Spatial-temporal characteristics of epidemic spread in-out flow mUsing SARS epidemic in Beijing as a case study

• Published in: 中国科学：地球科学英文版 2013 (8), p.1380-1397
• Main Author: HU BiSong GONG JianHua ZHOU JiePing SUN Jun YANG LiYang XIA Yu Abdoul Nasser IBRAHIM
• Format: Article
• Language: English
• Subjects: SARS疫情; 信息参数; 北京; 时空演化规律; 时空特征; 案例; 流行病学特点; 网络特性
• ISSN: 1674-7313; 1869-1897

For better detecting the spatial-temporal change mode of individual susceptible-infected-symptomatic-treated-recovered epi- demic progress and the characteristics of information/material flow in the epidemic spread network between regions, the epi- demic 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 characteris- tics 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 dis- perse 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 infor- mation/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 b