传染性非典型肺炎传播规律及其防制研究
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摘要
传染性非典型肺炎(SARS)是一种新发的传染病,阐明其传播规律及其动力是进行有效防制的基础。虽然人们已在这方面进行了大量卓有成效的研究,但是,仍然有很多方面有待于进一步阐明。本研究从五个方面,即SARS潜伏期的精确估计及其影响因素研究、气象因素、空气污染对SARS流行的影响、SARS干预措施效果评价、SARS传播动力学研究、SARS传播的分子机制研究,力图进一步揭示SARS传播的规律、动力、分子机制以及预防控制的规律。研究结果为防制SARS提供了科学依据。
1 材料与方法
在潜伏期研究部分,我们从全国范围内选择了209例有明确接触史的SARS临床诊断病例作为研究对象,通过电话调查其接触史和5种潜伏期可能影响因素(即接触方式、年龄、性别、职业、地区)的情况。最后,我们获得了209个潜伏期观测数据及其相应的影响因素数据。其中,有161个潜伏期数据为区间数据,具有多次暴露史。对于含区间数据的潜伏期估计,目前尚没有现成的方法。本研究建立了一种基于EM算法的极大似然估计方法用于估计潜伏期的分布参数,从而得到潜伏期的均值估计。对于含区间数据潜伏期的影响因素研究,同样也没有现成的方法。我们采用基于EM算法的广义线性模型对含区间数据的潜伏期进行单因子和多因子分析。
论文第二部分的研究目标在于探讨气象因素、空气污染对SARS续发率是否会发生影响,从而探讨其对SARS流行的影响。SARS病例调查数据来源于全国的SARS个案调查数据库,密切接触者调查数据来源于全国的密切接触者调查数据库。气象资料由国家气象中心提供,空气污染指数(API)资料由国家环保局提供。资料分析方法:(1)对各种气象因素、API与续发率之间进行相关与多元回归分析;(2)分别按各种气象因素、API进行单变量的样品聚类,然后分别对各类的续发率均值进行比较;(3)对各种气象因素、API进行多变量的样品聚类,然后对各类的续发率均值进行比较。
论文第三部分的研究目的在于评价中国内地及北京市SARS主要干预措施的效果。SARS每日发病例数来源于全国SARS个案调查数据库,SARS潜伏期分布数据来源于第一部分研究结果。SARS控制措施资料通过查阅有关文件、研究文献以及咨询有关政府官员获得。气象数据由国家气象局提供。为了克服基于疫情报告和发病例数变化的传统评价方法的缺陷,我们采用基于EMS算法的非参数极大似然估计方法构建中国内地及北京市的SARS感染曲线,然后结合各种
主要控制措施实施和落实的情况,以及北京市SARS流行期间的气象条件,直观地解释和评价中国内地及北京市各种控制措施的效果。
在论文第四部分,我们通过建立SARS传播的动力学模型来研究SARS流行的规律及动力,同时对北京市SARS主要干预措施进行定量评价。模型的结构是在充分复习、总结目前传播动力学研究的最新成果的基础上,同时结合对SARS传播规律的了解而提出的。模型参数估计以北京市SARS流行的实际数据和北京市的人口数据为材料进行估计或拟合的。参数拟合估计方法采用Gepasi 3.3中的Genetic algorithm。模型对北京市SARS流行过程的模拟在Vanguard DecisionPro for Windows 4.0.23软件中通过构建SARS传播动力学模型的Markov链模型实现。模拟值与实际值的显著性检验采用配对t检验,在SPSS 11.5上实现。最后,通过15种想定情景研究对北京市主要干预措施进行定量评价。上述研究通过改变Markov链模型有关参数实现。
在论文第五部分,我们通过生物信息学方法对SARS-CoV与宿主相互作用关系进行探讨,从而了解SARS传播的分子机制。研究所需的基因组序列、蛋白质序列均来自NCBI GenBank。SARS-CoV蛋白中可能的caveolin-binding基序搜寻通过DNASIS MAX 1.0软件中的氨基酸基序搜索功能实现。采用分子模拟和分子对接技术来进一步证实上述发现的SARS-CoV蛋白中可能结合caveolin-1的位点。
2 主要结果与结论
第一部分的研究发现,SARS潜伏期服从分布。潜伏期均值和方差的极大似然估计值分别为4.89天(95%CI 4.83-4.91,99%CI 4.81-4.91)和11.40天2;95%的病人在感染SARS-CoV后11.42天内将发病,99%的病人在感染后15.89天内将发病。单因子和多因子分析结果表明,除性别因素以外,接触方式、地区、年龄、职业因素对潜伏期均值估计均有明显的影响。
第二部分的研究发现,日均气压、日均相对湿度、日均气温与SARS续发率呈负相关。日均风速、日均日照时数及API对SARS续发率无显著影响。日均气温、日均气压和日均相对湿度分别为12.3℃～14.4℃、89.2kPa～89.6kPa和38.9%～44.1%时的气象条件较易发生SARS流行。
第三部分的研究发现:(1)广东省在2月3日采取的全面措施是有效的,对控制中国内地第一波疫情起到了关键性的作用;(2)4月19日左右采取的一系列措施,包括4月21日起每日公布疫情、规范疫情报告和病例治疗、加强病例管理和密切接触者检疫以及成立全国防治非典指挥部等,对中国内地第二波疫情控制起了决定性的作用;(3)北京市4月18日开始针对医务人员采取的措施(包括SARS防护知识和技能培训以及防护设备和措施的全面正确使用等)对北京市
疫情控制起了很大作用;(4)4月20日至4月27日期间采取的一组措施,包括每日疫情发布、加大宣传力度、对病例和密切接触者的严格管理(定点医
Severe acute respiratory syndrome (SARS) is a new and emerging infectious disease. To clarify the pattern and dynamics of spread of its causal agent is the base of its effective prevention and control. Although some epidemiolgoical research on this subject has been done, there are still many aspects pending further clarification. Our study tries to further reveal the pattern of spread, transmission dynamics, molecular mechanism, and the prevention and control of SARS from five aspects: the accurate estimation of the incubation period of SARS and its influential factors, the effects of the meteorological condition and air pollution on the epidemic of SARS, the evaluation of intervention measures implemented in the SARS outbreak, the transmission dynamics of SARS, the molecular mechanism of transmission of SARS. Our findings in the study have provided the scientific basis for the prevention and control of SARS.
