showed that sunny hours and soil moisture were the most important factors in the model function.