Spatial and statistical analyses of the relations between vegetation cover and incidence of cutaneous leishmaniasis in an endemic province, northeast of Iran

Mollalo, Abolfazl; Alimohammadi, Abbas; Shahrisvand, M.; Shirzadi, Mohammad Reza; Malek, Mohammad Reza

doi:10.1016/s2222-1808(14)60500-4

Cited by 31 publications

(12 citation statements)

References 11 publications

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“…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.…”

Section: Discussionsupporting

confidence: 92%

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

Ahmadkhani¹,

Alesheikh²

2017

APJTD

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Section: Discussionsupporting

confidence: 92%

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

Ahmadkhani¹,

Alesheikh²

2017

APJTD

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“…The findings of Mollalo et al . [9, 24], using spatial scan statistics cluster detection technique in this endemic area, illustrated that the most likely spatial clusters were located in northern and northeastern parts of the study area, with arid and semi-arid climates and low vegetation cover, supporting the view that this areas contains potential high-risk populations and warrants closer consideration. However, the methods used in this study to define high-risk areas are more robust than cluster detection methods, which were merely based on case incidence rates due to the fact that results of clustering methods cannot be extrapolated to other areas, while risk factors can be used to identify high-risk zones in other areas where risk factor data are available [25].…”

Section: Discussionmentioning

confidence: 63%

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

Mollalo

Khodabandehloo

2016

Epidemiol. Infect.

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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.

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“…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].…”

Section: Introductionmentioning

confidence: 99%

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

Mollalo

Mao

Rashidi

et al. 2019

IJERPH

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Despite the usefulness of artificial neural networks (ANNs) in the study of various complex problems, ANNs have not been applied for modeling the geographic distribution of tuberculosis (TB) in the US. Likewise, ecological level researches on TB incidence rate at the national level are inadequate for epidemiologic inferences. We collected 278 exploratory variables including environmental and a broad range of socio-economic features for modeling the disease across the continental US. The spatial pattern of the disease distribution was statistically evaluated using the global Moran’s I, Getis–Ord General G, and local Gi* statistics. Next, we investigated the applicability of multilayer perceptron (MLP) ANN for predicting the disease incidence. To avoid overfitting, L1 regularization was used before developing the models. Predictive performance of the MLP was compared with linear regression for test dataset using root mean square error, mean absolute error, and correlations between model output and ground truth. Results of clustering analysis showed that there is a significant spatial clustering of smoothed TB incidence rate (p < 0.05) and the hotspots were mainly located in the southern and southeastern parts of the country. Among the developed models, single hidden layer MLP had the best test accuracy. Sensitivity analysis of the MLP model showed that immigrant population (proportion), underserved segments of the population, and minimum temperature were among the factors with the strongest contributions. The findings of this study can provide useful insight to health authorities on prioritizing resource allocation to risk-prone areas.

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Spatial and statistical analyses of the relations between vegetation cover and incidence of cutaneous leishmaniasis in an endemic province, northeast of Iran

Cited by 31 publications

References 11 publications

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

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

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

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

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