DOI : 10.5958/0974-4576.2020.00054.7 © J. ent. Res., 44 (2) : 315-322 (2020)

Modeling of at risk areas of Zoonotic Cutaneous Leishmaniasis (ZCL) using Hierarchical Analysis Process (AHP) and Geographic Information System (GIS) in Southwest of

Elham Jahanifard*, Ahmad Ali Hanafi-Bojd**, Amir Ahmad Akhavan**, Mona Sharififard*, Atefeh Khazeni**** and Babak Vazirianzadeh*** *Social Determinants of Health Research Center, Jundishapur University of Medical Sciences, Ahvaz, Iran

ABSTRACT Present study is concentrated on modeling of ZCL using eco-environmental and climatic elements in some counties situated in the center of the province and preparing their risk maps. Pairwise comparative matrices were designed based on 7 criteria, including mean temperature, mean humidity, mean rainfall, elevation, distance from river, land use and soil texture that were completed by leishmaniasis experts. The weight of criteria was obtained by Expert choice 11. The risk map was drawn using overlaying seven criteria and multiplying their weight derived from AHP method in ArcGIS10.5 software. The highest weight belongs to the climatic elements and the lowest weights were related to distance from the river. Also, very high- and high-risk areas were regarded as hot spots. The incidence rate of disease was calculated in Hamidyeh (6.5), Karoun (1.5), Ahvaz (1.03) and Bavy (0.726) per 10000 in 2017. The incidence rate of ZCL decreased in Bavy County to 0.4 per 10000 persons while the ZCL incidence rates were increased to 1.04, 6.7 and 1.7 per 10000 persons in Ahvaz, Hamidyeh and Karoun Counties in 2018, respectively. In two rural districts, Tarah and Jahad, of Hamidyeh County and the majority parts of the risk of disease was predicted moderate. The risk map based on AHP and GIS is able to visualize the problems and help to Health policy makers to use the available evidence and make the best decision.

Key words : AHP, Cutaneous leishmaniasis, Khuzestan, risk map, Iran.

INTRODUCTION due to agricultural, urbanization, industrial activities, Leishmaniasis as neglected infectious and weak immune system and lack of financial resources vector- borne disease is caused by a variety species (WHO, 2019). Zoonotic cutaneous leishmaniasis of Leishmania parasite that transmit by sand flies is a common disease between human and animal species (Yaghoobi-Ershadi, 2012). Leishmaniasis is that environmental, ecological and geological an endemic disease in Iran and have three clinical factors can influence the distribution of the forms including zoonotic cutaneous leishmaniasis vector and the reservoir and consequently the (ZCL), anthroponetic cutaneous leishmaniasis (ACL) emergence of the disease (Salomón et al., 2012). and zoonotic visceral leishmaniasis (ZVL) due The most cases of CL occur in the Americas, the to Leishmania major, L. tropica and L. infantum, Mediterranean basin, the Middle East and Central respectively (Yaghoobi-Ershadi, 2012; Mohebali, Asia. More than 95% of new CL cases occurred 2013). The different forms of the disease are in 6 countries as follows: Afghanistan, Algeria, correlated to numerous factors including poverty, Brazil, Colombia, Iran, Iraq and the Syrian Arab malnutrition, population emigration, inappropriate Republic during 2017. New cases of cutaneous and poor housing, making environmental changes leishmaniasis are estimated to be 7,00,000 to 1 million and also some 26,000 to 65,000 deaths *Corresponding author's E-mail : [email protected], elham.jahani56@ gmail.com occur annually (WHO, 2019). Moreover, the new **Department of Medical Entomology & Vector Control, School of Public cases of zoonotic visceral leishmaniasis (ZVL) are Health, Tehran University of Medical Sciences, Tehran, Iran ***Department of Medical Entomology & Vector Control, School of Public about 100-300 in the country, annually (Mohebali, Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran 2013). Iran is one of counties in view of cases ****Isfahan Province Health Center, Isfahan University of Medical Sciences, Isfahan, Iran number of CL in the world in 2015 (Piroozi, Journal of Entomological Research, June 2020

