广州SARS 流行的空间风险因子 与空间相关性特征

doi:10.11821/xb200809008

Abstract

Abstract:

Most of the traditional epidemiological studies are based on the classic statistical analysis instead of spatial information. Spatial analysis of risk factor and autocorrelation characteristics of epidemic can guide scientific prevention and control measures. Spatio-temporal data of 1277 cases of infected persons in 2003 in Guangzhou are studied. Map of incidence rate based on 1 km×1 km grids is gained by kriging and kernel methods. Nine spatial risk factors, such as population density, traffic net, hospital, shopping mall, school, etc., are explored, results show that these risk factors are significantly correlated to incidence rate of SARS. Strict control measures to these risk factors can effectively prevent and control SARS epidemic. Global and local spatial autocorrelation characteristics are quantitatively measured with Moran's I and LISA statistics. Spatial cluster of incidence rate has experienced a weak-strong-weak process. High-high cluster areas are mainly in the center of Guangzhou city, where have high population density, economically active, and well-developed traffic net. The focus of high-high cluster areas did not transfer in the whole SARS epidemic process. The Government has taken successfully the prevention and control measures to prevent the further spread of SARS; however, the strategy of taking infectors to the nearest hospital contributed to the result that the spread risk has been high in the city centre. SARS incidence emerged in Guangzhou provides a sample for studying SARS and other unexpected new epidemics emerged in urban areas. Spatial autocorrelation analysis of SARS in Guangzhou provides a scientific basis for the emergency plan of the outbreak of SARS or other unexpected new epidemics in urban areas.

Key words: severe acute respiratory syndrome, spatial risk factor, map of incidence rate, Guangzhou

CAO Zhidong1, 2, WANG Jinfeng1, GAO Yige1, 2,HAN Weiguo3, FENG Xiaolei4, ZENG Guang5. Risk Factor s and Autocor r elation Char acter istics on Sever e Acute Respir atory Syndrome in Guangzhou[J].Acta Geographica Sinica, 2008, 63(9): 981-993.

References

[1] Tobler W. A computer movie simulating urban growth in the Detroit Region. Economic Geography, 1970, 46 (2): 234-240.

[2] Goodchild M F. The application of advanced tecnology in assessing environmental impacts. In: Corwin D L and Loague K (eds.). Application of GIS to the Modeling of Non-point Source Pollutants in the Vadose Zone. Soil Science Society of America Journal, 1996, 1-17.

[3] WHO. http://www.who.int/csr/sars/country/2003_05_31/en/index.html, 2003.

[4] Chen K T. SARS in Taiwan. International Journal of Infectious Diseases, 2005, 9(2): 77-85.

[5] Riley S. Transmission dynamics of the etiological agent of SARS in Hong Kong: Impact of public health interventions. Science, 2003, 300(5627): 1961-1966.

[6] Lipsitch M. Transmission dynamics and control of severe acute respiratory syndrome. Science, 2003, 300 (5627):1966-1970.

[7] Donnelly C A. Epidemiological determinants of spread of causal agent of severe acute respiratory syndrome in Hong Kong. Lancet, 2003, 361(9371): 1761-1766.

[8] Leong H N. SARS in Singapore: Predictors of disease severity. Annals Academy of Medicine Singapore, 2006, 35(5): 326-331.

[9] Shi Yaolin. Stochastic dynamic model of SARS spreading. Chinese Science Bulletin, 2003, 13: 1373-1377.
[ 石耀霖. SARS 传染扩散的动力学随机模型. 科学通报, 2003, 13: 1373-1377.]

[10] Yang Fangting, Hou Lihua, Han Jun. Simulation analysis for the SARS epidemiological process in Beijing. Journal of System Simulation, 2003, 15(7): 991-994.
[杨方廷, 侯立华, 韩军. 北京SARS 疫情过程的仿真分析. 系统仿真学报, 2003, 15(7): 991-994.]

[11] Wang Huiwen, Li Dapeng, Long Wen. Evaluation and predictive modeling on epidemic situation of SARS. Journal of Beijing University of Aeronautics and Astronautics (Social Sciences Edition), 2003, 16(2): 1-6.
[ 王惠文, 李大鹏, 龙文. SARS 疫情的状态评估和预测建模研究. 北京航空航天大学学报(社会科学版), 2003, 16(2): 1-6.]

[12] Chen Qizi. Application of SIR Model in forecasting and analyzing for SARS. Journal of Peking University (Health Sciences), 2003, 35: 75-80.
[陈奇志. 随机模型在非典型肺炎预测及疫情分析中的应用. 北京大学学报(医学版), 2003, 35: 75-80.]

[13] Wang J F. Spatial dynamics of an epidemic of severe acute respiratory syndrome in an urban area. Bulletin of the World Health Organization, 2006, 84(12): 965-968.

[14] Meng B. Understanding the spatial diffusion process of severe acute respiratory syndrome in Beijing. Public Health, 2005, 119(12): 1080-1087.

[15] Wang Jinfeng, Meng Bin, Zheng Xiaoying. Analysis on the multi-distribution and the major influencing factors on severe acute respiratory syndrome in Beijing. Chinese Journal of Epidemiology,2005,26(3):164-168.
[ 王劲峰, 孟斌, 郑晓瑛. 北京市2003 年SARS 疫情的多维分布及其影响因素分析. 中华流行病学杂志2005,26(3): 164-168.]

