Modeling of Environmental Factors Affecting the Prevalence of Zoonotic and Anthroponotic Cutaneous, and Zoonotic Visceral Leishmaniasis in Foci of Iran: A Remote Sensing and GIS Based Study

• Main Authors: Abdolali Golpayegani, Ali Reza Moslem, Amir Ahmad Akhavan, Azam Zeydabadi, Amir Hossein Mahvi, Ahmad Allah-Abadi
• Format: Article
• Language: English
• Published: Tehran University of Medical Sciences 2018-03-01
• Series: Journal of Arthropod-Borne Diseases
• Subjects: Leishmaniasis; GIS; Remote sensing; Environmental variables; Nonlinear regression
• Online Access: https://jad.tums.ac.ir/index.php/jad/article/view/817
• ISSN: 1735-7179; 2322-2271

Background: Leishmaniasis is a re-emerging serious international public health problem, and both visceral and cu­taneous types of leishmaniasis became important endemic diseases in Iran. In this study, the relationships between environmental factors (vegetation and elevation) and the prevalence of diseases have been investigated. Methods: All international and national online databases were searched by terms such as leishmaniasis, incidence, prevalence and other related words attributed to Iran and published until first quarter of 2015. The developed data­base in Excel, later imported to the ArcMap for spatial analyst and mapping. Afterwards, the software was used for modeling the relationship between the prevalence/incidence and environmental variables (vegetation and elevation) by both linear and nonlinear regression. Results: After mapping the prevalence data from 144 studies, considering non-parametric ANOVA, the tendency of zoonotic visceral leishmaniasis to presence in high elevation and high vegetation was more than Anthroponotic and zoonotic cutaneous leishmaniasis. While linear regression showed weaker results for modeling, however, additive nonparametric regression analysis suggested that 10km buffers for elevation, and 10 as well as 50km buffers for veg­etation could contribute in better fitness in modeling of these variables. Conclusion: The detailed maps for distribution of disease concluded. The nonlinear regression is a reliable predictor of the relationship between environmental factors and disease incidence, although more and wide researchers are needed to confirm it.