Spatial and Statistical Analysis of Leptospirosis in Guilan Province, Iran

Nia, A. Mohammadi; Alimohammadi, Abbas; Habibi, Roya; Shirzadi, Mohammad Reza

doi:10.5194/isprsarchives-xl-1-w5-497-2015

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…Spatial autocorrelation can authenticate that spatial data do not follow a random distribution and one or more underlying processes have led to this pattern [13]. Among spatial autocorrelation approaches for identifying spatial patterns and hotspots, Moran's I index is the most popular method capable of detecting explicit outliers [14], and has been utilized in many disciplines such as disease occurrence [15], urban planning and management [16,17], social networks studies [18], and soil pollution [19,20]. For air pollution, Fang et al measured the spatial autocorrelation of AQI at the city level using global and local Moran's I, and estimated the comprehensive impacts and spatial variations of China's urbanization process on air quality [21].…”

Section: Introductionmentioning

confidence: 99%

An Assessment of Spatial Pattern Characterization of Air Pollution: A Case Study of CO and PM2.5 in Tehran, Iran

Habibi

Alesheikh

Mohammadinia

et al. 2017

IJGI

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Statistically clustering air pollution can provide evidence of underlying spatial processes responsible for intensifying the concentration of contaminants. It may also lead to the identification of hotspots. The patterns can then be targeted to manage the concentration level of pollutants. In this regard, employing spatial autocorrelation indices as important tools is inevitable. In this study, general and local indices of Moran's I and Getis-Ord statistics were assessed in their representation of the structural characteristics of carbon monoxide (CO) and fine particulate matter (PM 2.5 ) polluted areas in Tehran, Iran, which is one of the most polluted cities in the world. For this purpose, a grid (200 m × 200 m) was applied across the city, and the inverse distance weighted (IDW) interpolation method was used to allocate a value to each pixel. To compare the methods of detecting clusters meaningfully and quantitatively, the pollution cleanliness index (PCI) was established. The results ascertained a high clustering level of the pollutants in the study area (with 99% confidence level). PM 2.5 clusters separated the city into northern and southern parts, as most of the cold spots were situated in the north half and the hotspots were in the south. However, the CO hotspots also covered an area from the northeast to southwest of the city and the cold spots were spread over the rest of the city. The Getis-Ord's PCI suggested a more polluted air quality than the Moran's I PCI. The study provides a feasible methodology for urban planners and decision makers to effectively investigate and govern contaminated sites with the aim of reducing the harmful effects of air pollution on public health and the environment.

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Section: Introductionmentioning

confidence: 99%

An Assessment of Spatial Pattern Characterization of Air Pollution: A Case Study of CO and PM2.5 in Tehran, Iran

Habibi

Alesheikh

Mohammadinia

et al. 2017

IJGI

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Exploring the Impact of Topographical and Climate Factors on Generation of the Vulnerability-map of Leptospirosis

آهنگرکانی¹,

فرنقی²,

شیرزادی³

2019

jgit

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Leptospirosis is one of the most widespread zoonotic disease caused by Leptospira bacteria. It is found wherever human is in direct or indirect contact with Leptospira bacteria thorough infected animals as well as contaminated soil or water. The disease is mostly found in tropical, subtropical, hot, and humid areas. The main objectives of this study are to investigate the seasonality relations between the topographical and climate factors, including altitude, slope, vegetation, average temperature, average humidity, precipitation and number of freezing days and incidence of Leptospirosis as well as modelling of Leptospirosis using support vector machine at the district level in Northern provinces of Iran. Pearson's correlation analysis was conducted to examine the type and strength of relationships between the topographical and climate variables and Leptospirosis incidence. Results of Pearson's correlation analysis indicate that average humidity, average temperature and rainfall were the most influential environmental factors which as effect on prevalence of Leptospirosis in the study area. Statistical analysis showed that most cases of the Leptospirosis prevalence have been recorded in the late spring and summer. On the other hand, the lowest incidences have occurred in winter. Also, high distribution of leptospirosis mainly located in the central areas of Guilan province, the eastern parts of Mazandaran province and western regions of Golestan province with a mild and humid climate and abundant rainfall. Eventually, performance of support vector machine (SVM) model evaluated by area under the ROC curve. The output maps showed that SVM model has excellent performance in the vulnerability mapping of Leptospirosis.

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Efficiency of Geographically Weighted Regression in Modeling Human Leptospirosis Based on Environmental Factors in Gilan Province, Iran

2017

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It is of little debate that Leptospirosis is verified as the most important zoonosis disease in tropical and humid regions. In North of Iran, maximum reports have been dedicated to Gilan province and it is considered as an endemic problem there. Therefore, modeling or researching about different aspects of it seems indispensable. Hence, this paper investigated various models of Geographically Weighted Regression (GWR) approach and impacts of seven environmental variables on modelling leptospirosis in Gilan. Accordingly, counts of patients were considered as dependent variable during 2009-2011 at village level and environmental variables were utilized as independent variables in the modelling. In addition, performance of two Kernels (Fixed and Adaptive), two Weighting Functions (Bisquare and Gaussian) and three Bandwidth Selection Criteria (AIC (Akaike Information Criterion), CV (Cross Validation) and BIC (Bayesian information criterion)) were compared and assessed in GWR models. Results illustrated: (1) Leptospirosis and effective variables vary locally across the study area (positive and negative); (2) Adaptive kernel in comparison to Fixed kernel, Bisquare weighting function to Gaussian, and also AIC to CV and BIC (due to R 2 and Mean Square Error (MSE) validation criteria); (3) Temperature and humidity were founded as impressive factors (include higher values of coefficients); Finally, contain more reliable results consecutively. However, the provided distribution maps asserted that central villages of Gilan not only are more predisposed to leptospirosis prevalence, but also prevention programs should focus on these regions more than others.

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Spatial and Statistical Analysis of Leptospirosis in Guilan Province, Iran

Cited by 3 publications

References 10 publications

An Assessment of Spatial Pattern Characterization of Air Pollution: A Case Study of CO and PM2.5 in Tehran, Iran

An Assessment of Spatial Pattern Characterization of Air Pollution: A Case Study of CO and PM2.5 in Tehran, Iran

Exploring the Impact of Topographical and Climate Factors on Generation of the Vulnerability-map of Leptospirosis

Efficiency of Geographically Weighted Regression in Modeling Human Leptospirosis Based on Environmental Factors in Gilan Province, Iran

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