1 Data and Methods
In the study of the incubation period of SARS, 209 probable cases with definite contact history were selected as the research objects. By telephone investigation, their contact history and five possible factors influencing the incubation period, that is contact pattern, age, sex, profession, and region, were obtained. Of the 209 observation data of the incubation period, 161 data belonged to interval data, which came from multiple contact days. There was no ready-made method for the estimation of the incubation period with interval data. Our study has set up a maximum likelihood estimation method based on EM algorithm, which was applied in estimating the distribution parameter of the incubation period. Then we obtained the estimation of the mean of the incubation period. As well as the estimation of the mean, no ready-made method was found in the study on the influencing factors of the incubation period with interval data. We developed a generalized linear model based on EM algorithm to do univariate and multivariate analysis.
In the second part of the dissertation, our aim was to find whether the meteorological elements and air pollution could affect the secondary attack rate of SARS. The data of SARS probable cases came from the nationwide case study
database of SARS. The data of close contact history came from the nationwide survey data of people with close contact. The State Meteorology Bureau provided the meteorological data and the State Environment Protection Bureau provided the Air Pollution Index (API) data.
The third part of the dissertation aims at the evaluation of the major intervention measures implemented in the SARS outbreak in China mainland and Beijing. The daily incidence of SARS came from nationwide SARS case study database. SARS incubation period distribution data came from the first part of the paper. Descriptive data of control measures were obtained through review of official documents and discussions with officials in MOH. In order to overcome the defects of the traditional evaluation methods based on report cases and incidence cases, we used a non-parameter maximum likelihood estimation method to reconstruct the SARS infectious curve of China mainland and Beijing, linking with the major control measures and the meteorological condition during the period of the spread of SARS in Beijing. By this means, we succeeded to explain and evaluate the effects of all sorts of control measures taken by China mainland and Beijing.