2019). Zoonotic cutaneous leishmaniasis (ZCL) based MCDA used to spatio-temporal distribution is endemic in 18 of 31 provinces. About 80% of ZCL in Golestan Province, northeast of Iran (Mollalo cases reported in the country are in the form of and Khodabandehloo, 2016). Analytic Hierarchy ZCL (Yaghoobi-Ershadi, 2012). About 4700 cases of Process and AHP fuzzy were used to determine ZCL were reported from during the susceptibility map of visceral leishmaniasis in 2010-2014 (Ostad et al., 2016). More than 400 northwest of Iran (Rajabi et al., 2012). cases have been reported in the Dasht Azadegan, The Analytic Hierarchy Process (AHP) is an Ahvaz and Counties during 2009-2014 effective tool for dealing with complex decision (Khademvatan et al., 2017). The geographical making that introduced by Thomas Saaty (1970s). distribution in north parts of Khuzestan for L. major Furthermore, it is a method of measurement and L. tropica with the high amount were calculated with ratio scales that help the decision makers 91.84 and 8.16%, respectively (Maraghi et al., 2013). in complicated circumstances to judge and make the best decision (Saaty, 1987). This method is Geographic Information System (GIS) is used to one of the multi-criteria decision making analysis assess the impact of various factors on health, public (MCDA) which is applicable to solving complicated health, disease distribution, health care, and to help problems due to simplicity and making multilevel make a decision (Keola et al., 2002). Moreover, this hierarchies (Danesh et al., 2015). GIS-based MCDA software was used for ecological niche modeling was used for spatio-temporal distribution of ZCL of various vectors and distribution of vector-borne in Golestan Province, northeast of Iran (Mollalo disease (Zou et al., 2006). Environmental variables and Khodabandehloo, 2016). Analytic Hierarchy are considered as a risk factor of leishmaniasis Process and AHP fuzzy were used to determine the distribution (Sharifi et al., 2015). Furthermore, susceptibility map of VL in northwest of the country ecological factors (vegetation cover, elevation) in (Rajabi et al., 2012). Modeling based on MCDA in combination with environmental variables can be combination with GIS is a useful and affordable used for predicting and GIS modeling of vectors method to prevent, control and monitor the disease. of diseases and also it can be used for better The objective of this research is modeling and understanding the way in vector control program prediction of zoonotic cutaneous leishmaniasis risk (Bhunia et al., 2012; Tsegaw et al., 2013). In the map using GIS and AHP methods in some counties survey of eco-environmental risk factors of CL, the in the center of Khuzestan Province. The result result showed that rainy days, minimum temperature, can reduce field operations costs and increase the wind velocity, maximum relative humidity and ability of decision makers. population density were the most effective factors in distributing the disease (Ali-Akbarpour et al., 2012). MATERIALS AND METHODS Slope, precipitation of the wettest quarter and Study area : Khuzestan Province (29° 58’ N, 47° the mean temperature of coldest as topographical 41’ E and 33° 4’ N, 50° 39’ E) is situated in the and environmental variables are involved in the southwest of Iran in bordering Iraq and the Persian prediction of distribution of Rhombomys opimus, Meriones libycus and Tatera indica, respectively Gulf. This province has two regions: mountainous (Gholamrezaei et al., 2016). Another study in regions north of the Ahvaz ridge, and the plains and southwest of Iran showed soil texture, land cover and marshlands to its south. The area is irrigated by four land use are the most elements in the distribution rivers (Karoun, Karkheh, Jarahi and Maroun). The of Nesokia indica and T. indica (Jahanifard et al., climate of Khuzestan is generally very hot and humid, 2019). The Analytic Hierarchy Process (AHP) is an especially in the south, while winters are cold and effective tool that introduced by Thomas Saaty at dry. Furthermore, desert conditions and sandstorms 1970s. Furthermore, it is a method of measurement are also observed. Ahvaz County (30.883333 N, with ratio scales that help to the decision maker 48.016667 E and 31.766667 N, 49.3 E) is the capital in complicated circumstance to do judge and make of Khuzestan Province. The county distance to the the best decision (Saaty, 1987). This method is farthest city of Khuzestan in the northeast is 276 one of the multi-criteria decision making analysis km () and the nearest is 30 km ( in (MCDA) that is applicable to solving complicated the west of Ahvaz). The county is 18 m above sea decision problems due to simplicity and making level. The larger part of Khuzestan Province is in multilevel hierarchies (Danesh et al., 2015). GIS- the lowlands and Ahvaz is also in this area that it

