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Zoonotic Cutaneous Leishmaniasis (ZCL) is one of the endemic diseases in central part of Iran. The aim of this cross-sectional study was to find the areas with a higher risk of infection considering the distribution of vector, reservoir hosts and human infection. Passive data recorded the positive cases of cutaneous leishmaniasis in Yazd province health center were collected for 10 years, from 2007 to 2016 at the County level. Considering all earlier studies conducted in Yazd province, records of Phlebotomus papatasi, the main vector of ZCL, and Rhombomys opimus, the main reservoir of ZCL, were collected and entered in a database. ArcGIS and MaxEnt model were used to map and predict the best ecological niches for both vector and reservoir. The most cumulative incidence of the disease was found to be in Khatam County, south of Yazd province. The area under curve (AUC) for R. opimus and P. papatasi was 0.955 and 0.914, respectively. We found higher presence probability of both vector and reservoir in central and eastern parts of the province. The jackknife test indicated that temperature and normalized difference vegetation index (NDVI) had the most effect on the model for the vector and reservoir, respectively. The areas with higher presence probability for the reservoirs and vectors were considered having the higher potential for ZCL transmission. These findings can be used to prevent and control the disease., (Copyright © 2018 Elsevier B.V. All rights reserved.)

• Mosquito infections with Sindbis (SINV) and Chikungunya (CHIKV) viruses in the northwest of Iran were examined. • The presence of the SINV genome in Culex pipiens complex and Cx. theileri was confirmed. • No CHIKV infection cases were identified in the 149 examined mosquito pools. • North, northwest, south, and southeast of Iran are the probable ecological niches of SINV vectors. Sindbis virus (SINV) and Chikungunya virus (CHIKV) are among the most widely spread mosquito-borne viruses worldwide. Due to the key role of mosquitoes in the transmission cycle of vector-borne diseases, models such as Maximum Entropy (MaxEnt) have been used in recent years to predict the environmental suitability and ecological niches of mosquito vectors. Infection of three mosquito species (Anopheles maculipennis s.l., Culex tritaeniorhynchus , and Culiseta longiareolata) with CHIKV has recently been reported in Iran. However, given the importance of vector-borne diseases in the country, there is a need for extensive studies on the infection of mosquitoes with CHIKV and SINV in different areas of the country. Accordingly, the current research was conducted to investigate the infection of mosquitoes with the two aforementioned viruses in the northwestern part of Iran and also to model the ecological niches of the vectors of these mosquito-borne viruses in the country. In the current study, 4639 mosquito specimens, consisting of 2515 adults and 2124 larvae, were collected from the wetlands of West Azerbaijan Province and identified. Ten species belonging to four genera were identified in this study. The specimens were allocated to 149 pools for the determination of infection with CHIKV and SINV. The amplification pattern of five pools comprising two mosquito species (Culex pipiens complex and Cx. Theileri) corresponded to the reference strain of SINV, and the isolates were sequenced to confirm the presence of SINV genome. No cases of CHIKV infection were found among the 149 examined mosquito pools. Data on the distribution of Cx. Pipiens complex and Cx. Theileri were mapped using ArcMap 10.5. Prediction maps of the presence probability for these species revealed that they are most likely to be found in and spread to the north, northwest, south, and southeastern areas of the country and in areas with abundant water resources. For the first time in Iran, our study investigated the presence probability of SINV vectors using ecological niche modeling. Ecological niche profiling showed that the most suitable habitats for Cx. pipiens are mainly concentrated in the north and northwestern parts of the country, whereas Cx. theileri is mostly located in the northwest and western regions. However, there were some other areas of low suitability for these two species in the country. Further studies in a broader geographical area with more species of mosquitos and the determination of infection with other mosquito-borne viruses can provide a clear understanding of the potential spread of mosquito-borne diseases in various regions of Iran. [Display omitted] [ABSTRACT FROM AUTHOR]

Zoonotic cutaneous leishmaniasis (ZCL), caused by Leishmania major, is a common zoonotic vector-borne disease in Iran. Close contact with infected reservoir hosts increases the probability of transmission of Leishmania parasite infections to susceptible humans. Four gerbil species (Rodentia: Gerbillidae) serve as the main reservoir hosts for ZCL in different endemic foci of Iran. These species include Rhombomys opimus , Meriones libycus, Meriones hurrianae and Tatera indica; while notable infection has been reported in Nesokia indica as well. The purpose of this study is to model the distribution of these reservoirs to identify the risk areas of ZCL. A data bank was developed including all published data during the period of 1970–2015. Maximum entropy model was used to find the most appropriate ecological niches for each species. The areas under curve obtained were 0.961, 0.927, 0.922, 0.997 and 0.899, instead of 1, for training test in R. opimus , M. libycus , T. indica , M. hurrianae and N. indica, respectively. The environmental variable with the highest gain when used in isolation was slope for R. opimus and N. indica , annual mean temperature for M. libycus, and seasonal precipitation for T. indica and M. hurrianae . Summation of presence probabilities for three main species, i.e., R. opimus , M. libycus and T. indica revealed favorable ecological niches in wide areas of 16 provinces. This is the first study to predict the distribution of ZCL reservoir hosts in Iran. Climatology and topography variables had high contributions toward the prediction of potential distribution of the main reservoir species; therefore, as climate changes, the models should be updated periodically with novel data, and the results should be used in disease-monitoring programs. [ABSTRACT FROM AUTHOR]