In the fourth part of the paper, we studied the pattern of spread of causal agent of SARS and its transmission dynamics, and evaluated the major intervention measures implemented in Beijing quantitatively by establishing the transmission dynamics model of SARS. The model structure was founded on the basis of the latest research results about transmission dynamics and the knowledge of the epidemiological determinants of spread of casual agent of SARS. Genetic algorithm was employed to parameter estimation. Modeling epidemic process of SARS in Beijing was fulfilled in Markov chain model of transmission dynamics of SARS by using Vanguard Deci
1 材料与方法
在潜伏期研究部分,我们从全国范围内选择了209例有明确接触史的SARS临床诊断病例作为研究对象,通过电话调查其接触史和5种潜伏期可能影响因素(即接触方式、年龄、性别、职业、地区)的情况。最后,我们获得了209个潜伏期观测数据及其相应的影响因素数据。其中,有161个潜伏期数据为区间数据,具有多次暴露史。对于含区间数据的潜伏期估计,目前尚没有现成的方法。本研究建立了一种基于EM算法的极大似然估计方法用于估计潜伏期的分布参数,从而得到潜伏期的均值估计。对于含区间数据潜伏期的影响因素研究,同样也没有现成的方法。我们采用基于EM算法的广义线性模型对含区间数据的潜伏期进行单因子和多因子分析。
论文第二部分的研究目标在于探讨气象因素、空气污染对SARS续发率是否会发生影响,从而探讨其对SARS流行的影响。SARS病例调查数据来源于全国的SARS个案调查数据库,密切接触者调查数据来源于全国的密切接触者调查数据库。气象资料由国家气象中心提供,空气污染指数(API)资料由国家环保局提供。资料分析方法:(1)对各种气象因素、API与续发率之间进行相关与多元回归分析;(2)分别按各种气象因素、API进行单变量的样品聚类,然后分别对各类的续发率均值进行比较;(3)对各种气象因素、API进行多变量的样品聚类,然后对各类的续发率均值进行比较。
论文第三部分的研究目的在于评价中国内地及北京市SARS主要干预措施的效果。SARS每日发病例数来源于全国SARS个案调查数据库,SARS潜伏期分布数据来源于第一部分研究结果。SARS控制措施资料通过查阅有关文件、研究文献以及咨询有关政府官员获得。气象数据由国家气象局提供。为了克服基于疫情报告和发病例数变化的传统评价方法的缺陷,我们采用基于EMS算法的非参数极大似然估计方法构建中国内地及北京市的SARS感染曲线,然后结合各种
主要控制措施实施和落实的情况,以及北京市SARS流行期间的气象条件,直观地解释和评价中国内地及北京市各种控制措施的效果。
在论文第四部分,我们通过建立SARS传播的动力学模型来研究SARS流行的规律及动力,同时对北京市SARS主要干预措施进行定量评价。模型的结构是在充分复习、总结目前传播动力学研究的最新成果的基础上,同时结合对SARS传播规律的了解而提出的。模型参数估计以北京市SARS流行的实际数据和北京市的人口数据为材料进行估计或拟合的。参数拟合估计方法采用Gepasi 3.3中的Genetic algorithm。模型对北京市SARS流行过程的模拟在Vanguard DecisionPro for Windows 4.0.23软件中通过构建SARS传播动力学模型的Markov链模型实现。模拟值与实际值的显著性检验采用配对t检验,在SPSS 11.5上实现。最后,通过15种想定情景研究对北京市主要干预措施进行定量评价。上述研究通过改变Markov链模型有关参数实现。
在论文第五部分,我们通过生物信息学方法对SARS-CoV与宿主相互作用关系进行探讨,从而了解SARS传播的分子机制。研究所需的基因组序列、蛋白质序列均来自NCBI GenBank。SARS-CoV蛋白中可能的caveolin-binding基序搜寻通过DNASIS MAX 1.0软件中的氨基酸基序搜索功能实现。采用分子模拟和分子对接技术来进一步证实上述发现的SARS-CoV蛋白中可能结合caveolin-1的位点。
2 主要结果与结论
第一部分的研究发现,SARS潜伏期服从分布。潜伏期均值和方差的极大似然估计值分别为4.89天(95%CI 4.83-4.91,99%CI 4.81-4.91)和11.40天2;95%的病人在感染SARS-CoV后11.42天内将发病,99%的病人在感染后15.89天内将发病。单因子和多因子分析结果表明,除性别因素以外,接触方式、地区、年龄、职业因素对潜伏期均值估计均有明显的影响。
第二部分的研究发现,日均气压、日均相对湿度、日均气温与SARS续发率呈负相关。日均风速、日均日照时数及API对SARS续发率无显著影响。日均气温、日均气压和日均相对湿度分别为12.3℃～14.4℃、89.2kPa～89.6kPa和38.9%～44.1%时的气象条件较易发生SARS流行。
第三部分的研究发现:(1)广东省在2月3日采取的全面措施是有效的,对控制中国内地第一波疫情起到了关键性的作用;(2)4月19日左右采取的一系列措施,包括4月21日起每日公布疫情、规范疫情报告和病例治疗、加强病例管理和密切接触者检疫以及成立全国防治非典指挥部等,对中国内地第二波疫情控制起了决定性的作用;(3)北京市4月18日开始针对医务人员采取的措施(包括SARS防护知识和技能培训以及防护设备和措施的全面正确使用等)对北京市
疫情控制起了很大作用;(4)4月20日至4月27日期间采取的一组措施,包括每日疫情发布、加大宣传力度、对病例和密切接触者的严格管理(定点医
Severe acute respiratory syndrome (SARS) is a new and emerging infectious disease. To clarify the pattern and dynamics of spread of its causal agent is the base of its effective prevention and control. Although some epidemiolgoical research on this subject has been done, there are still many aspects pending further clarification. Our study tries to further reveal the pattern of spread, transmission dynamics, molecular mechanism, and the prevention and control of SARS from five aspects: the accurate estimation of the incubation period of SARS and its influential factors, the effects of the meteorological condition and air pollution on the epidemic of SARS, the evaluation of intervention measures implemented in the SARS outbreak, the transmission dynamics of SARS, the molecular mechanism of transmission of SARS. Our findings in the study have provided the scientific basis for the prevention and control of SARS.
