Background and aim: Echinococcosis as a zoonosis disease is one of the most important parasitic helminth that is affected by many risk factors such as the environmental factors. Thus, we predicted the regional vulnerability to Echinococcosis based on environmental factors using a fuzzy inference system (FIS) in Lorestan Province. 
Methods: Our study was cross-sectional study on 200 patients from Lorestan Province (west of Iran) who underwent surgery for hydatidosis between October 2005 and November 2014. In order to model the vulnerability to Echinococcosis, first we determined the effective environmental variables. In the next step, the FIS was designed and implemented using MATLAB v.2012 software. Thus, definition and determination of linguistic variables, linguistic values, and their range were performed based on expert knowledge. Then, the membership functions of inputs (environmental variables) and output (vulnerability to Echinococcosis) were defined. A fuzzy rules base was formed. Also, the defuzzification of output was done using a centroid defuzzification function. To test the accuracy of the predictive model, we calculated the AUC (to this purpose, we used four different thresholds, 5%, 10%, 15%, and 20%) using IDRISI Selva v.17.0 software.
Results: Based on the results of this study, Aligoudarz and Koohdasht counties were identified as a highest and lowest risk area in Lorestan, respectively. The results showed that a predictive model was more efficient than a random model (AUC>0.5). Also, potential vulnerable areas cover 78.29% at threshold of 5%, 60.72% at threshold of 10%, 43.54% at threshold of 15%, and 39.82% at threshold of 20% of the study area.
Conclusion: According to the success of this research, we emphasized the necessity of attention to fuzzy approach to model vulnerability to hydatidosis. This approach can provide a practical economic basis for making informed preventive services decisions and the allocation of health resources.


Keywords: Echinococcosis; regional vulnerability; environmental variables; fuzzy inference system


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