Risk Mapping and Spatial Modeling of Human Cystic Echinococcosis in Iran From 2009 to 2018: A Gis-Based Survey Publisher



Parandin F¹ ; Hanafibojd AA² ; Heydarpour F³ ; Mohebali M^{1, 4} ; Zeinali M⁵ ; Sari AA⁶ ; Rezaei M⁷ ; Rokni MB^{1, 4} Show Affiliations
Source: Iranian Journal of Parasitology Published:2022

Abstract

Background: Cystic echinococcosis (CE) is one of the most important parasitic infections in subgroup seven common neglected diseases of humans and animals. It is in the list of 18 neglected tropical diseases of the WHO. We aimed to analyze the situation of the disease in Iran using Geographical Information System (GIS) and satellite data analysis. Methods: The data obtained from the Ministry of Health and Medical Education, Tehran, Iran and other related centers from 2009 to 2018 were analyzed using GIS. Then, the spatial distribution maps of the disease were generated, and the hot spots of the disease in Iran were determined using spatial analysis of ArcGIS10.5 software. Geographically weighted regression (GWR) analysis in ArcGIS10.5 was used to correlate the variables affecting the disease including temperature, relative humidity, normalized different vegetation index (NDVI) and incidence of hydatidosis. Data analysis was performed by Linear regression analysis and SPSS 21 software using descriptive statistics and chi-square test. Results: Zanjan, Khorasan Razavi, North Khorasan, Chaharmahal Bakhtiari, Hamedan, Semnan, and Ardabil provinces were the hot spots of CE. The results of geographical weighted regression analysis showed that in Khorasan Razavi, North Khorasan, Chaharmahal Bakhtiari, Hamedan, Semnan, Ardabil, Zanjan, Qazvin, and Ilam provinces, the highest correlation between temperature, humidity, vegetation density and the incidence of hydatidosis was observed (P<0.001). Conclusion: The use of maps could provide reliable estimates of at-risk populations. Climatic factors of temperature, humidity, NDVI had a greater impact on the probability of hydatidosis. These factors can be an indicator used to predict the presence of disease. Environmental and climatic factors were associated with echinococcosis. © 2022 Parandin et al. Published by Tehran University of Medical Sciences.