1 Data and Methods
In the study of the incubation period of SARS, 209 probable cases with definite contact history were selected as the research objects. By telephone investigation, their contact history and five possible factors influencing the incubation period, that is contact pattern, age, sex, profession, and region, were obtained. Of the 209 observation data of the incubation period, 161 data belonged to interval data, which came from multiple contact days. There was no ready-made method for the estimation of the incubation period with interval data. Our study has set up a maximum likelihood estimation method based on EM algorithm, which was applied in estimating the distribution parameter of the incubation period. Then we obtained the estimation of the mean of the incubation period. As well as the estimation of the mean, no ready-made method was found in the study on the influencing factors of the incubation period with interval data. We developed a generalized linear model based on EM algorithm to do univariate and multivariate analysis.
In the second part of the dissertation, our aim was to find whether the meteorological elements and air pollution could affect the secondary attack rate of SARS. The data of SARS probable cases came from the nationwide case study
database of SARS. The data of close contact history came from the nationwide survey data of people with close contact. The State Meteorology Bureau provided the meteorological data and the State Environment Protection Bureau provided the Air Pollution Index (API) data.
The third part of the dissertation aims at the evaluation of the major intervention measures implemented in the SARS outbreak in China mainland and Beijing. The daily incidence of SARS came from nationwide SARS case study database. SARS incubation period distribution data came from the first part of the paper. Descriptive data of control measures were obtained through review of official documents and discussions with officials in MOH. In order to overcome the defects of the traditional evaluation methods based on report cases and incidence cases, we used a non-parameter maximum likelihood estimation method to reconstruct the SARS infectious curve of China mainland and Beijing, linking with the major control measures and the meteorological condition during the period of the spread of SARS in Beijing. By this means, we succeeded to explain and evaluate the effects of all sorts of control measures taken by China mainland and Beijing.
In the fourth part of the paper, we studied the pattern of spread of causal agent of SARS and its transmission dynamics, and evaluated the major intervention measures implemented in Beijing quantitatively by establishing the transmission dynamics model of SARS. The model structure was founded on the basis of the latest research results about transmission dynamics and the knowledge of the epidemiological determinants of spread of casual agent of SARS. Genetic algorithm was employed to parameter estimation. Modeling epidemic process of SARS in Beijing was fulfilled in Markov chain model of transmission dynamics of SARS by using Vanguard Deci
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Shao J, Tu D. The Jackknife and Bootstrap. Springer-Verlag New York, Inc. 1995.
McCullagh P, Nelder JA. Generalized Linear Models. Chapman and Hall, 1989.
Bates DV, Baker-Anderson M, Sizto R. Asthma attack periodicity: a study of hospital emergency visits in Vancouver. Environ Res 1990; 51: 51-70.
Schwarta J, Dockery DW. Particulate air pollution and daily mortality in Steubenville, Ohio. Am J Epidemiol 1992; 135: 12-9.
Dockery DW, Pope CA. Acute respiratory effects of particulate air pollution. Annu Rev Public Health 1994; 15: 107-32.
Schwartz J, Slater D, Larson TV, et al. Particulate air pollution and hospital emergency room visits for asthma in Seattle. Am Rev Respir Dis 1993; 147: 826-31.
李青春,陆晨,刘彦,等. 北京地区呼吸道疾病与气象条件关系的分析. 气象 1999; 25(3): 8-12.
赵杰夫,吕中科,赵燕妮,等. 流行性感冒与气象条件的关系初探. 湖北气象 1998; 2: 17-8.
张彩平,顾盈华,李楠. 气象专家分析发现气温17-28℃最易于SARS流行. http://news.xinhuanet.com/st/2003-09/27/content_1103502.htm (accessed September 27, 2003).
王瑾,仇逸. 研究发现:气象因素与SARS流行关联密切. http://news.sohu.com/53/77/news213297753.shtml (accessed September 17, 2003).
杨智聪,杜琳,王鸣,等. 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): 1028-30.
王铮,蔡砥,李山,等. 中国SARS流行的季节性风险探讨. 地理研究 2003; 22(5): 541-50.
叶殿秀,杨贤为,张强. 北京地区SARS与气象条件关系分析. 气象 2003; 29(10): 42-5.
叶玲,黄超光. SARS传播与空气质量相关性研究. 军医进修学院学报 2003; 24(4): 295-8.
WHO. Guidelines, recommendations, descriptions: First data on stability and resistance of SARS coronavirus compiled by members of WHO laboratory network. http://www.who.int/csr/sars/survival_2003_05_04/en/index.html (accessed May 4, 2003).
