GIS Based Spatial Modeling to Mapping and Estimation Relative Risk of Different Diseases Using Inverse Distance Weighting (IDW) Interpolation Algorithm and Evidential Belief Function (EBF) (Case study: Minor Part of Kirkuk City, Iraq)

 
 
 
  • Abstract
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  • Abstract


    The health of the individual is one of the most important indicators of good living and quality of life for the community. Therefore, the contribution of developing of public health sector management and monitoring of diseases related to the cultural, economic, and social progress of any society. Moreover, the diseases occur from spatial factors where the distribution and concentration differ in diverse positions. Hence, GIS can be used as a decision support system in order to help the mangers, assess and monitoring of various types of diseases. Thus, this research aims to define a spatial distribution, prediction of risks and analysis of disease hazard areas in Kirkuk city, north east of Iraq using two models evidential belief function (EBF) and Inverse distance weighting (IDW). IDW determines the correlation between conditioning factors and disease occurrence. Consequently, EBF can be used to assess the effect of each class of conditioning factors on diseases occurrence. The result shows that Al-Wasity quarter reports the highest range of the patients who have the blood diseases (D89-D50) in 2017. Contrary, the northern parts of the city and some quarters in the center of the city (Tessen, Bagdad road, Al-Mansor) reflect the lowest range of the patients in blood diseases. Eye diseases (H59-H00) and its accessories have the same spatial distribution. The result also demonstrated that the GIS based spatial techniques is provided a prospect to simplify and measure the epidemic state of different diseases within specific areas(minor part of Kirkuk city), and lay a base to pursue future surveys into the environmental factors responsible for the augmented disease threat.

     

     

  • Keywords


    Spatial analysis; diseases analysis; evidential belief function, empirical Bayes approach.

  • References


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Article ID: 24098
 
DOI: 10.14419/ijet.v7i4.37.24098




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