316 Modeling of at risk areas of Zoonotic Cutaneous Leishmaniasis can be called the hottest areas of the country due AHP and predicting map of ZCL : Analytical Hierarchy to the lack of vegetation. Process is one of the main mathematical models and Karoun County (31.35 N, 48.683333 E and 31.7 multi-criteria techniques to support the decision theory N, 49.25 E) is situated in southwest of Khuzestan (Marinoni, 2004). This method developed in 1980 and in an area of over 5,000 Km2 and it is the fourth has experienced in various fields such as assessing, largest city in Khuzestan Province. The city has a designing, performance and decision making (Saaty warm and humid climate that reaches over 50°C and Vargas, 1991). The method summarized in nine in the hot summer. Hamidyeh County (30.883333 steps. Step 1: define alternatives, Step 2: organize N, 48.35 E and 31.266667 N, 48.9 E) is located at criteria, Step 3: make pairwise comparison, Step 25 Km from west of Ahvaz City in the Ahvaz road 4: collect input, Step 5: check consistency, Step 6: to Susanger City. It is 25 m above sea level. Bavy find group value, Step 7: weight of criteria, Step8: County (31.283333N, 48.266667 E and 31.7 N, 48.6 ranking the alternatives, and Step 9: final decisions E) is a county in the north of Ahavz County that has 2 (Saaty and Vargas, 1991). Analytical Hierarchy regions with Molasani City at the center of it (Fig. 1). Process is based on the human judgment, which sometimes causes an inconsistency; therefore the Leishmaniasis & eco-environmental and climatic data : consistency ratio (CR) is needed to be calculated. Cutaneous leishmaniasis data were collected from This ratio measures the level of inconsistency and the the health department of Khuzestan from 2015-2018 acceptable value of CR is below 0.1 (Saaty, 2008). based on the number of cases by location. The data were imported into an Excel file and transferred to the A pairwise matrix was developed based on seven ArcMap software. For the predicting of the disease, criteria to evaluate and determine the weighting of seven criteria like mean temperature, mean humidity, effective criteria for the distribution of leishmaniasis. mean precipitation, soil texture, elevation, land use The matrix was completed using the Saaty scale and distance from the river are regarded because by experts who specialized in vector and reservoir of their importance in distribution and life cycle of of leishmaniasis and the disease. The comparison Leishmania parasite, Phlebotomine vector and rodent scale of Saaty uses to explain the relative importance reservoir (Fig. 2). Climatic data were obtained from scale between two alternatives which the numerical Meteorological Organization of Khuzestan Province. rating is in the range of 1-9. In other words, the Regarding to analysis the data by the inverse distance number scale shows how many times one element weighted model (IDW), the climatic layers were is more dominant over another. The number of prepared in raster format. Shape files of river, land scales is 1-9 that means equally preferred, equally use, soil texture and elevation were prepared from to moderately, moderately preferred, moderately to the Iranian National Geographical Organization of strongly, strongly preferred, strongly to very strongly, Armed Forces. Distance from river layer was obtained very strongly preferred, very strong to extremely and based on the buffer operation in ArcMap application. extremely preferred, respectively (Saaty, 2005). All paired comparative matrix were analyzed by Expert choice version 11 software (Fig. 3). Seven layers were reclassified regarding sub criteria matrix analysis. The susceptibility map of ZCL was obtained by multiplying standard weight derived from the paired comparative matrix and overlaying all layers. The final map divided into 5 classes, very low, low, moderate, high and very high which shows the severity of the disease in Ahvaz, Hamidyeh, Bavy and Karoun Counties. Also, the rural district shape files of all countries overlaid on risk map to determine at risk areas (Fig. 4).