李敬云,鲍作义,刘思扬,等. SARS病毒在外界环境物品中生存和抵抗力的研究. 中国消毒学杂志 2003; 20(2): 110.
鲍作义,刘永健,刘思扬,等. SARS病毒对温度耐受性的实验研究. 中国消毒学杂志 2003; 20(3): 161-2.
Cui Y, Zhang ZF, Froines J, et al. Air pollution and case fatality of SARS in the People’s Republic of China: an ecologic study. Environmental Health: A Global Access Science Source 2003; 2: 15.
中华人民共和国卫生部. http://www.moh.gov.cn/ (accessed October 1, 2003).
王鸣,杜琳,周端华,等. 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): 353-57.
梁万年. 北京市SARS干预措施的效果效益评价. 预防医学文献信息 2003; 9(6): 766-8.
程锦泉,周俊安,周丽. 深圳市抗击“非典”实施策略及效果分析. 中国公共卫生 2003; 19(9): 1031-2.
Chan-Yeung M, Yu WC. Outbreak of severe acute respiratory syndrome in Hong Kong Special Administrative Region: case report. BMJ 2003; 326: 850-2.
Brookmeyer R, Gail MH. Minimum size of the acquired immunodeficiency syndrome (AIDS) epidemic in the United States. Lancet 1986; 11: 1320-2.
Becker NG, Watson LF, Carlin JB. A method of non-parametric back-projection and its application to AIDS data. Stat Med 1991; 10(10): 1527-42.
Chau PH, Yip PS.F, Cui JS. Reconstructing the incidence of human immunodeficiency virus (HIV) in Hong Kong by using data from HIV positive tests and diagnoses of acquired immune deficiency syndrome. Appl Statist 2003; 52: 237-48.
Cui JS, Becker NG. Estimating HIV incidence using dates of both HIV and AIDS diagnoses. Stat Med 2000; 19: 1165-77.
Dempster AP, Laird NM, Rubin, DB. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society (Series B) 1977; 39(1): 1-38.
Silverman BW, Jones MC, Wilson JD, et al. A smoothed EM approach to indirect estimation problems, with particular reference to stereology and emission tomography. Journal of the Royal Statistical Society (Series B) 1990; 52: 271-324.
姜庆五主编. 流行病学. 科学出版社 2003; 174-88.
Anderson RM, May RM. Infectious diseases of humans: dynamics and control. Oxford University Press 1991.
吴开琛,吴开录,陈文江,等. SARS传播数学模型与流行趋势预测研究. 中国热带医学 2003; 3(4): 421-5.
方北香. 传染病动力学常微分方程模型解的整体存在唯一性. 复旦学报(自然科学版) 2001; 40(6): 640.
张双德,郝海,张喜红. 一类含有潜伏期的传染病动力学模型. 数理医药学杂志 2002; 15(5): 385-6.
Lloyd AL. Realistic distributions of infectious periods in epidemic models: changing patterns of persistence and dynamics. Theoretical Population Biology
2001; 60: 59-71.
Medlock J, Kot M. Spreading disease: integro-differential equations old and new. Mathematical Biosciences 2003; 184(2): 201-22.
Daley DJ, Gani JM. Epidemic modeling: an introduction. Cambridge University Press 1999.
Ball F, Neal P. A general model for stochastic SIR epidemics with two levels of mixing. Mathematical Biosciences 2002; 180: 73-102.
O’Neill PD. A tutorial introduction to Bayesian inference for stochastic epidemic models using Markov chain Monte Carlo methods. Mathematical Biosciences 2002; 180: 103-114.
Dye C, Gay N. Modeling the SARS epidemic. Science 2003; 300: 1884-5.
Drazen JM. Case clusters of the severe acute respiratory syndrome. N Engl J Med 2003; 348: e6-7.
杨凤立. 科研单位最新调查显示四成儿童体内有SARS抗体. 新华网 http://www.xinhuanet.com/newscenter/gnzxbb.htm (accessed July 3, 2003)
Compton SR, Barthold SW, Smith AL. The cellular and molecular pathogenesis of coronaviruses. Lab Anim Sci 1993; 43: 15-20.
Haijema BJ, Volders H, Rottier PJ. Switching species tropism an effective way to manipulate the feline coronavirus genome. J Virol 2003; 77: 4528-38.
Ng TW, Turinici G, Danchin A. A double epidemic model for the SARS propagation. BMC Infectious Diseases 2003; 3: 19.
Moles CG, Mendes P, Banga JR. Parameter estimation in biochemical pathways: a comparison of global optimization methods. Genome Research 2003; 13: 2467-74.
http://www.gepasi.org Gepasi biochemical simulation package.
http://www.vanguardsw.com/updates/decisionpro40/ Vanguard DecisionPro for Windows
Ksiazek TG, Erdman D, Goldsmith CS, Zaki SR, Peret T, Emery S, et al. A novel coronavirus associated with severe acute respiratory syndrome. N Engl J Med 2003; 348:1947-58.
