Application of decision tree for prediction of cutaneous leishmaniasis incidence based on environmental and topographic factors in Isfahan Province, Iran.

Cutaneous Leishmaniasis (CL) is a neglected tropical disease that continues to be a health problem in Iran. Nearly 350 million people are thought to be at risk. We investigated the impact of the environmental factors on CL incidence during the period 2007- 2015 in a known endemic area for this disease in Isfahan Province, Iran. After collecting data with regard to the climatic, topographic, vegetation coverage and CL cases in the study area, a decision tree model was built using the classification and regression tree algorithm. CL data for the years 2007 until 2012 were used for model construction and the data for the years 2013 until 2015 were used for testing the model. The Root Mean Square error and the correlation factor were used to evaluate the predictive performance of the decision tree model. We found that wind speeds less than 14 m/s, altitudes between 1234 and 1810 m above the mean sea level, vegetation coverage according to the normalized difference vegetation index (NDVI) less than 0.12, rainfall less than 1.6 mm and air temperatures higher than 30°C would correspond to a seasonal incidence of 163.28 per 100,000 persons, while if wind speed is less than 14 m/s, altitude less than 1,810 m and NDVI higher than 0.12, then the mean seasonal incidence of the disease would be 2.27 per 100,000 persons. Environmental factors were found to be important predictive variables for CL incidence and should be considered in surveillance and prevention programmes for CL control.

Climate and environmental factors affecting the incidence of cutaneous leishmaniasis in Isfahan, Iran.

Cutaneous leishmaniasis (CL) is a vector-borne human disease caused by Leishmania, a parasite transmitted by sand flies. CL is endemic in the Isfahan Province, Iran. This study was designed to identify the climate and environmental factors associated with CL incidence in Isfahan Province. Data included incident cases of CL, climate, and environmental factors, which were collected across 23 districts of province from 2007 to 2015. Analyses were performed with generalized linear models (GLMs) to fit a function to the relationships between the response and predictors. We used negative binomial regression due to over-dispersed distribution of CL cases. The effects of all seven climate and environmental factors were found to be significant (all p < 0.01), and the model explained 40% of the deviance of CL incidence. There was a positive relation between mean temperature, relative humidity, and slope of area with disease incidence; however, negative association was demonstrated between maximum wind speed, rainfall, altitude, and vegetation cover with CL incidence. Cutaneous leishmaniasis continues to be a widespread challenge, especially in northwestern parts of Iran. Climate and environmental factors should be considered when selecting the most appropriate strategies for preventing and controlling CL.

Spatial analysis of cutaneous leishmaniasis in an endemic area of Iran based on environmental factors.

Leishmaniasis is a parasitic disease caused by different species of protozoan parasites. Cutaneous leishmaniasis (CL) is still a great public health problem in Iran, especially in Isfahan Province. Distribution and abundance of vectors and reservoirs of this disease is affected by different factors such as climatic, socioeconomic and cultural. This study aimed to identify the hotspot areas for CL in Isfahan and assess the relations between the climatic and topographic factors with CL incidence using spatial analysis. We collected data on the total number of CL cases, population at risk, vegetation coverage, altitude and climatic data for each district of the province from 2011 to 2015. Global Moran's Index was used to map clustering of CL cases across districts and the Getis-Ord (Gi*) statistics was used to determine hotspots areas of the disease in Isfahan. We applied overlay analysis to assess the correlation between the climatic and topographic factors with CL incidence. We found the CL distribution significantly clustered (Moran's Index=0.17, P<0.001) with the Ardestan and Aran va Bidgol (P<0.01) districts along with the Naein and Natanz districts (P<0.05) to be strong hotspot areas. Overlay analysis revealed a high incidence of CL in areas with relative humidity of 27-30%, mean temperature of 15-19°C, mean precipitation of 5-20 mm, maximum wind speed about 12-16 m/s and an altitude of 600-1,800 m. Our study showed that spatial analysis is a feasible approach for identifying spatial disease pattern and detecting hotspots of this infectious disease.