RESULTS AND DISCUSSION In vector-borne diseases, factors and Fig. 1. Study area including Hamidyeh, Bavy, Karoun and Ahvaz Counties, Khouzestan Province, Iran. environmental conditions and their severity can

317 Journal of Entomological Research, June 2020

Fig. 2. The maps of seven criteria for predicting zoonotic cutaneous leishmaniasis; a: Distance from river; b: Elevation; c: Soil texture; d: Humidity; e: Rainfall; f: Temperature; g: Land cover

318 Modeling of at risk areas of Zoonotic Cutaneous Leishmaniasis

Fig. 3. The weight of seven criteria affecting the zoonotic cutaneous leishmaniasis in by Analytic Hierarchy Process.

to the mean temperature, mean humidity and mean rainfall and the lowest weight was related to distance from the river. Mollalo et al. (2016) indicated that the occurrence of ZCL in the plains or low altitude areas was much higher than at the high elevation. It was also found that vegetation cover had a strong negative association with the risk of ZCL. A research in Tunisia showed that among the three environmental factors, humidity especially in July and September was more effective on the disease incidence and even more effective than rainfall (Toumi et al., 2012). Environmental and climatic factors can be used as Fig. 4. Zoonotic Cutaneous Lesihmaniasis risk map in rural a strategy for preventing and controlling the disease districts of counties, Khuzestan Province, Iran. because there were a positive relationship between mean temperature, relative humidity and slope with affect on distribution of vector and reservoir as well the incidence of CL in Isfahan. Furthermore, it was a as disease incidence (Gonzalez et al., 2010). Slope, negative relationship between maximum wind speed, land cover, mean temperature and elevation were altitude, vegetation cover and the disease incidence introduced as the best factors in vector distribution (Ramezankhani et al., 2018). Pairwise comparisons (Sofizadehet al., 2016). Ebrahimi et al. (2016) showed were also conducted between sub-criteria and their the possibility of Phlebotomus papatasi existence in weights were calculated by Expert choice 11 Software the temporal and rainy areas was equal to 80%. Also, (Table 2). Temperature criterion was divided into 4 they showed that there is no relationship between sub-criteria which 25-26°C had the highest weight elevation and density of vector. Pairwise comparative (0.258). About the criteria of mean humidity, the matrices were completed about the possibility of ZCL most weight was regarded to the third sub-criteria. by experts (Table 1). The importance and weight of Among 4 sub-criteria of rainfall, the amount of more these criteria and sub-criteria were determined with than 29 mm showed the weight equal to 0.525. All respect to the purpose of the study, the susceptibility the sub-criteria of elevation had the same values. map of the disease. Figure 3 demonstrated the The soil with clay and loamy texture was known as weight of the criteria. The highest weight belongs suitable choice in this research.

Table 1. Effective criteria on zoonotic cutaneous leishmaniasis risk map, Khuzestan Province, Iran 2018. Criteria Mean Mean Mean Soil texture Elevation Land cover Distance temperature humidity rainfall from river Mean temperature 1 2.47 2.47 3.48 4.22 2.71 6.80 Mean humidity 0.4 1 1.82 1.71 2.6 2.47 4.22 Mean rainfall 0.4 0.5 1 1.71 4.12 2.08 4.21 Soil texture 0.29 0.58 0.58 1 5.59 3.63 4.72 Elevation 0.24 0.38 0.24 0.18 1 1.71 1.44 Land cover 0.37 0.4 0.48 0.28 0.58 1 1.44 Distance from river 0.15 0.24 0.23 0.21 0.69 0.69 1

