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 netw...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.展开更多
The lofty and extensive Tibetan Plateau has significant mass elevation effect(MEE). In recent years, a great effort has been made to quantify MEE, with the recognition of intra-mountain basal elevation(MBE) as the mai...The lofty and extensive Tibetan Plateau has significant mass elevation effect(MEE). In recent years, a great effort has been made to quantify MEE, with the recognition of intra-mountain basal elevation(MBE) as the main determinant of MEE. In this study, we improved the method of estimating MEE with MODIS and NECP data, by refining temperature laps rate, and dividing MBE plots, and then analyzed the spatio-temporal variation of MEE in the Plateau. The main conclusions include: 1) the highest average annual MEE of the plateau is as high as 11.5488°C in the southwest of the plateau, where exists a high-MEE core and MEE takes on a trend of decreasing from the core to the surrounding areas; 2) in the interior of the plateau, the maximum monthly MEE is 14.1108°C in the highest MBE plot(4934 m) in August; while the minimum monthly MEE appeared primarily in January and February; 3) in the peripheral areas of the plateau, annual mean MEE is relatively low, mostly between 3.0068°C–5.1972°C, where monthly MEE is high in January and December and low in June and July, completely different from the MEE time-series variation in the internal parts of the plateau.展开更多
基金supported by National Natural Science Foundation of China(Grant Nos. 40871181 and 41101369)Key Knowledge Innovative Program of Chinese Academy of Sciences (Grant No. KZCX2-EW-318)+2 种基金Jiangxi Provincial Natural Science Foundation (Grant No. 20114BAB215024)Natural Science Youth Foundation of Jiangxi Provincial Office of Education (Grant No. GJJ11073)Open Foundation of Key Laboratory of Poyang Lake Wetland and Watershed Research,Ministry of Education (Grant No.PK2010001)
文摘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.
基金supported by the Natural Science Foundation of China (Grant Nos.41401111 and 41601091)
文摘The lofty and extensive Tibetan Plateau has significant mass elevation effect(MEE). In recent years, a great effort has been made to quantify MEE, with the recognition of intra-mountain basal elevation(MBE) as the main determinant of MEE. In this study, we improved the method of estimating MEE with MODIS and NECP data, by refining temperature laps rate, and dividing MBE plots, and then analyzed the spatio-temporal variation of MEE in the Plateau. The main conclusions include: 1) the highest average annual MEE of the plateau is as high as 11.5488°C in the southwest of the plateau, where exists a high-MEE core and MEE takes on a trend of decreasing from the core to the surrounding areas; 2) in the interior of the plateau, the maximum monthly MEE is 14.1108°C in the highest MBE plot(4934 m) in August; while the minimum monthly MEE appeared primarily in January and February; 3) in the peripheral areas of the plateau, annual mean MEE is relatively low, mostly between 3.0068°C–5.1972°C, where monthly MEE is high in January and December and low in June and July, completely different from the MEE time-series variation in the internal parts of the plateau.
