### INTRODUCTION

*Leishmania major*, is distributed mainly in the central [9] and southwest regions [10], while anthroponotic CL, caused by

*Leishmania tropica*, affects almost all urban areas [11]. The spatial inequality of CL in Iran makes it a threat to public health and poses major challenges to control strategies [12]. Studying the spatial and geographic patterns of CL is important because the components of the chain of infection, including the parasite, host vector, and required environmental conditions, are spatially distributed [13-15]. Although several studies have been published on the epidemiological and spatial patterns of leishmaniasis in the general population in Iran, the spatial distribution of CL among special populations, such as the army and military personnel, has not been adequately studied [16,17]. The army is the most vulnerable group, with the highest incidence of this disease, due to its distinct spatial status [18], and immunologically naive troops are sometimes deployed to endemic areas for training or operational activities [19,20]. Knowledge of the spatial patterns of CL in at-risk populations can help direct control strategies for truly needy areas and reduce the burden of the disease.

### MATERIALS AND METHODS

### Study area and setting

### Study design and data source

*i*are considered as its neighbors.

### Statistical analysis

#### Spatial autocorrelation

#### Spatial mapping

*i*can be calculated through dividing the number of new cases by the number of people residing in area

*i*. An important goal of spatial epidemiology is to compare the incidence rates of a disease in different areas. However, the crude incidence rate does not allow valid and robust comparative inferences across areas because it is not corrected for the number of people at risk or the sex and age structure of each area, and it faces several other methodological issues in a spatial setting. In order to derive valid inferences, the incidence rates must be standardized, and indicators such as the standardized incidence ratio (SIR) must be estimated [29]. In the present study, the incidence rate was corrected only for the population at risk. Since the number of cases in each area follows a Poisson distribution, the SIR is calculated as the ratio of the observed number of incident cases of a disease (

*O*) in the study area

_{i}*i*to the number of cases that would be expected (

*E*) if the study area

_{i}*i*had the standard or total incidence rate.

*E*is calculated as follows:

_{i}*n*is the number of at-risk individuals in area i. Thus, the SIR is calculated as follows:

_{i}*u*, which refers to how the SIR in area

_{i}*i*is shrunk toward the global mean of the study area (the prior distribution of

*u*follows a normal distribution,

_{i}*τ*

_{u}and

*τ*

_{v}are the parameters of precision, and the hyperprior will be defined for them in the next step. We used a gamma (0.01, 0.01) hyperprior for

#### Spatial cluster analysis

*C*is the total number of CL cases,

*c*is the observed number of CL cases within the window,

*E[c]*is the crude expected number of CL cases within the window under the null hypothesis, and

*C-E[c]*is the expected number of CL cases outside the window. The cluster with the maximum log likelihood ratio was taken as the most likely cluster. The maximum likelihood method test was used for deviations from the null hypothesis that the number of CL cases inside and outside of the window would be equal (

*H*vs. the alternative hypothesis

_{o}: θ_{in}=θ_{out}*H*). The statistical significance of the detected clusters was tested using MC hypothesis testing with 999 permutations. SaTScan version 9.4.2 (https://www.satscan.org/) developed by Kulldorff [35] was used for spatial cluster analysis. All cartographic manipulations and displays were performed in ArcGIS version 10.3 (Esri, Redlands, CA, USA).

_{a}: θ_{in}≠θ_{out}