319 Journal of Entomological Research, June 2020

Table 2. Sub-criteria of seven important criteria and their many agricultural fields (Jahanifard et al., 2019). The weight using Analytic Hierarchy Process. weighted maps of seven criteria (mean temperature, Criteria Sub-criteria Standard Inconsistency mean humidity, mean rainfall, elevation, land use, weight ratio of Sub- soil texture and distance from river) were prepared of sub- criteria by reclassify maps based on weight of sub-criteria. criteria The risk map of ZCL was obtained from overlaying Mean Less than 25°C 0.105 all weighted map multiple calculated weights in AHP temperature 25-26 (°C) 0.258 0.04 method. For better understanding, the final map was classified into 5 levels including very low, low, More than 26°C 0.637 moderate, high and very high (Fig. 4). Also very Mean Less than 42% 0.128 high and high risk areas were regarded as hot humidity 42-43 (%) 0.276 0.00527 spots. Moshrehat and Ghayzanyeh rural districts of More than 43% 0.595 Ahvaz County and Sovayseh rural district of Karon Mean Less than 19 mm 0.061 County were situated in a very high risk area of the rainfall 19-24 (mm) 0.139 disease. Molasani and Vays rural district of Bavy 0.03 County were in low and very low high risk area 24-29 (mm) 0.275 of ZCL. In two rural districts, Tarah and Jahad, of More than 29 mm 0.525 Hamidyeh County and the majority parts of Ahvaz Elevation -30-37 (m) 0.333 County the risk of disease was predicted moderate. 37-104 (m) 0.333 0 The cases of disease were 194 persons in study 104-118 (m) 0.333 areas in 2018. The Figure 5 shows the cases of Soil texture Clay-Loamy-Sandy 0.243 ZCL in 2018 that Ahvaz County with more than 60 Clay-Loamy 0.607 cases and Bavy County with less than 10 patients 0.03 Sandy 0.101 had the most and least cases of CL, respectively. Inappropriate lands 0.049 The incidence rate of the disease was calculated in Hamidyeh (6.5), Karoun (1.5), Ahvaz (1.03) and Land use Drylands- 0.037 0.07 Bavy (0.726) per 10000 in 2017. The incidence rate Barelands- Sandylands- of ZCL decreased in Bavy County to 0.4 per 10000 Marshlands persons while the ZCL incidence rates were increased to 1.04, 6.7 and 1.7 per 10,000 persons in Ahvaz, Irrigated agriculture- 0.325 Agriculture Hamidyeh and Karoun Counties in 2018, respectively. Cumulative leishmaniasis cases were high in Ahvaz Pool-Swamp-River 0.046 County during 2017 and 2018. The reason could be Garden-Jungle- 0.479 due to socio-demographic variables such as personal Pasture habits, home conditions, work conditions and leisure Rocky-Urban areas 0.112 activities (Pedrosa and de Alencar Ximenes, 2009). Distance 0-300 (m) 0.519 from river 300-600 (m) 0.26 0.04 600-900 (m) 0.168 More than 900 m 0.053

Regarding the expert’s idea, this study confirmed that agricultural lands had an important role in increasing leishmaniasis cases. Previous researches clarified that human factors, such as agricultural projects, provide an appropriate environment for the growth of vectors and reservoir habitats (Shirzadi et al., 2015; Oryan et al., 2014). Also, a research which was conducted in revealed human Fig. 5. Zoonotic cutaneous leishmaniasis cases in study leishmaniasis cases occurred in the areas with areas in 2018.

