ABSTRACT
Leptospirosis is a zoonotic disease found wherever human is in direct or indirect contact with contaminated water and environment. Considering the increasing number of cases of this disease in the northern part of Iran, identifying areas characterized by high disease incidence risk can help policy-makers develop strategies to prevent its further spread. This study presents an approach for generating predictive risk maps of leptospirosis using spatial statistics, environmental variables and machine learning. Moran's I demonstrated that the distribution of leptospirosis cases in the study area in Iran was highly clustered. Pearson's correlation analysis was conducted to examine the type and strength of relationships between climate and topographical factors and incidence of the disease. To handle the complex and nonlinear problems involved, machine learning based on the support vector machine classification algorithm and multilayer perceptron neural network was exploited to generate annual and monthly predictive risk maps of leptospirosis distribution. Performance of both models was evaluated using receiver operating characteristic curve and Kappa coefficient. The output results demonstrated that both models are adequate for the prediction of the probability of leptospirosis incidence.
