Model of cholera dissemination using geographic information systems and fuzzy clustering means: Case study, Chabahar, Iran

Pezeshki, Zahra; Tafazzoli-Shadpour, Mohammad; Mansourian, Ali; Eshrati, Babak; Omidi, E.; Nejadqoli, I.

doi:10.1016/j.puhe.2012.07.002

Cited by 19 publications

(14 citation statements)

References 12 publications

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“…Data of geographic parameters were processed using geographic information system (GIS) and used as inputs of statistical analysis and neural network models (11).…”

Section: Objectivesmentioning

confidence: 99%

“…Geographical parameters included elevation and distances of villages from district health center, rural health centers, national eastern border, the nearest river and subterranean canal. Using geographic information systems, these parameters were quantified through geographical layers as discussed in previous article (11).…”

Section: Data Collectionmentioning

confidence: 99%

“…Since the histograms of independent and dependent variables were not normally distributed, the cholera incidence among villages was binary coded. Using SPSS version 11.5, binary logistic regression and correlate bivariate performed with 95% confidence interval to model influence of climate and geographical parameters in cholera incidence in 10 years period of study (11).…”

Section: Logistic Regression Modeling and Correlate Bivariatementioning

confidence: 99%

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Model of Cholera Forecasting Using Artificial Neural Network in Chabahar City, Iran

Pezeshki¹,

Tafazzoli-Shadpour²,

Nejadgholi³

et al. 2016

Int J Enteric Pathog

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Background: Cholera as an endemic disease remains a health issue in Iran despite decrease in incidence. Since forecasting epidemic diseases provides appropriate preventive actions in disease spread, different forecasting methods including artificial neural networks have been developed to study parameters involved in incidence and spread of epidemic diseases such as cholera. Objectives: In this study, cholera in rural area of Chabahar, Iran was investigated to achieve a proper forecasting model. Materials and Methods: Data of cholera was gathered from 465 villages, of which 104 reported cholera during ten years period of study. Logistic regression modeling and correlate bivariate were used to determine risk factors and achieve possible predictive model onehidden-layer perception neural network with backpropagation training algorithm and the sigmoid activation function was trained and tested between the two groups of infected and non-infected villages after preprocessing. For determining validity of prediction, the ROC diagram was used. The study variables included climate conditions and geographical parameters. Results: After determining significant variables of cholera incidence, the described artificial neural network model was capable of forecasting cholera event among villages of test group with accuracy up to 80%. The highest accuracy was achieved when model was trained with variables that were significant in statistical analysis describing that the two methods confirm the result of each other. Conclusions: Application of artificial neural networking assists forecasting cholera for adopting protective measures. For a more accurate prediction, comprehensive information is required including data on hygienic, social and demographic parameters.

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“…Data of geographic parameters were processed using geographic information system (GIS) and used as inputs of statistical analysis and neural network models (11).…”

Section: Objectivesmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

Section: Logistic Regression Modeling and Correlate Bivariatementioning

confidence: 99%

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Model of Cholera Forecasting Using Artificial Neural Network in Chabahar City, Iran

Pezeshki¹,

Tafazzoli-Shadpour²,

Nejadgholi³

et al. 2016

Int J Enteric Pathog

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“…Geographical information systems (GIS) can be applied for risk mapping and identifying endemic areas for diseases (Elnaiem et al, 2003;Rinaldi et al, 2006;Pezeshki et al, 2012). For example, Bhunia et al (2011) studied the influence of the distribution of inland water bodies on transmission of VL and presence of its dominant vector in a GIS model.…”

Section: Introductionmentioning

confidence: 99%

Environmental modelling of visceral leishmaniasis by susceptibility-mapping using neural networks: a case study in north-western Iran

et al. 2014

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Abstract. Visceral leishmaniasis (VL) is a potentially fatal vector-borne zoonotic disease, which has become an increasing public health problem in the north-western part of Iran. This work presents an environmental health modelling approach to map the potential of VL outbreaks in this part of the country. Radial basis functional link networks is used as a data-driven method for predictive mapping of VL in the study area. The high susceptibility areas for VL outbreaks account for 36.3% of the study area and occur mainly in the north (which may affect the neighbouring countries) and South (which is a warning for other provinces in Iran). These parts of the study area have many nomadic, riverside villages. The overall accuracy of the resultant map was 92% in endemic villages. Such susceptibility maps can be used as reconnaissance guides for planning of effective control strategies and identification of possible new VL endemic areas.

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“…discussion Kwan 2012). Furthermore, distances in epidemiological and demographic research are either defined on interval scale (e.g., Pezeshki et al 2012) or on ordinal scale (using distance intervals, cf. Staedke et al 2003).…”

Section: Impact Of Geographic Context In Demographic Analysesmentioning

confidence: 99%

Importance of the Geocoding Level for Historical Demographic Analyses: A Case Study of Rural Parishes in Sweden, 1850–1914

et al. 2017

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Geocoding longitudinal and individual-level historical demographic databases enables novel analyses of how micro-level geographic factors affected demographic outcomes over long periods. However, such detailed geocoding involves high costs. Additionally, the high spatial resolution cannot be properly utilized if inappropriate methods are used to quantify the geographic factors. We assess how different geocoding levels and methods used to define geographic variables affects the outcome of detailed spatial and historical demographic analyses. Using a longitudinal and individual-level demographic database geocoded at the property unit level, we analyse the effects of population density and proximity to wetlands on all-cause mortality for individuals who lived in five Swedish parishes, 1850-1914. We compare the results from analyses on three detailed geocoding levels using two common quantification methods for each geographic variable. Together with the method selected for quantifying the geographic factors, even small differences in positional accuracy (20-50 m) between the property units and slightly coarser geographic levels heavily affected the results of the demographic analyses. The results also show the importance of accounting for geographic changes over time. Finally, proximity to wetlands and population density affected the mortality of women and children, respectively. However, all possible determinants of mortality were not evaluated in the analyses. In conclusion, for rural historical areas, geocoding to property units is likely necessary for fine-scale analyses at distances within a few hundred metres. We must also carefully consider

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Model of cholera dissemination using geographic information systems and fuzzy clustering means: Case study, Chabahar, Iran

Cited by 19 publications

References 12 publications

Model of Cholera Forecasting Using Artificial Neural Network in Chabahar City, Iran

Model of Cholera Forecasting Using Artificial Neural Network in Chabahar City, Iran

Environmental modelling of visceral leishmaniasis by susceptibility-mapping using neural networks: a case study in north-western Iran

Importance of the Geocoding Level for Historical Demographic Analyses: A Case Study of Rural Parishes in Sweden, 1850–1914

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