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The Spatial Analysis on Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China Based on Geographic Information System
BACKGROUND: Hemorrhagic fever with renal syndrome (HFRS) is endemic in mainland China, accounting for 90% of total reported cases worldwide, and Jiangsu is one of the most severely affected provinces. In this study, the authors conducted GIS-based spatial analyses in order to determine the spatial d...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4160164/ https://www.ncbi.nlm.nih.gov/pubmed/25207806 http://dx.doi.org/10.1371/journal.pone.0083848 |
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author | Bao, Changjun Liu, Wanwan Zhu, Yefei Liu, Wendong Hu, Jianli Liang, Qi Cheng, Yuejia Wu, Ying Yu, Rongbin Zhou, Minghao Shen, Hongbing Chen, Feng Tang, Fenyang Peng, Zhihang |
author_facet | Bao, Changjun Liu, Wanwan Zhu, Yefei Liu, Wendong Hu, Jianli Liang, Qi Cheng, Yuejia Wu, Ying Yu, Rongbin Zhou, Minghao Shen, Hongbing Chen, Feng Tang, Fenyang Peng, Zhihang |
author_sort | Bao, Changjun |
collection | PubMed |
description | BACKGROUND: Hemorrhagic fever with renal syndrome (HFRS) is endemic in mainland China, accounting for 90% of total reported cases worldwide, and Jiangsu is one of the most severely affected provinces. In this study, the authors conducted GIS-based spatial analyses in order to determine the spatial distribution of the HFRS cases, identify key areas and explore risk factors for public health planning and resource allocation. METHODS: Interpolation maps by inverse distance weighting were produced to detect the spatial distribution of HFRS cases in Jiangsu from 2001 to 2011. Spatio-temporal clustering was applied to identify clusters at the county level. Spatial correlation analysis was conducted to detect influencing factors of HFRS in Jiangsu. RESULTS: HFRS cases in Jiangsu from 2001 to 2011 were mapped and the results suggested that cases in Jiangsu were not distributed randomly. Cases were mainly distributed in northeastern and southwestern Jiangsu, especially in Dafeng and Sihong counties. It was notable that prior to this study, Sihong county had rarely been reported as a high-risk area of HFRS. With the maximum spatial size of 50% of the total population and the maximum temporal size of 50% of the total population, spatio-temporal clustering showed that there was one most likely cluster (LLR = 624.52, P<0.0001, RR = 8.19) and one second-most likely cluster (LLR = 553.97, P<0.0001, RR = 8.25), and both of these clusters appeared from 2001 to 2004. Spatial correlation analysis showed that the incidence of HFRS in Jiangsu was influenced by distances to highways, railways, rivers and lakes. CONCLUSION: The application of GIS together with spatial interpolation, spatio-temporal clustering and spatial correlation analysis can effectively identify high-risk areas and factors influencing HFRS incidence to lay a foundation for researching its pathogenesis. |
format | Online Article Text |
id | pubmed-4160164 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-41601642014-09-12 The Spatial Analysis on Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China Based on Geographic Information System Bao, Changjun Liu, Wanwan Zhu, Yefei Liu, Wendong Hu, Jianli Liang, Qi Cheng, Yuejia Wu, Ying Yu, Rongbin Zhou, Minghao Shen, Hongbing Chen, Feng Tang, Fenyang Peng, Zhihang PLoS One Research Article BACKGROUND: Hemorrhagic fever with renal syndrome (HFRS) is endemic in mainland China, accounting for 90% of total reported cases worldwide, and Jiangsu is one of the most severely affected provinces. In this study, the authors conducted GIS-based spatial analyses in order to determine the spatial distribution of the HFRS cases, identify key areas and explore risk factors for public health planning and resource allocation. METHODS: Interpolation maps by inverse distance weighting were produced to detect the spatial distribution of HFRS cases in Jiangsu from 2001 to 2011. Spatio-temporal clustering was applied to identify clusters at the county level. Spatial correlation analysis was conducted to detect influencing factors of HFRS in Jiangsu. RESULTS: HFRS cases in Jiangsu from 2001 to 2011 were mapped and the results suggested that cases in Jiangsu were not distributed randomly. Cases were mainly distributed in northeastern and southwestern Jiangsu, especially in Dafeng and Sihong counties. It was notable that prior to this study, Sihong county had rarely been reported as a high-risk area of HFRS. With the maximum spatial size of 50% of the total population and the maximum temporal size of 50% of the total population, spatio-temporal clustering showed that there was one most likely cluster (LLR = 624.52, P<0.0001, RR = 8.19) and one second-most likely cluster (LLR = 553.97, P<0.0001, RR = 8.25), and both of these clusters appeared from 2001 to 2004. Spatial correlation analysis showed that the incidence of HFRS in Jiangsu was influenced by distances to highways, railways, rivers and lakes. CONCLUSION: The application of GIS together with spatial interpolation, spatio-temporal clustering and spatial correlation analysis can effectively identify high-risk areas and factors influencing HFRS incidence to lay a foundation for researching its pathogenesis. Public Library of Science 2014-09-10 /pmc/articles/PMC4160164/ /pubmed/25207806 http://dx.doi.org/10.1371/journal.pone.0083848 Text en © 2014 Liu et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Bao, Changjun Liu, Wanwan Zhu, Yefei Liu, Wendong Hu, Jianli Liang, Qi Cheng, Yuejia Wu, Ying Yu, Rongbin Zhou, Minghao Shen, Hongbing Chen, Feng Tang, Fenyang Peng, Zhihang The Spatial Analysis on Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China Based on Geographic Information System |
title | The Spatial Analysis on Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China Based on Geographic Information System |
title_full | The Spatial Analysis on Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China Based on Geographic Information System |
title_fullStr | The Spatial Analysis on Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China Based on Geographic Information System |
title_full_unstemmed | The Spatial Analysis on Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China Based on Geographic Information System |
title_short | The Spatial Analysis on Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China Based on Geographic Information System |
title_sort | spatial analysis on hemorrhagic fever with renal syndrome in jiangsu province, china based on geographic information system |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4160164/ https://www.ncbi.nlm.nih.gov/pubmed/25207806 http://dx.doi.org/10.1371/journal.pone.0083848 |
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