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Spatial-temporal characteristics of epidemic spread in-out flow—Using SARS epidemic in Beijing as a case study
For better detecting the spatial-temporal change mode of individual susceptible-infected-symptomatic-treated-recovered epidemic progress and the characteristics of information/material flow in the epidemic spread network between regions, the epidemic spread mechanism of virus input and output was ex...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7104600/ https://www.ncbi.nlm.nih.gov/pubmed/32288762 http://dx.doi.org/10.1007/s11430-012-4479-z |
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author | Hu, BiSong Gong, JianHua Zhou, JiePing Sun, Jun Yang, LiYang Xia, Yu Ibrahim, Abdoul Nasser |
author_facet | Hu, BiSong Gong, JianHua Zhou, JiePing Sun, Jun Yang, LiYang Xia, Yu Ibrahim, Abdoul Nasser |
author_sort | Hu, BiSong |
collection | PubMed |
description | For better detecting the spatial-temporal change mode of individual susceptible-infected-symptomatic-treated-recovered epidemic progress and the characteristics of information/material flow in the epidemic spread network between regions, the epidemic spread mechanism of virus input and output was explored based on individuals and spatial regions. Three typical spatial information parameters including working unit/address, onset location and reporting unit were selected and SARS epidemic spread in-out flow in Beijing was defined based on the SARS epidemiological investigation data in China from 2002 to 2003 while its epidemiological characteristics were discussed. Furthermore, by the methods of spatial-temporal statistical analysis and network characteristic analysis, spatial-temporal high-risk hotspots and network structure characteristics of Beijing outer in-out flow were explored, and spatial autocorrelation/heterogeneity, spatial-temporal evolutive rules and structure characteristics of the spread network of Beijing inner in-out flow were comprehensively analyzed. The results show that (1) The outer input flow of SARS epidemic in Beijing concentrated on Shanxi and Guangdong provinces, but the outer output flow was disperse and mainly includes several north provinces such as Guangdong and Shandong. And the control measurement should focus on the early and interim progress of SARS breakout. (2) The inner output cases had significant positive autocorrelative characteristics in the whole studied region, and the high-risk population was young and middle-aged people with ages from 20 to 60 and occupations of medicine and civilian labourer. (3) The downtown districts were main high-risk hotspots of SARS epidemic in Beijing, the northwest suburban districts/counties were secondary high-risk hotspots, and northeast suburban areas were relatively safe. (4) The district/county nodes in inner spread network showed small-world characteristics and information/material flow had notable heterogeneity. The suburban Tongzhou and Changping districts were the underlying high-risk regions, and several suburban districts such as Shunyi and Huairou were the relatively low-risk safe regions as they carried out minority information/material flow. The exploration and analysis based on epidemic spread in-out flow help better detect and discover the potential spatial-temporal evolutive rules and characteristics of SARS epidemic, and provide a more effective theoretical basis for emergency/control measurements and decision-making. |
format | Online Article Text |
id | pubmed-7104600 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-71046002020-03-31 Spatial-temporal characteristics of epidemic spread in-out flow—Using SARS epidemic in Beijing as a case study Hu, BiSong Gong, JianHua Zhou, JiePing Sun, Jun Yang, LiYang Xia, Yu Ibrahim, Abdoul Nasser Sci China Earth Sci Research Paper For better detecting the spatial-temporal change mode of individual susceptible-infected-symptomatic-treated-recovered epidemic progress and the characteristics of information/material flow in the epidemic spread network between regions, the epidemic spread mechanism of virus input and output was explored based on individuals and spatial regions. Three typical spatial information parameters including working unit/address, onset location and reporting unit were selected and SARS epidemic spread in-out flow in Beijing was defined based on the SARS epidemiological investigation data in China from 2002 to 2003 while its epidemiological characteristics were discussed. Furthermore, by the methods of spatial-temporal statistical analysis and network characteristic analysis, spatial-temporal high-risk hotspots and network structure characteristics of Beijing outer in-out flow were explored, and spatial autocorrelation/heterogeneity, spatial-temporal evolutive rules and structure characteristics of the spread network of Beijing inner in-out flow were comprehensively analyzed. The results show that (1) The outer input flow of SARS epidemic in Beijing concentrated on Shanxi and Guangdong provinces, but the outer output flow was disperse and mainly includes several north provinces such as Guangdong and Shandong. And the control measurement should focus on the early and interim progress of SARS breakout. (2) The inner output cases had significant positive autocorrelative characteristics in the whole studied region, and the high-risk population was young and middle-aged people with ages from 20 to 60 and occupations of medicine and civilian labourer. (3) The downtown districts were main high-risk hotspots of SARS epidemic in Beijing, the northwest suburban districts/counties were secondary high-risk hotspots, and northeast suburban areas were relatively safe. (4) The district/county nodes in inner spread network showed small-world characteristics and information/material flow had notable heterogeneity. The suburban Tongzhou and Changping districts were the underlying high-risk regions, and several suburban districts such as Shunyi and Huairou were the relatively low-risk safe regions as they carried out minority information/material flow. The exploration and analysis based on epidemic spread in-out flow help better detect and discover the potential spatial-temporal evolutive rules and characteristics of SARS epidemic, and provide a more effective theoretical basis for emergency/control measurements and decision-making. Springer Berlin Heidelberg 2012-10-27 2013 /pmc/articles/PMC7104600/ /pubmed/32288762 http://dx.doi.org/10.1007/s11430-012-4479-z Text en © Science China Press and Springer-Verlag Berlin Heidelberg 2012 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Research Paper Hu, BiSong Gong, JianHua Zhou, JiePing Sun, Jun Yang, LiYang Xia, Yu Ibrahim, Abdoul Nasser Spatial-temporal characteristics of epidemic spread in-out flow—Using SARS epidemic in Beijing as a case study |
title | Spatial-temporal characteristics of epidemic spread in-out flow—Using SARS epidemic in Beijing as a case study |
title_full | Spatial-temporal characteristics of epidemic spread in-out flow—Using SARS epidemic in Beijing as a case study |
title_fullStr | Spatial-temporal characteristics of epidemic spread in-out flow—Using SARS epidemic in Beijing as a case study |
title_full_unstemmed | Spatial-temporal characteristics of epidemic spread in-out flow—Using SARS epidemic in Beijing as a case study |
title_short | Spatial-temporal characteristics of epidemic spread in-out flow—Using SARS epidemic in Beijing as a case study |
title_sort | spatial-temporal characteristics of epidemic spread in-out flow—using sars epidemic in beijing as a case study |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7104600/ https://www.ncbi.nlm.nih.gov/pubmed/32288762 http://dx.doi.org/10.1007/s11430-012-4479-z |
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