Spatial autocorrelation and epidemiological survey of visceral leishmaniasis in an endemic area of Azerbaijan region, the northwest of Iran

Abstract

Visceral leishmaniasis (VL) is a common infectious disease that is endemic in Iran. This study aimed to investigate the spatial autocorrelation of VL in the northwest of Iran. In this cross-sectional study, the data of all patients were collected in 2009-2017 and analyzed by SPSS23 and Moran's and General G Index. The MaxEnt3.3.3 software was used to determine the ecological niche. A big hot spot area was identified in five counties in the northwest of Iran. More than 70% of the cases were reported from these regions, and the incidence rate increased in the northwest of Iran from 2013 to 2017. Seasonal rainfall and average daily temperature were the most important climate variables affecting the incidence of VL in this region (p < 0.05). Therefore, it can be concluded that VL in the northwest of Iran is expanding to new areas along the border with the Republic of Azerbaijan, and the northeastern section of this region is a high-risk area.

Epidemiological Profile and Spatial Patterns of Enterobiasis in Children Aged 3–9 Years in China from 2016 to 2020

“…Global spatial autocorrelation analysis was conducted with the global spatial autocorrelation index in Moran’s I in the ArcGIS(Esri® 10.2, Redlands, CA, USA) software [ 18 , 19 , 20 ], and the spatial clustering of E. vermicularis infection in children was assessed according to the location of elements and attribute values. Hot spot analysis was carried out using Getis-Ord G i * (ArcGIS 10.1, Esri, Redlands, CA, USA) [ 21 , 22 ], and each element in the data set was analyzed to obtain the Z score and P value.…”

Spatio-temporal characteristics and control strategies in the early period of COVID-19 spread: a case study of the mainland China

“…Infectious diseases generally have distribution characteristics of spatial autocorrelation (Adham et al 2020 ; Masinaei et al 2020 ; Ding et al 2021 ). Spatial autocorrelation embodies the distribution law of the agglomeration or dispersion of infectious diseases on the spatial level, which is of great significance to the stage analysis and situation prediction of diseases (Zhang et al 2019 ; Mao et al 2020 ).…”