Spatio-temporal evolution of Beijing 2003 SARS epidemic

Cao, Zhi; Zeng, Da jun; Zheng, Xiaolong; Wang, Quan Yi; Wang, Fei-Yue; Wang, Jinfeng; Wang, Xiao Li

doi:10.1007/s11430-010-0043-x

Cited by 31 publications

(22 citation statements)

References 23 publications

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“…(2) Mathematical statistical analysis of epidemiological data, such as quantitative analysis for temporal individual cases, close contacts and control measures using classical mathematical statistical methods [6,7]. (3) Spatial-temporal statistical analysis of SARS epidemic characteristics, including exploring the distribution patterns of SARS cases in spatial-temporal scales [8], analyzing the spatial autocorrelation and heterogeneity characteristics of SARS epidemic using spatial-temporal statistical methods [9][10][11], and exploring the spatial pattern, temporal process, and driving factors of SARS epidemic using meta models combining multiple spatial-temporal statistical methods [12]. (4) simulation and prediction of SARS epidemic process based on network dynamic models, such as simulating and analyzing the SARS epidemic process using system dynamics and multi-agent system [13,14], and simulating the spread mechanisms based on models of small-world network and scale-free network [15,16].…”

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confidence: 99%

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

Gong

Zhou

et al. 2012

Sci. China Earth Sci.

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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. in-out flow, SARS, Beijing, epidemic spread network, spatial-temporal characteristics, control measurement Citation: Hu B S, Gong J H, Zhou J P, et al. Spatial-temporal characteristics of epidemic spread in-out flow--Using SARS epidemic in Beijing as a case study.

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confidence: 99%

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

Gong

Zhou

et al. 2012

Sci. China Earth Sci.

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“…Spatial interaction exists in the spatio-temporal transmission of an infectious disease [16]. The epidemiological mechanism interacting with socioeconomic factors at different spatial locations determines the variability of the geographical distribution of a disease [17]. The disparities of TB incidence were in uenced by geospatial factors, population and socioeconomic heterogeneity, which will further affect the migrant population [18][19][20][21][22].…”

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confidence: 99%

Modeling tuberculosis transmission flow in China

Wang¹,

Xu²,

Hu³

et al. 2020

Preprint

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Background: China has the largest population and third largest number of tuberculosis cases in the world. Tuberculosis still remains a major public health concern in China. The increasing floating population has become an important part of China’s socioeconomic process and brought the potential risk for infectious disease transmission in the huge population. Both the flow of tuberculosis population in the country and the role of massive floating population in tuberculosis transmission are yet unclear.Methods: 14,027 tuberculosis flow data were derived from the new smear-positive pulmonary tuberculosis cases in China in 2012, provided by the nationwide Infectious Disease Reporting System. Spatial interaction model was used to model the tuberculosis flow and the regional socioeconomic factors.Results: The Pearl River Delta in southern China and the Yangtze River Delta along China’s east coast presented as the largest destination and concentration areas of tuberculosis inflows. Socioeconomic factors were determinants of tuberculosis flow. A 10% increase in per capita GDP was associated with 2.1% decrease in tuberculosis outflows from the provinces of origin, and 0.5% increase in tuberculosis inflows to the destinations and 18.9% increase in intraprovincial flow. Per capita net income of rural households and per capita disposable income of urban households were positively associated with tuberculosis flows. A 10% increase in per capita net income corresponded to 3.6% increase in outflows from the origin, 12.8% increase in inflows to the destinations and 47.9% increase in intraprovincial flows. Tuberculosis incidence had positive impacts on tuberculosis flows. A 10% increase in the number of tuberculosis cases corresponded to 1.1% increase in tuberculosis inflows to the destinations, 2.0% increase in outflows from the origins, and 2.2% increase in intraprovincial flows. A 10% increase in the tuberculosis incidence rate was associated with 9.9% increase in tuberculosis intraprovincial flows. In addition, the tuberculosis flow had a significant spatial dependence and positively affected tuberculosis flows in the neighboring regions.Conclusions: Tuberculosis flows had clear spatial stratified heterogeneity and spatial autocorrelation; regional socio-economic characteristics had different and statistically significant effects on tuberculosis flows in the origin and destination, and income factor played an important role among the determinants.

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“…Their results showed that public health interventions such as early recognition, prompt isolation, and appropriate precautionary measures, could effectively limit spread of the virus. (2) Use of spatial statistics to explore the spatial clustering characteristics of SARS [12][13][14]. For example, some researchers used geostatistic, such as semivariogram, Moran's I and LISA statistics to study the risks of SARS transmission and spatiotemporal evolution in Beijing or Guangzhou.…”

Section: Introductionmentioning

confidence: 99%

Spatial pattern of severe acute respiratory syndrome in-out flow in 2003 in Mainland China

Wang

et al. 2014

BMC Infect Dis

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BackgroundSevere acute respiratory syndrome (SARS) spread to 32 countries and regions within a few months in 2003. There were 5327 SARS cases from November 2002 to May 2003 in Mainland China, which involved 29 provinces, resulted in 349 deaths, and directly caused economic losses of $18.3 billion.MethodsThis study used an in-out flow model and flow mapping to visualize and explore the spatial pattern of SARS transmission in different regions. In-out flow is measured by the in-out degree and clustering coefficient of SARS. Flow mapping is an exploratory method of spatial visualization for interaction data.ResultsThe findings were as follows. (1) SARS in-out flow had a clear hierarchy. It formed two main centers, Guangdong in South China and Beijing in North China, and two secondary centers, Shanxi and Inner Mongolia, both connected to Beijing. (2) “Spring Festival travel” strengthened external flow, but “SARS panic effect” played a more significant role and pushed the external flow to the peak. (3) External flow and its three typical kinds showed obvious spatial heterogeneity, such as self-spreading flow (spatial displacement of SARS cases only within the province or municipality of onset and medical locations); hospitalized flow (spatial displacement of SARS cases that had been seen by a hospital doctor); and migrant flow (spatial displacement of SARS cases among migrant workers). (4) Internal and external flow tended to occur in younger groups, and occupational differentiation was particularly evident. Low-income groups of male migrants aged 19–35 years were the main routes of external flow.ConclusionsDuring 2002–2003, SARS in-out flow played an important role in countrywide transmission of the disease in Mainland China. The flow had obvious spatial heterogeneity, which was influenced by migrants’ behavior characteristics. In addition, the Chinese holiday effect led to irregular spread of SARS, but the panic effect was more apparent in the middle and late stages of the epidemic. These findings constitute valuable input to prevent and control future serious infectious diseases like SARS.Electronic supplementary materialThe online version of this article (doi:10.1186/s12879-014-0721-y) contains supplementary material, which is available to authorized users.

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Spatio-temporal evolution of Beijing 2003 SARS epidemic

Cited by 31 publications

References 23 publications

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

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

Modeling tuberculosis transmission flow in China

Spatial pattern of severe acute respiratory syndrome in-out flow in 2003 in Mainland China

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