| 北京地区SARS发病的气象流行病学研究 |
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| 引用本文： | 袁劲松,貟洪敏,蓝薇,刘颜,于洁,刘晓平,贾少微,房家智,王嵬.北京地区SARS发病的气象流行病学研究[J].中国科学院研究生院学报,2005,1(5):579-588. |
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| 作者姓名： | 袁劲松 貟洪敏 蓝薇 刘颜 于洁 刘晓平 贾少微 房家智 王嵬 |
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| 作者单位： | 1. 北京大学深圳医院,深圳,518036
2. 北京大学深圳医院,深圳,518036;中国科学院研究生院生物学系,北京,100049;Centre for Human Genetic,Edith Cowan University,Australia |
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| 基金项目： | 深圳市福田区科技局公益性科研项目(2003-11-28)，北京大学深圳医院SARS专项攻关基金(2003-1)资助 |
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| 摘 要： | 本文探索北京地区SARS发病例数与气象因子间的相关关系，建立关键气象因子与发病例数间的数学模型，并进行SARS疫情气象危险度预测和报警分级。应用SPSS统计软件，将SARS发病例数与998个气象因子进行双变量相关分析，再将密切相关的气象因子与发病例数进行多元线性回归分析，用逐步回归法求出回归方程。相关分析表明，SARS发病与前期气象因子相关程度由大到小排列依次为：平均相对湿度、气温（最低气温、最高气温、平均气温）、平均风速、平均降水量、平均气压、平均云量、平均日较差；其中与平均相对湿度、气温、平均降水量、平均云量为负相关，与平均风速、平均气压、平均日较差为正相关；逐步回归法筛选出回归方程为：Y=218.692 – 0.698X630 – 2.043X716 + 2.282X921，决定系数R2 =0.847；建立了SARS发病气象危险度5级预警模型。结论是SARS发病与前期气象因子存在明显的相关关系, SARS的流行特点有季节倾向性；最关键气象因子依次为X630（前第13至第17天平均气温）、X716（前第13至第17天平均相对湿度）、X921（前第9至第13天平均风速）；SARS最易流行的气象条件为：平均气温16.9℃（95% CI 10.7-23.1），平均相对湿度52.2%（33.0-71.4），平均风速2.8m·s-1（2.0-3.6）。
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| 关 键 词： | 相关关系 北京 气象 流行病学 严重急性呼吸综合征 |
| 文章编号： | 1002-1175(2005)05-0579-10 |
| 修稿时间： | 2004年7月1日 |
Epidemiological study of association between climate determinants and spread of severe acute respiratory syndrome (SARS) in Beijing |
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| YUAN Jin-Song,YUN Hong-Min,LAN Wei,LIU Yan,YU Jie,LIU Xiao-Ping,JIA Shao-Wei,FANG Jia-Zhi,WANG Wei.Epidemiological study of association between climate determinants and spread of severe acute respiratory syndrome (SARS) in Beijing[J].Journal of the Graduate School of the Chinese Academy of Sciences,2005,1(5):579-588. |
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| Authors: | YUAN Jin-Song YUN Hong-Min LAN Wei LIU Yan YU Jie LIU Xiao-Ping JIA Shao-Wei FANG Jia-Zhi WANG Wei |
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| Institution: | 1. Peking University Shenzhen Hospital, Shenzhen, 518036, China;
2. Department of Biology, Graduated School, Chinese Academy of Sciences;
3. Centre for Human Genetic, Edith Cowan University, Australia |
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| Abstract: | To determine the relationship between the spread of SARS and climate determinants, the correlations between 998 climate determinants and the clinically diagnosed SARS cases were investigated. Those significant determinants to the spread of SARS were further analyzed using multiple linear regression analysis. Significant correlations were found between the spread of SARS and seven climate determinants. The absolute values of correlation coefficient (r) of the determinants are in the following orders: average relative humidity, temperature, average wind speed, average precipitation, average barometric pressure, average cloudiness, average temperature daily ranges. The spread of SARS is negatively associated with average relative humidity, temperature, average precipitation, average cloudiness, whereas was positively associated with average wind speed, average barometric pressure, average temperature daily range. Multiple linear regression was performed and by reference to the most correlated determinants, an equationY=218.692-0.698X630-2.043X716+2.282X921 (R2=0.847) was established to predict the risk of SARS spread and to set up an alert system. We concluded that there are significant correlations between the climate determinants and the spread of SARS. The most significant climate determinants are the average temperature and average relative humidity from the 13th to 17th days of pre-clinical diagnosis of SARS, and the average wind speed from the 9th to 13th days of pre-diagnosis. The most optimal climate for the spread of SARS is the period with the weather conditions of the average temperature 16.9℃ (95% CI 10.7-23.1),the average relative humidity 52.2% (33.0-71.4), and average wind speed 2.8m·s-1 (2-3.6). |
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| Keywords: | association study Beijing climate epidemics severe acute respiratory syndrome |
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