Mukherjee S, Ghosh RN, Maxfield FR. Endocytosis. Physiol Rev 1997; 77: 759-803.
Yu XJ, Luo C, Lin JC, Hao P, He YY, Guo ZM, et al. Putative hAPN receptor binding sites in SARS_CoV spike protein. Acta Pharmacol Sin 2003; 24: 481-8.
Bergelson JM, John NS, Kawaguchi S, Chan M, Stubdal H, Modlin J, et al. Infection by echoviruses 1 and 8 depends on the alpha 2 subunit of human VLA-2.
J Virol 1993; 67: 6847-52.
Davis GE, Camarillo CW. An alpha 2 beta 1 integrin-dependent pinocytic mechanism involving intracellular vacuole formation and coalescence regulates capillary lumen and tube formation in three-dimensional collagen matrix. Exp Cell Res 1996; 224: 39-51.
DeTulleo L, Kirchhausen T. The clathrin endocytic pathway in viral infection. EMBO J 1998; 17: 4585-93.
Anderson HA, Chen Y, Norkin LC. MHC class I molecules are enriched in caveolae but do not enter with simian virus 40. J Gen Virol 1998; 79: 1469-77.
Smart EJ, Graf GA, McNiven MA, Sessa WC, Engelman JA, Scherer PE, et al. Caveolins, Liquid-Ordered Domains, and Signal Transduction. Mol Cell Biol 1999; 19: 7289-304.
Verkade SP, Virta FM, Simons K, Ikonen E. Caveolin-1 and -2 in the Exocytic Pathway of MDCK Cells. J Cell Biol 1998; 140: 795-806.
Parton RG. Caveolae and caveolins. Curr Opin Cell Biol 1996; 8: 542-8.
Couet J, Li SW, Okamoto T, Ikezu T, Lisanti MP. Identification of Peptide and Protein Ligands for the Caveolin-scaffolding Domain. J Biol Chem 1997; 272: 6525-33.
Hurlstone AF, Reid G, Reeves JR, Fraser J, Strathdee G, Rahilly M, et al. Analysis of the CAVEOLIN-1 gene at human chromosome 7q31.1 in primary tumours and tumour-derived cell lines. Oncogene 1999; 18: 1881-90.
Okamoto T, Schlegel A, Scherer PE, Lisanti MP. Caveolins, a Family of Scaffolding Proteins for Organizing "Preassembled Signaling Complexes" at the Plasma Membrane. J Biol Chem 1998; 273: 5419-22.
Stuart AD, Eustace HE, McKee TA, Brown TDK. A Novel Cell Entry Pathway for a DAF-Using Human Enterovirus Is Dependent on Lipid Rafts. J Virol 2002; 76: 9307-22.
Rota PA, Oberste MS, Monroe SS, Nix WA, Campagnoli R, Icenogle JP, et al. Characterization of a Novel Coronavirus Associated with Severe Acute Respiratory Syndrome. Science 2003; 300: 1394-99.
SARS统计分析和地理分布. 中华人民共和国卫生部网站 http://www.moh.gov.cn/zhgl/zt/yqfb/index.htm (accessed June 1, 2003).
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王鸣,杜琳,周端华,等. 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): 353-57.
彭国文,何剑峰,郭汝宁,等. 广东省传染性非典型肺炎流行病学特征分析. 华南预防医学 2003; 29(3): 11-2.
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Dempster AP, Laird NM, Rubin, DB. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society (Series B) 1977; 39(1): 1-38.
Meng XL, Rubin DB. Using EM to Obtain Asymptotic Variance-Covariance Matrices: The SEM Algorithm. Journal of the American Statistical Association 1991; 86(416): 899-909.
Meng XL, Rubin DB. Maximum likelihood estimation via the ECM algorithm: A general framework. Biometrika 1993; 80(2): 267-278.
Efron B. The Jackknife, the Bootstrap and Other Resampling Plans, Society for industrial and Applied Mathematics. Philadelphia, 1995.
Shao J, Tu D. The Jackknife and Bootstrap. Springer-Verlag New York, Inc. 1995.
McCullagh P, Nelder JA. Generalized Linear Models. Chapman and Hall, 1989.
Bates DV, Baker-Anderson M, Sizto R. Asthma attack periodicity: a study of hospital emergency visits in Vancouver. Environ Res 1990; 51: 51-70.
Schwarta J, Dockery DW. Particulate air pollution and daily mortality in Steubenville, Ohio. Am J Epidemiol 1992; 135: 12-9.
