Background: Human brucellosis is widespread in Fars province. The present study aimed to investigate the effect of climate on its incidence and determine the areas prone to the infection.
Methods: Monthly meteorological data and the incidence rate of human brucellosis during 2009-2015 were collected and their correlation was studied using Pearson’s correlation coefficient. Additionally, the multiple regression method and multi-layer perceptron neural network model were used to predict the incidence of human brucellosis. In order to analyze the data SPSS software (version 16.0), MATLAB software (version 8.1), and GIS software (version 10.4) were used.
Results: Pearson’s regression analysis, on a monthly basis, showed a significant indirect correlation between the incidence of human brucellosis (with a time lag of up to 5 months) and climatic parameters (minimum temperature: -0.72 and evaporation: -0.73) in Abadeh (Fars, Iran). Moreover, there was a significant direct correlation (P<0.001) between the incidence of human brucellosis and the maximum relative humidity (+0.67) and rainfall (+0.48). The incidence of human brucellosis in Abadeh was predicted by using artificial neural network models (4 layers, 4 neurons in each layer), the Levenberg-Marquardt training algorithm, and Sigmoid transfer function. It was determined that a correlation rate of 0.89 in the training level and 0.8 in the test level (with the lowest error rate) were the best values in multi-layer perceptron modeling.
Conclusion: Climatic parameters are important factors in determining the incidence rate of human brucellosis in Fars province. Climate conditions provide a favorable environment for the spread of human brucellosis in this area.