The changing spatiotemporal patterns of the individual susceptible-infected-symptomatic-treated-recovered epidemic process and the interactions of information/material flows between regions,along with the 2002-2003 Severe Acute Respiratory Syndrome(SARS) epidemiological investigation data in China's Mainland,including three typical locations of individuals(working unit/home address,onset location and reporting unit),are used to define the in-out flow of the SARS epidemic spread.Moreover,the input/output transmission networks of the SARS epidemic are built according to the definition of in-out flow.The spatiotemporal distribution of the SARS in-out flow,spatial distribution and temporal change of node characteristic parameters,and the structural characteristics of the SARS transmission networks are comprehensively and systematically explored.The results show that(1) Beijing and Guangdong had the highest risk of self-spread and output cases,and prevention/control measures directed toward self-spread cases in Beijing should have focused on the later period of the SARS epidemic;(2) the SARS transmission networks in China's Mainland had significant clustering characteristics,with two clustering areas of output cases centered in Beijing and Guangdong;(3) Guangdong was the original source of the SARS epidemic,and while the infected cases of most other provinces occurred mainly during the early period,there was no significant spread to the surrounding provinces;in contrast,although the input/output interactions between Beijing and the other provinces countrywide began during the mid-late epidemic period,SARS in Beijing showed a significant capacity for spatial spreading;(4) Guangdong had a significant range of spatial spreading throughout the entire epidemic period,while Beijing and its surrounding provinces formed a separate,significant range of high-risk spreading during the mid-late period;especially in late period,the influence range of Beijing's neighboring provinces,such as Hebei,was even slightly larger than that
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 w
HU BiSongGONG JianHuaZHOU JiePingSUN JunYANG LiYangXIA YuAbdoul Nasser IBRAHIM