Dockery DW, Pope CA. Acute respiratory effects of particulate air pollution. Annu Rev Public Health 1994; 15: 107-32.
Schwartz J, Slater D, Larson TV, et al. Particulate air pollution and hospital emergency room visits for asthma in Seattle. Am Rev Respir Dis 1993; 147: 826-31.
李青春,陆晨,刘彦,等. 北京地区呼吸道疾病与气象条件关系的分析. 气象 1999; 25(3): 8-12.
赵杰夫,吕中科,赵燕妮,等. 流行性感冒与气象条件的关系初探. 湖北气象 1998; 2: 17-8.
张彩平,顾盈华,李楠. 气象专家分析发现气温17-28℃最易于SARS流行. http://news.xinhuanet.com/st/2003-09/27/content_1103502.htm (accessed September 27, 2003).
王瑾,仇逸. 研究发现:气象因素与SARS流行关联密切. http://news.sohu.com/53/77/news213297753.shtml (accessed September 17, 2003).
杨智聪,杜琳,王鸣,等. 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): 1028-30.
王铮,蔡砥,李山,等. 中国SARS流行的季节性风险探讨. 地理研究 2003; 22(5): 541-50.
叶殿秀,杨贤为,张强. 北京地区SARS与气象条件关系分析. 气象 2003; 29(10): 42-5.
叶玲,黄超光. SARS传播与空气质量相关性研究. 军医进修学院学报 2003; 24(4): 295-8.
WHO. Guidelines, recommendations, descriptions: First data on stability and resistance of SARS coronavirus compiled by members of WHO laboratory network. http://www.who.int/csr/sars/survival_2003_05_04/en/index.html (accessed May 4, 2003).
李敬云,鲍作义,刘思扬,等. SARS病毒在外界环境物品中生存和抵抗力的研究. 中国消毒学杂志 2003; 20(2): 110.
鲍作义,刘永健,刘思扬,等. SARS病毒对温度耐受性的实验研究. 中国消毒学杂志 2003; 20(3): 161-2.
Cui Y, Zhang ZF, Froines J, et al. Air pollution and case fatality of SARS in the People’s Republic of China: an ecologic study. Environmental Health: A Global Access Science Source 2003; 2: 15.
中华人民共和国卫生部. http://www.moh.gov.cn/ (accessed October 1, 2003).
王鸣,杜琳,周端华,等. 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): 353-57.
梁万年. 北京市SARS干预措施的效果效益评价. 预防医学文献信息 2003; 9(6): 766-8.
程锦泉,周俊安,周丽. 深圳市抗击“非典”实施策略及效果分析. 中国公共卫生 2003; 19(9): 1031-2.
Chan-Yeung M, Yu WC. Outbreak of severe acute respiratory syndrome in Hong Kong Special Administrative Region: case report. BMJ 2003; 326: 850-2.
Brookmeyer R, Gail MH. Minimum size of the acquired immunodeficiency syndrome (AIDS) epidemic in the United States. Lancet 1986; 11: 1320-2.
Becker NG, Watson LF, Carlin JB. A method of non-parametric back-projection and its application to AIDS data. Stat Med 1991; 10(10): 1527-42.
Chau PH, Yip PS.F, Cui JS. Reconstructing the incidence of human immunodeficiency virus (HIV) in Hong Kong by using data from HIV positive tests and diagnoses of acquired immune deficiency syndrome. Appl Statist 2003; 52: 237-48.
Cui JS, Becker NG. Estimating HIV incidence using dates of both HIV and AIDS diagnoses. Stat Med 2000; 19: 1165-77.
Dempster AP, Laird NM, Rubin, DB. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society (Series B) 1977; 39(1): 1-38.
Silverman BW, Jones MC, Wilson JD, et al. A smoothed EM approach to indirect estimation problems, with particular reference to stereology and emission tomography. Journal of the Royal Statistical Society (Series B) 1990; 52: 271-324.
姜庆五主编. 流行病学. 科学出版社 2003; 174-88.
Anderson RM, May RM. Infectious diseases of humans: dynamics and control. Oxford University Press 1991.
吴开琛,吴开录,陈文江,等. SARS传播数学模型与流行趋势预测研究. 中国热带医学 2003; 3(4): 421-5.
方北香. 传染病动力学常微分方程模型解的整体存在唯一性. 复旦学报(自然科学版) 2001; 40(6): 640.
张双德,郝海,张喜红. 一类含有潜伏期的传染病动力学模型. 数理医药学杂志 2002; 15(5): 385-6.
Lloyd AL. Realistic distributions of infectious periods in epidemic models: changing patterns of persistence and dynamics. Theoretical Population Biology
2001; 60: 59-71.
Medlock J, Kot M. Spreading disease: integro-differential equations old and new. Mathematical Biosciences 2003; 184(2): 201-22.
