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Spatial distribution and cluster analysis of dengue using self organizing maps in Andhra Pradesh, India, 2011–2013
BACKGROUND AND OBJECTIVES: Dengue is an emerging and re-emerging infectious disease, transmitted by mosquitoes. It is mostly prevalent in tropical and sub-tropical regions of the world, particularly, in Asia-Pacific region. To understand the epidemiology and spatial distribution of dengue, a retrosp...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5952657/ https://www.ncbi.nlm.nih.gov/pubmed/29774299 http://dx.doi.org/10.1016/j.parepi.2016.11.001 |
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author | Mutheneni, Srinivasa Rao Mopuri, Rajasekhar Naish, Suchithra Gunti, Deepak Upadhyayula, Suryanarayana Murty |
author_facet | Mutheneni, Srinivasa Rao Mopuri, Rajasekhar Naish, Suchithra Gunti, Deepak Upadhyayula, Suryanarayana Murty |
author_sort | Mutheneni, Srinivasa Rao |
collection | PubMed |
description | BACKGROUND AND OBJECTIVES: Dengue is an emerging and re-emerging infectious disease, transmitted by mosquitoes. It is mostly prevalent in tropical and sub-tropical regions of the world, particularly, in Asia-Pacific region. To understand the epidemiology and spatial distribution of dengue, a retrospective surveillance study was conducted in the state of Andhra Pradesh, India during 2011–2013. MATERIAL AND METHODS: District-wise disease endemicity levels were mapped through geographical information system (GIS) tools. Spatial statistical analysis such as Getis-Ord Gi* was performed to identify hot spots and cold spots of dengue disease. Similarly self organizing maps (SOM), a datamining tool was also applied to understand the endemicity patterns in study areas. RESULTS: The analysis shows that districts of Warangal, Karimnagar, Khammam and Vizianagaram are reported as hot spot regions whereas Adilabad and Nizamabad reported as cold spots for dengue. The SOM classify 23 districts in 03 major (07 sub) clusters. These SOM clusters were projected in the geographical space and based on the disease/cases intensity the districts were characterized into low, medium and high endemic areas. CONCLUSION: This visualization approach, SOM-GIS helps the public health officials to identify the disease endemic zones and take real time decisions for disease management. |
format | Online Article Text |
id | pubmed-5952657 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-59526572018-05-17 Spatial distribution and cluster analysis of dengue using self organizing maps in Andhra Pradesh, India, 2011–2013 Mutheneni, Srinivasa Rao Mopuri, Rajasekhar Naish, Suchithra Gunti, Deepak Upadhyayula, Suryanarayana Murty Parasite Epidemiol Control Article BACKGROUND AND OBJECTIVES: Dengue is an emerging and re-emerging infectious disease, transmitted by mosquitoes. It is mostly prevalent in tropical and sub-tropical regions of the world, particularly, in Asia-Pacific region. To understand the epidemiology and spatial distribution of dengue, a retrospective surveillance study was conducted in the state of Andhra Pradesh, India during 2011–2013. MATERIAL AND METHODS: District-wise disease endemicity levels were mapped through geographical information system (GIS) tools. Spatial statistical analysis such as Getis-Ord Gi* was performed to identify hot spots and cold spots of dengue disease. Similarly self organizing maps (SOM), a datamining tool was also applied to understand the endemicity patterns in study areas. RESULTS: The analysis shows that districts of Warangal, Karimnagar, Khammam and Vizianagaram are reported as hot spot regions whereas Adilabad and Nizamabad reported as cold spots for dengue. The SOM classify 23 districts in 03 major (07 sub) clusters. These SOM clusters were projected in the geographical space and based on the disease/cases intensity the districts were characterized into low, medium and high endemic areas. CONCLUSION: This visualization approach, SOM-GIS helps the public health officials to identify the disease endemic zones and take real time decisions for disease management. Elsevier 2016-11-04 /pmc/articles/PMC5952657/ /pubmed/29774299 http://dx.doi.org/10.1016/j.parepi.2016.11.001 Text en © 2016 Published by Elsevier Ltd on behalf of World Federation of Parasitologists. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Mutheneni, Srinivasa Rao Mopuri, Rajasekhar Naish, Suchithra Gunti, Deepak Upadhyayula, Suryanarayana Murty Spatial distribution and cluster analysis of dengue using self organizing maps in Andhra Pradesh, India, 2011–2013 |
title | Spatial distribution and cluster analysis of dengue using self organizing maps in Andhra Pradesh, India, 2011–2013 |
title_full | Spatial distribution and cluster analysis of dengue using self organizing maps in Andhra Pradesh, India, 2011–2013 |
title_fullStr | Spatial distribution and cluster analysis of dengue using self organizing maps in Andhra Pradesh, India, 2011–2013 |
title_full_unstemmed | Spatial distribution and cluster analysis of dengue using self organizing maps in Andhra Pradesh, India, 2011–2013 |
title_short | Spatial distribution and cluster analysis of dengue using self organizing maps in Andhra Pradesh, India, 2011–2013 |
title_sort | spatial distribution and cluster analysis of dengue using self organizing maps in andhra pradesh, india, 2011–2013 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5952657/ https://www.ncbi.nlm.nih.gov/pubmed/29774299 http://dx.doi.org/10.1016/j.parepi.2016.11.001 |
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