320 Modeling of at risk areas of Zoonotic Cutaneous Leishmaniasis

Hmaidyeh County has more agricultural fields and Bhunia, G.S., Kesar, S., Chatterjee, N., Mandal, R., animal husbandries and in this area more habitats Kumar, V. and Das, P. 2012. Seasonal relationship provided for vector and reservoir. Also the stray dogs between normalized difference vegetation index and in this area may be as the reservoir. Earlier studies abundance of the Phlebotomus kala-azar vector in confirmed they were the secondary reservoirs of CL an endemic focus in Bihar, India. Geospat Health., in Khuzestan (Spotin et al., 2014). 1: 51-62. Cutaneous leishmaniasis susceptibility mapping Danesh, D., Ryan, M.J. and Abbasi, A. 2015. Using analytic hierarchy process as a decision-making using multi-criteria decision making (Analytic tool in project portfolio management. Int. J. Econ. hierarchy Process and Analytic Network Process) in Manag. Eng., 9: 4194-204. combination with GIS was conducted in Izeh County (Termeh, 2018). It is reported that the altitude is Gholamrezaei, M., Mohebali, M., Hanafi-Bojd, A.A., the most important criteria in incidence of CL that Sedaghat, M.M. and Shirzadi, M.R. 2016. Ecological is different from our research. The cause of this Niche Modeling of main reservoir hosts of zoonotic discrepancy may be due to differences in the level cutaneous leishmaniasis in Iran. Acta Trop., 160: 44-52. of knowledge of the individuals who complete the pairwise comparative matrix. Gonzalez, C., Wang, O., Strutz, S.E., Gonzalez-Salazar, C., Sanchez-Cordero, V. and Sarkar, S. 2010. Climatic variables had more impact on ZCL than Climate change and risk of leishmaniasis in North environmental factors regarding experts’ ideas. The America: predictions from ecological niche models combination of GIS and AHP represented valuable of vector and reservoir species. PLoS Negl. Trop. information about the areas having potential of the Dis., 4: e585. disease. Analytic Hierarchy Process, a multi-criteria Jahanifard, E., Hanafi-Bojd, A.A., Nasiri, H., Matinfar, decision-making, is an easy and flexible technique, H.R., Charrahy, Z., Abai, M.R., Yaghoobi-Ershadi, which is able to prioritize criteria and sub-criteria M.R. and Akhavan, A.A. 2019. Prone Regions of easily and make the best decision. The role of the Zoonotic Cutaneous Leishmaniasis in Southwest of ZCL risk map is highlighting the areas where are Iran: Combination of Hierarchical Decision Model more sensitive or appropriate in the presence of (AHP) and GIS. J. Arthropod Borne Dis., 3: 310-23. vector and reservoir and subsequently the disease Keola, S., Tokunaga, M., Tripathi, N.K. and Wisa, incidence. The first step in preventing and controlling W. 2002. Spatial Surveillance of Epidemiological the disease is to identify the areas at risk for policy Disease, A Case Study in Ayutthaya Province. makers to know about the disease status in the Thailand: GIS Development Magazine, 6: 41-44. regions. It should be noted that, raising knowledge and attitude of people who are living in the risk Khademvatan, S., Salmanzadeh, S., Foroutan-Rad, M., Bigdeli, Sh., Hedayati-Rad, F., Saki, J. and regions through holding seminar and workshop can Heydari-Gorji, E. 2017. Spatial distribution and be effective in reducing incidence of ZCL. epidemiological features of cutaneous leishmaniasis in southwest of Iran. Alexandria Med. J., 53: 93-98. ACKNOWLEDGEMENTS Maraghi, Sh., Mardanshah, O., Rafiei, A., Samarbafzadeh, The authors thank the Health Department of A. and Vazirianzadeh, B. 2013. Identification of Khuzestan for providing the CL data. This study cutaneous leishmaniasis agents in four geographical was financially supported by Social Determinants regions of Khuzestan province using Nested PCR. of Health Research Center, Ahvaz Jundishapur Jundishapur J. Microbiol., 6: e4866. University of Medical Sciences, Project No. SDH- Marinoni, O. 2004. Implementation of the analytical 9517 and Ethical Code IR.AJUMS.REC.1395.624. hierarchy process with VBA in ArcGIS. Comput. Geosci., 30: 637-46. REFERENCES Mohebali, M. 2013. Visceral leishmaniasis in Iran: review Ali-Akbarpour, M., Mohammadbeigi, A., Tabatabaee, of the epidemiological and clinical features. Iran J. S.H. and Hatam, G. 2012. Spatial analysis of Parasitol., 8: 348-58. eco-environmental risk factors of cutaneous leishmaniasis in southern Iran. J. Cutan. Aesthet. Mollalo, A. and Khodabandehloo, E. 2016. Zoonotic Surg., 5: 30-35. cutaneous leishmaniasis in northeastern Iran: A

