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Background: Phlebotomus and Sergentomya are distributed in the old-world regions and transmit leishmaniases through mammalian and reptile hosts. Cutaneous leishmaniasis (CL) is one of the most important diseases in Iran. Iranian sand flies belong to three Oriental, Palearctic, and Afrotropical Regions. Fars Province is located in Palearctic, but southern parts are affected Oriental region situations on phglebotomine population variety. Therefore, a comprehensive study was required on the vectors of the disease in this Province. Methods: Regarding the approved role of the environmental factors in creating the fauna and distribution of living creatures, the “de martonne climate method” was used, and the climate was noticed as an important environmental factor for the determination of vector distribution. Accordingly, 14 sampling sites were selected from 10 foci in different climates of Fars Province. 19648 sand flies were collected from the studied areas in this Province during 2016. Results: Phlebotomus papatasi and Se. antennata were the most frequent species, which were caught from in/outdoor areas. Phlebotomus sergenti and Ph. alexandri were caught from both Palearctic and Oriental zones but were more prevalent in the cold semi-arid climate of the Palearctic zone. Moreover, they were not caught from the hot desert and summer Mediterranean climates of the Oriental zone. Conclusions: It seems that Ph. papatasi as the main vector of CL could be well distributed in different climates in Fars. Moreover, some species like Ph. sergenti preferred especial climates in the Palearctic zone. Therefore, these data could be helpful to control leishmaniases more efficiently. [ABSTRACT FROM AUTHOR]

Objective: To determine the endemic values of cutaneous leishmaniasis in different cities of Fars province, Iran. Methods: Totally, 29 201 cases registered from 2010 to 2015 in Iranian Fars province were selected, and the endemic values of cutaneous leishmaniasis were determined by retrospective clusters derived from spatiotemporal permutation modeling on a time-series design. The accuracy of the values was assessed using receiver operating characteristic (ROC) curve. SPSS version 22, ArcGIS, and ITSM 2002 software tools were used for analysis. Results: Nine statistically significant retrospective clusters (P<0.05) resulted in finding seven significant and accurate endemic values (P<0.1). These valid endemic scores were generalized to the other 18 cities based on 6 different climates in the province. Conclusions: Retrospectively detected clusters with the help of ROC curve analysis could help determine cutaneous leishmaniasis endemic values which are essential for future prediction and prevention policies in the area. [ABSTRACT FROM AUTHOR]

Objective: To establish an early warning system for cutaneous leishmaniasis in Fars province, Iran in 2016. Methods: Time-series data were recorded from 29 201 cutaneous leishmaniasis cases in 25 cities of Fars province from 2010 to 2015 and were used to fit and predict the cases using time-series models. Different models were compared via Akaike information criterion/ Bayesian information criterion statistics, residual analysis, autocorrelation function, and partial autocorrelation function sample/model. To decide on an outbreak, four endemic scores were evaluated including mean, median, mean + 2 standard deviations, and median + interquartile range of the past five years. Patients whose symptoms of cutaneous leishmaniasis began from 1 January 2010 to 31 December 2015 were included, and there were no exclusion criteria. Results: Regarding four statistically significant endemic values, four different cutaneous leishmaniasis space-time outbreaks were detected in 2016. The accuracy of all four endemic values was statistically significant (P<0.05). Conclusions: This study presents a protocol to set early warning systems regarding time and space features of cutaneous leishmaniasis in four steps: (i) to define endemic values based on which we could verify if there is an outbreak, (ii) to set different time-series models to forecast cutaneous leishmaniasis in future, (iii) to compare the forecasts with endemic values and decide on space-time outbreaks, and (iv) to set an alarm to health managers. [ABSTRACT FROM AUTHOR]