Geographic Information System‐Based Analysis of the Spatial and Spatio‐Temporal Distribution of Zoonotic Cutaneous Leishmaniasis in Golestan Province, North‐East of Iran

Abstract

Zoonotic cutaneous leishmaniasis (ZCL), a vector-borne disease, poses serious psychological as well as social and economic burden to many rural areas of Iran. The main objectives of this study were to analyse yearly spatial distribution and the possible spatial and spatio-temporal clusters of the disease to better understand spatio-temporal epidemiological aspects of ZCL in rural areas of an endemic province, located in north-east of Iran. Cross-sectional survey was performed on 2983 recorded cases during the period of 2010-2012 at village level throughout the study area. Global clustering methods including the average nearest-neighbour distance, Moran's I, general G indices and Ripley's K-function were applied to investigate the annual spatial distribution of the existing point patterns. Presence of spatial and spatio-temporal clusters was investigated using the spatial and space-time scan statistics. For each year, semivariogram analysis and all global clustering methods indicated meaningful persistent spatial autocorrelation and highly clustered distribution of ZCL, respectively. Eight significant spatial clusters, mainly located in north and northeast of the province, and one space-time cluster, observed in northern part of the province and during the period of September 2010-November 2010, were detected. Comparison of the location of ZCL clusters with environmental conditions of the study area showed that 97.8% of cases in clusters were located at low altitudes below 725 m above sea level with predominantly arid and semi-arid climates and poor socio-economic conditions. The identified clusters highlight high-risk areas requiring special plans and resources for more close monitoring and control of the disease.

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

“…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. They carried out a cross sectional study on zoonotic cutaneous leishmaniasis (ZCL) disease and found out that the majority of the ZCL cases were occurred in arid and semi-arid climates.…”

GIS-based spatial modeling of COVID-19 incidence rate in the continental United States

“…Spatial models are critical tools to statistically investigate the geographic relationship between several explanatory variables and disease outbreak (Mollalo et al 2015(Mollalo et al , 2016, such as COVID-19. In this study, we examine a few regressive and autoregressive spatial models to determine how well they can explain variations of COVID-19 in the continental United States based on several environmental, topographic, socioeconomic, behavioral, and demographic factors as explanatory variables.…”

Spatial and temporal distribution of American cutaneous leishmaniasis in Acre state, Brazil

“…Therefore, geographical information systems (GISs) and spatial analysis, in view of their usefulness in terms of understanding and visualizing the epidemiological behaviour of diseases such as leishmaniasis, have been important tools in achieving these objectives both in Brazil [4–6] and in other countries [7–10]. …”