Background: Cutaneous leishmaniasis is the most widespread form of parasitic diseases transmitted by sand flies through their bites causing localized skin lesions or nodular lesions and numerous spots on human skin. In the Setif region of Algeria, the disease has become a major concern for public health. This area is at high risk because it is located near the southern foci of leishmaniasis. Factors that promote parasite transmission are multiple and complex to analyze. The purpose is to determine and enumerate the factors that favor the transmission of leishmaniasis. Establish the parasite dynamics as a function of space and time.
Methods: An artificial neural network is established. The input variables are factors that favor parasite transmission (Seasons, climatic conditions, defy of migratory flow of population and goods). The output variable is the degree of spread of the leshmaniosis. Since input variables are considered complex, uncertain, an artificial neural network demonstrates its ability to solve such complexity. Result: After the learning phase of the network from the real data, this creates a function of correspondence between the space of inputs and output. The function is corrected by adjusting the weights of each input until it is optimized.
Conclusion: The established system makes it possible to instantly read the degree of spread of the leshmaniosis from the introduction of the random values at the input with the maximum precision. The proposed system remains extensible to input variables that may have an effect on the output.