321 Journal of Entomological Research, June 2020

GIS-based spatio-temporal multi-criteria decision- Iran-narrative review article. Iran J. Public Health, making approach. Epidemiol. Infect., 144: 2217-29. 44: 299-307. Oryan, A., Alidadi, S. and Akbari, M. 2014. Risk factors Shirzadi, M.R., Mollalo, A. and Yaghoobi-Ershadi, associated with leishmaniasis. Trop. Med. Surg., M.R. 2015. Dynamic relations between incidence 2: e118. of zoonotic cutaneous leishmaniasis and climatic factors in Golestan Province, Iran. J. Arthropod- Ostad, M., Shirian, S., Pishro, F., Abbasi, T., Ai, A. and Borne Dis., 9: 148-60. Azimi, F. 2016. Control of cutaneous leishmaniasis using geographic information systems from 2010 Sofizadeh, A., Rassi, Y., Vatandoost, H., Hanafi-Bojd, to 2014 in Khuzestan Province, Iran. PLoS One, A.A., Mollalo, A., Rafizadeh, S. and Akhavan, A.A. 11: e0159546. 2016. Predicting the distribution of Phlebotomus papatasi (diptera: psychodidae), the primary vector Pedrosa, F. and de Alencar Ximenes, R.A. 2009. of zoonotic cutaneous leishmaniasis, in Golestan Sociodemographic and environmental risk factors province of Iran using ecological niche modeling: for American cutaneous leishmaniasis (ACL) in the comparison of MaxEnt and GARP models. J. Med. State of Alagoas, Brazil. Am. J. Trop. Med. Hyg., Entomol., 54: 312-20. 81: 195-201. Spotin, A., Rouhani, S. and Parvizi, P. 2014. The Piroozi, B., Moradi, G., Alinia, C., Mohamadi, P., Gouya, associations of Leishmania major and Leishmania M.M., Nabavi, M., Gharachorloo, F., Khadem Erfan, tropica aspects by focusing their morphological M.B. and Shirzadi, M.R. 2019. Incidence, burden, and molecular features on clinical appearances and trend of cutaneous leishmaniasis over four in Khuzestan province, Iran. Biomed. Res. Int., decades in Iran. Iran J. Public Health, 4: 28-35. pp. 1-13. Rajabi, M., Mansourian, A. and Bazmani, A. 2012. Termeh, S.V. 2018. Cutaneous leishmaniasis susceptibility Susceptibility mapping of visceral leishmaniasis mapping using multi-criteria decision-making based on fuzzy modelling and group decision- techniques analytic hierarchy process (AHP) and making methods. Geospat. Health., 1: 37-50. analytic network process (ANP). J. Res. Environ. Ramezankhani, R., Sajjadi, N., Jozi, S.A. and Shirzadi, Health, 3: 276-87. M.R. 2018. Climate and environmental factors Tsegaw, T., Gadisa, E., Seid, A., Abera, A., Teshome, affecting the incidence of cutaneous leishmaniasis A., Mulugeta, A., Herrero, M., Argaw, D., Jorge, A. in Isfahan, Iran. Environ. Sci. Pollut. Res., 25: and Aseffa, A. 2013. Identification of environmental 11516-26. parameters and risk mapping of visceral Saaty, R.W. 1987. The analytic hierarchy process-what leishmaniasis in Ethiopia by using geographical it is and how it is used. Mathl. Model., 9: 161-76. information systems and a statistical approach. Saaty, T.L. and Vargas, L.G. 1991. Prediction, projection, Geospat. Health., 7: 299-308. and forecasting: applications of the analytic Toumi, A., Chlif, S., Bettaieb, J., Alaya, N.B., Boukthir, hierarchy process in economics, finance, politics, A., Ahmadi, Z.E. and Salah, A.B. 2012. Temporal games and sports. Kluwer Academic Publications. dynamics and impact of climate factors on the Saaty, T.L. 2005. Theory and applications of the analytic incidence of zoonotic cutaneous leishmaniasis in network process: Decision making with benefits, central Tunisia. PLoS Negl. Trop. Dis., 6: e1633. opportunities, costs and risks. Pittsburgh: RWS WHO. 2019. Available from: https://www.who.int/news- Publications. room/fact-sheets/ detail/ leishmaniasis. Saaty, T.L. 2008. Decision making with the analytic Yaghoobi-Ershadi, M.R. 2012. Phlebotomine sand hierarchy process. Int. J. Services Sci., 1: 83-98. flies (Diptera: Psychodidae) in Iran and their role Salomón, O.D., Quintana, M.G., Mastrángelo, A.V. on Leishmania transmission. J. Arthropod Borne and Fernández, M.S. 2012. Leishmaniasis and Dis., 6: 1-17. climate change-case study: Argentina. J. Trop. Zou, L., Miller, S.N. and Schmidmann, E.T. 2006. Med., pp. 1-11. Mosquito larval habitat mapping using remote Sharifi, I., Aflatoonian, M.R., Fekri, A.R., Parizi, M.H., sensing and GIS: Implications of Coalbed methane Afshar, A.A., Khosravi, A., Sharifi, F., Aflatoonian, development and West Nile Virus. J. Med. Entomol., B., Khamesipour, A., Dowlati, Y. and Modabber, 43: 1034-41. F. 2015. A comprehensive review of cutaneous leishmaniasis in Kerman province, southeastern (Received : February 3, 2020; Accepted : June 23, 2020)

322