Identifying the Climatic Factors Affecting the Prevalence of Cutaneous Leishmaniosis in Ilam Province, Iran, Using Satellite Imagery

Leishmaniosis is a common parasitic disease in tropical and subtropical areas of the world caused by protozoan of the genus Leishmania and transmitted to human via the bite of the species of sandflies. This is a zoonotic disease subsumed by the World Health Organization (WHO) in the list of 6 important infectious diseases in the world. The population of the insect vector of this disease is influenced by changes in climate, and has a very strong relationship with climatic factors. The current study aimed at verifying and explaining the feasibility and capability of satellite imagery and data to contribute to identifying, analyzing, and explaining the impacts of climatic factors, and its spatial distribution on the prevalence of leishmaniosis in Ilam province, Iran.
For this purpose, the climatic data including air temperature, soil temperature, relative humidity, precipitation, wind speed, and global solar radiation from 8 meteorological synoptic stations along with the NDVI and LST products of the MODIS sensor for a 3-year period, from 2011 to 2013, were gathered and used. To carry out the current study, at first, raster of all the data were created in Arc GIS 10.2; then, the correlation between the average of raster’s pixels of satellite and stationary data with the raster of leishmaniasis prevalence in a seasonal and temporal scale was verified and analyzed, using the Pearson correlation coefficient. At the end, to identify and model the variables affecting the incidence of leishmaniasis, a multivariate regression model of the prevalence of leishmaniosis was designed for the studied area, based on the correlated variables.
The results for the entire studied area in a seasonal temporal scale showed a negative strong correlation between leishmaniasis prevalence and soil temperature, air temperature, and wind speed (with correlation coefficients of -0.760, -0.756, and -0.691, respectively), and a strong positive correlation with relative humidity and precipitation (with correlation coefficients of 0.660 and 0.662, respectively). Also, wind speed was verified as a limiting factor for leishmaniasis prevalence.
The obtained result showed that the remote sensing data, particularly the LST product from MODIS sensor and rainfall data from TRMM satellite, had acceptable capabilities to be replaced with meteorological stationary data and they also helped to explain and identify factors influencing the prevalence and distribution of spatial patterns of the disease.