Daley DJ, Gani JM. Epidemic modeling: an introduction. Cambridge University Press 1999.
Ball F, Neal P. A general model for stochastic SIR epidemics with two levels of mixing. Mathematical Biosciences 2002; 180: 73-102.
O’Neill PD. A tutorial introduction to Bayesian inference for stochastic epidemic models using Markov chain Monte Carlo methods. Mathematical Biosciences 2002; 180: 103-114.
Dye C, Gay N. Modeling the SARS epidemic. Science 2003; 300: 1884-5.
Drazen JM. Case clusters of the severe acute respiratory syndrome. N Engl J Med 2003; 348: e6-7.
杨凤立. 科研单位最新调查显示四成儿童体内有SARS抗体. 新华网 http://www.xinhuanet.com/newscenter/gnzxbb.htm (accessed July 3, 2003)
Compton SR, Barthold SW, Smith AL. The cellular and molecular pathogenesis of coronaviruses. Lab Anim Sci 1993; 43: 15-20.
Haijema BJ, Volders H, Rottier PJ. Switching species tropism an effective way to manipulate the feline coronavirus genome. J Virol 2003; 77: 4528-38.
Ng TW, Turinici G, Danchin A. A double epidemic model for the SARS propagation. BMC Infectious Diseases 2003; 3: 19.
Moles CG, Mendes P, Banga JR. Parameter estimation in biochemical pathways: a comparison of global optimization methods. Genome Research 2003; 13: 2467-74.
http://www.gepasi.org Gepasi biochemical simulation package.
http://www.vanguardsw.com/updates/decisionpro40/ Vanguard DecisionPro for Windows
Ksiazek TG, Erdman D, Goldsmith CS, Zaki SR, Peret T, Emery S, et al. A novel coronavirus associated with severe acute respiratory syndrome. N Engl J Med 2003; 348:1947-58.
Mukherjee S, Ghosh RN, Maxfield FR. Endocytosis. Physiol Rev 1997; 77: 759-803.
Yu XJ, Luo C, Lin JC, Hao P, He YY, Guo ZM, et al. Putative hAPN receptor binding sites in SARS_CoV spike protein. Acta Pharmacol Sin 2003; 24: 481-8.
Bergelson JM, John NS, Kawaguchi S, Chan M, Stubdal H, Modlin J, et al. Infection by echoviruses 1 and 8 depends on the alpha 2 subunit of human VLA-2.
J Virol 1993; 67: 6847-52.
Davis GE, Camarillo CW. An alpha 2 beta 1 integrin-dependent pinocytic mechanism involving intracellular vacuole formation and coalescence regulates capillary lumen and tube formation in three-dimensional collagen matrix. Exp Cell Res 1996; 224: 39-51.
DeTulleo L, Kirchhausen T. The clathrin endocytic pathway in viral infection. EMBO J 1998; 17: 4585-93.
Anderson HA, Chen Y, Norkin LC. MHC class I molecules are enriched in caveolae but do not enter with simian virus 40. J Gen Virol 1998; 79: 1469-77.
Smart EJ, Graf GA, McNiven MA, Sessa WC, Engelman JA, Scherer PE, et al. Caveolins, Liquid-Ordered Domains, and Signal Transduction. Mol Cell Biol 1999; 19: 7289-304.
Verkade SP, Virta FM, Simons K, Ikonen E. Caveolin-1 and -2 in the Exocytic Pathway of MDCK Cells. J Cell Biol 1998; 140: 795-806.
Parton RG. Caveolae and caveolins. Curr Opin Cell Biol 1996; 8: 542-8.
Couet J, Li SW, Okamoto T, Ikezu T, Lisanti MP. Identification of Peptide and Protein Ligands for the Caveolin-scaffolding Domain. J Biol Chem 1997; 272: 6525-33.
Hurlstone AF, Reid G, Reeves JR, Fraser J, Strathdee G, Rahilly M, et al. Analysis of the CAVEOLIN-1 gene at human chromosome 7q31.1 in primary tumours and tumour-derived cell lines. Oncogene 1999; 18: 1881-90.
Okamoto T, Schlegel A, Scherer PE, Lisanti MP. Caveolins, a Family of Scaffolding Proteins for Organizing "Preassembled Signaling Complexes" at the Plasma Membrane. J Biol Chem 1998; 273: 5419-22.
Stuart AD, Eustace HE, McKee TA, Brown TDK. A Novel Cell Entry Pathway for a DAF-Using Human Enterovirus Is Dependent on Lipid Rafts. J Virol 2002; 76: 9307-22.
Rota PA, Oberste MS, Monroe SS, Nix WA, Campagnoli R, Icenogle JP, et al. Characterization of a Novel Coronavirus Associated with Severe Acute Respiratory Syndrome. Science 2003; 300: 1394-99.