Zoonotic cutaneous leishmaniasis in northeastern Iran: a GIS-based spatio-temporal multi-criteria decision-making approach

Abstract

Zoonotic cutaneous leishmaniasis (ZCL) constitutes a serious public health problem in many parts of the world including Iran. This study was carried out to assess the risk of the disease in an endemic province by developing spatial environmentally based models in yearly intervals. To fill the gap of underestimated true burden of ZCL and short study period, analytical hierarchy process (AHP) and fuzzy AHP decision-making methods were used to determine the ZCL risk zones in a Geographic Information System platform. Generated risk maps showed that high-risk areas were predominantly located at the northern and northeastern parts in each of the three study years. Comparison of the generated risk maps with geocoded ZCL cases at the village level demonstrated that in both methods more than 90%, 70% and 80% of the cases occurred in high and very high risk areas for the years 2010, 2011, and 2012, respectively. Moreover, comparison of the risk categories with spatially averaged normalized difference vegetation index (NDVI) images and a digital elevation model of the study region indicated persistent strong negative relationships between these environmental variables and ZCL risk degrees. These findings identified more susceptible areas of ZCL and will help the monitoring of this zoonosis to be more targeted.

Space-time analysis of human brucellosis considering environmental factors in Iran

“…Although they had studied annual average of some climatic features spatially, the results of temporal surveys of this paper were almost supplementary. They are also consistent with the previous findings of Mollalo et al [29] and Mollalo and Khodabandehloo [30] who observed a significant association between vegetation cover and cutaneous leishmaniasis (CL) incidence in Golestan Province of Iran, however, the direction of the correlation varies. And also in other researches performed by Mollalo et al [31] and Sofizadeh et al [32] similar results were derived.…”

A GIS-Based Artificial Neural Network Model for Spatial Distribution of Tuberculosis across the Continental United States

“…Moreover, at the ecological level, factors such as climate, altitude, air pollution, economic level, unemployment rate, and poverty have found significant on TB occurrence [17,18]. One of the major drawbacks of the highly applied traditional statistical models in the study of TB is that these models are often based on several hard-to-meet assumptions [19,20]. This can bias the estimations of TB frequency/ incidence rate [21].…”

A Fuzzy-GA Based Decision Making System for Detecting Damaged Buildings from High-Spatial Resolution Optical Images

“…After extracting textural features, it is necessary to employ a decision making system [43] or a machine learning technique to provide a relation between the extracted features and the damage extent of buildings. In this study, a decision making system based on MFIS and GA is used to provide the mentioned relation.…”