[16] He Jianfeng. Severe acute respiratory syndrome in Guangdong Province of China: Epidemiology and control measures. Chinese Journal of Preventive Medicine, 2003, 37(4): 227-231.
[何剑峰. 广东省严重急性呼吸综合征的流行与控制. 中华预防医学杂志, 2003, 37(4): 227-231.]

[17] Wang Ming. Study on epidemiology for SARS in Guangzhou. China Public Health, 2004, 20(2): 131-133.
[王鸣. 广州市传染性非典型肺炎流行病学分析. 中国公共卫生, 2004, 20(2): 131-133.]

[18] Wang Ming, Du Ling, Zhou Duanhua. Study on the epidemiology and measures for control on severe acute respiratory syndrome in Guangzhou city. Chinese Journal of Epidemiology, 2003, 24(5): 353-357.
[王鸣, 杜琳, 周端华. 广州市传染性非典型肺炎流行病学及预防控制效果的初步研究. 中华流行病学杂志, 2003, 24(5): 353-357.]

[19] Tan Xuhui, Liu Qing, He Jianfeng. Study on the mathematical model for predicting transmission trend of severe acute respiratory syndrome in Guangzhou city. Chinese Journal of Health Statistics, 2006,23(3): 258-263.
[谭旭辉, 柳青, 何剑锋. 广东省SARS 传播趋势的预测模型研究. 中国卫生统计, 2006,23(3): 258-263.]

[20] Yang Zhicong, Du Ling, Wang Ming. Analysis on effect of air pressure and air temperature on outbreak and epidemic of SARS. China Public Health, 2003, 19(9): 1028-1030.
[杨智聪, 杜琳, 王鸣. 气压与气温对SARS 发病流行的影响分析. 中国公共卫生, 2003, 19(9): 1028-1030.]

[21] Bracken I, David M. The generation of spatial population distributions from census censored data. Environment and Planning A, 1989, 21: 537-543.

[22] Lu Anmin, Li Chengming, Lin Zongjian. Spatial distribution of statistical population data. Geomatics and Information Science of Wuhan University, 2002, 27(3): 301-305.
[吕安民, 李成名, 林宗坚. 人口统计数据的空间分布化研究. 武汉大学学报(信息科学版), 2002, 27(3): 301-305.]

[23] Fan Yida, Shi Peijun, Gu Zhihui. A method of data gridding from administration cell to gridding cell. Scientia Geographica Sinica, 2004, 24(1): 105-108.
[范一大, 史培军, 辜智慧. 行政单元数据向网格单元转化的技术方法. 地理科学, 2004, 24(1): 105-108.]

[24] Tian Yongzong, Chen Shupeng, Yue Tianxiang. Simulation of Chinese population density based on land use. Acta Geographica Sinica, 2004, 59(2): 283-292.
[田永中, 陈述彭, 岳天祥. 基于土地利用的中国人口密度模拟. 地理学报, 2004, 59(2): 283-292.]

[25] Liu Jiyuan, Yue Tianxiang, Wang Ying'an. Digital simulation of population density in China. Acta Geographica Sinica, 2003, 58(1): 17-24.
[刘纪远, 岳天祥, 王英安. 中国人口密度数字模拟. 地理学报, 2003, 58(1): 17-24.]

[26] Zhuo Li, Chen Jin, Shi Peijun. Modeling population density of China in 1998 based on DMSP/OLS nighttime light image. Acta Geographica Sinica, 2005, 60(2): 266-276.
[卓莉, 陈晋, 史培军. 基于夜间灯光数据的中国人口密度模拟. 地理学报, 2005, 60(2): 266-276.]

[27] Liao Yilan, Wang Jinfeng, Meng Bin. A method of spatialization of statistical population. Acta Geographica Sinica,2007, 62(10): 1110-1119.
[ 廖一兰, 王劲峰, 孟斌. 人口统计数据空间化的一种方法. 地理学报, 2007, 62(10): 1110-1119.]

[28] Cliff A, Ord J K. Spatial Autocorrelation. London: Pion, 1973.

[29] Fisher M H, Scholten J. Spatial Analytical Perspectives on GIS. London: Taylor & Francis, 1996.

[30] Anselin L. Local indicators of spatial association (LISA). Geographical Analysis, 1995, 27(2): 93-115.

[31] Haining R P. Spatial Data Analysis: Theory and Practice. Cambridge: Cambridge University, 2003.

[32] Ord J K, Getis A. Local spatial autocorrelation statistics: Distributional issues and application. Geographical Analysis, 1995, 27(4): 286-306.

[33] Moran P. The interpretation of statistical maps. Journal of the Royal Statistical Society (Series B), 1948, 10: 243-251.

[34] Wang Jinfeng. Spatial Analysis. Beijing: Science Press, 2006.
[王劲峰. 空间分析. 北京: 科学出版社, 2006.]

Risk Factor s and Autocor r elation Char acter istics on Sever e Acute Respir atory Syndrome in Guangzhou

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