应用随机网络对SARS在北京传播规律的研究
发布时间：2018-11-20 17:30
【摘要】： 传染性非典型肺炎,又称严重急性呼吸综合症传染(Severe AcuteRespiratory Syndrome,SARS),是由SARS冠状病毒感染引起的新发急性传染病.2002年11月在我国广东省首次发现后,至2003年6月,在短短时间里,蔓延肆虐于世界32个国家和地区,患者达八千多例,死亡七百多人,其中中国内地5327例(其中广东报告1512例),死亡349例(其中广东报告58例).2004年广东又发现4个新发病例,此外,台湾、新加坡、和我国还发生过实验室感染事故,SARS已成为二十一世纪首个对人类健康造成严重危害的传染病. 研究SARS的传播规律,一方面可以使我们从理论上了解SARS的传播规律;另一方面,为将来出现其它感染性疾病提供预防和治疗措施. 本文首先回顾了前人对SARS的研究现状,他们是采用Malthus模型和Logistic模型对SARS在北京的传播情况进行拟合的,也有采用SIR模型对北京SARS疫情的流行规律进行拟合的,拟合效果较好,但是这些模型都过于理想化;于是,有必要进行进一步研究,在这里用随机网络理论来探讨SARS在北京的传播,同时得到了相应结论,并提出了合理的预防措施. 通过比较,可以看出,虽然前人的研究可以使我们很直观的了解SARS病例随时间变化的情况,但是它只是一种事后描述;使用随机网络理论可以得到当传染能力T0.0375时,疾病不会大规模流行.
[Abstract]:Infectious atypical pneumonia (Severe AcuteRespiratory Syndrome,SARS), also called severe acute respiratory syndrome (SARS), is a new acute infectious disease caused by SARS coronavirus infection. In a short period of time, it has spread to 32 countries and regions in the world, with more than 8,000 patients and more than 700 deaths, including 5327 cases in the mainland of China (of which 1512 cases were reported from Guangdong). There were 349 deaths (58 reported in Guangdong). Four new cases were found in Guangdong in 2004. In addition, there were laboratory infections in Taiwan, Singapore, and China. SARS has become the first infectious disease to cause serious harm to human health in 21 century. On the one hand, we can understand the transmission law of SARS in theory; on the other hand, we can provide preventive and therapeutic measures for other infectious diseases in the future. In this paper, we first reviewed the current research status of SARS by predecessors. They used Malthus model and Logistic model to fit the transmission of SARS in Beijing, and SIR model was used to fit the epidemic pattern of SARS epidemic in Beijing, and the fitting effect was good. But these models are too idealistic; Therefore, it is necessary to further study the spread of SARS in Beijing by using the stochastic network theory. At the same time, the corresponding conclusions are obtained, and reasonable preventive measures are put forward. Through comparison, we can see that although previous studies can make us understand the changes of SARS cases over time, it is only a description after the fact; Using stochastic network theory, it can be concluded that when infectious capacity T 0.0375, the disease does not spread in a large scale.
【学位授予单位】：河北工业大学
【学位级别】：硕士
【学位授予年份】：2007
【分类号】：R181.3;O157.5
[Abstract]:Infectious atypical pneumonia (Severe AcuteRespiratory Syndrome,SARS), also called severe acute respiratory syndrome (SARS), is a new acute infectious disease caused by SARS coronavirus infection. In a short period of time, it has spread to 32 countries and regions in the world, with more than 8,000 patients and more than 700 deaths, including 5327 cases in the mainland of China (of which 1512 cases were reported from Guangdong). There were 349 deaths (58 reported in Guangdong). Four new cases were found in Guangdong in 2004. In addition, there were laboratory infections in Taiwan, Singapore, and China. SARS has become the first infectious disease to cause serious harm to human health in 21 century. On the one hand, we can understand the transmission law of SARS in theory; on the other hand, we can provide preventive and therapeutic measures for other infectious diseases in the future. In this paper, we first reviewed the current research status of SARS by predecessors. They used Malthus model and Logistic model to fit the transmission of SARS in Beijing, and SIR model was used to fit the epidemic pattern of SARS epidemic in Beijing, and the fitting effect was good. But these models are too idealistic; Therefore, it is necessary to further study the spread of SARS in Beijing by using the stochastic network theory. At the same time, the corresponding conclusions are obtained, and reasonable preventive measures are put forward. Through comparison, we can see that although previous studies can make us understand the changes of SARS cases over time, it is only a description after the fact; Using stochastic network theory, it can be concluded that when infectious capacity T 0.0375, the disease does not spread in a large scale.
【学位授予单位】：河北工业大学
【学位级别】：硕士
【学位授予年份】：2007
【分类号】：R181.3;O157.5
【参考文献】
相关期刊论文 前4条
1 王新为,李劲松,金敏,甄蓓,孔庆鑫,宋农,肖文s，
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