using kriging for statistical disease mapping of pulmonary tuberculosis

Clicks: 140
ID: 172540
2003
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Popular

Ranked #102 of 133 articles by views in 2017 ieee international conference on communication, networks and satellite, comnetsat 2017 - proceedings

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 133 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Introduction: The map of diseases is usually constructed using the information from diseases incidnce in some regions. Some factors, such as measurment error and rapid variation of diseases rates in different regions make maps so wiggly that their interpretation becomes difficult. Therefore these maps must be smoothed using statisical methods. Methods: Since disease rates of different regions reflect an spatial correlation structure, in this paper the spatial correlation structure of data is specified by fitting a variogram model, then kriging as a best linear unbiased prediction method is used to make a smooth map of diseases. Results: The tuberculosis incidence rates of 262 counties of Iran are used to demonstrate the application and accuracy of the diseases mapping method presented in this paper. The smoothing map of tuberculosis disease, obtained by kriging method shows the geographical trend of the disease in Iran. In this map, central and western regions of Iran have minimum incidence rates, and it gradually increases toward the eastern boundaries. Discussion: The object of this article is introducing kriging method for disease mapping and tuberculosis disease is used to demonstrate the application of this method. There is on dubt that the numerical results of prediction and mapping can be affected by undercount in the smir positive (S +) tuberculosis data, which are gathered by the office for campaigning against diseases. However this method has a wide application in different areas of medical sciences. such as geographical epidemiology of diseases, environmental health and environmental engineering.
Reference Key
zade2003journalusing Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;M MOHAMMAD ZADE;ANOUSHIRAVAN KAZEMNEZHAD;S FAGHIHZADEH;Y WAGBEI
Journal 2017 ieee international conference on communication, networks and satellite, comnetsat 2017 - proceedings
Year 2003
DOI
DOI not found
URL
http://journals.mui.ac.ir/jrms/article/view/2986
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.