CASIA OpenIR  > 毕业生  > 硕士学位论文
基于复杂网络的传染病传播行为特征和防控策略研究
Alternative TitleComplex Network-Based Analysis of Characteristics of the Spread of Infectious Diseases and Prevention and Control Strategies
罗媛
Subtype工学硕士
Thesis Advisor王飞跃 ; 曾大军
2010-05-13
Degree Grantor中国科学院研究生院
Place of Conferral中国科学院自动化研究所
Degree Discipline计算机应用技术
Keyword严重急性呼吸系统综合症 甲型h1n1流感 复杂网络 疾病监测 Sars Influenza a(H1n1) Complex Network Epidemic Surveillance
Abstract传染病是公共卫生安全隐患的重要来源,城市化和全球化背景下,人口越来越密集,人类交流越来越频繁,世界正在变得越来越小,突发传染病流行的风险日益加剧,也给突发传染疾病的防控带出了新的挑战。掌握传染病的传播扩散规律、传播网络拓扑特征、疫情发展趋势等可以为制定高效应急反应预案提供科学依据。 论文主要以2003年北京SARS流行事件和2009年北京市甲型H1N1流感作为研究对象,分别从网络构建和缺失关系推断、网络分析、基于多来源开源信息的监测建模、基于社会计算的防控措施探讨等方面展开了研究,主要归纳为两部分的内容: 1. 用复杂网络测量方法对 2003 年爆发的北京市 SARS 传播网络进行了研究。对传播网络缺失关系进行推断并且将社区发现算法应用到传播网络中。对北京市SARS 传播网络进行了实证研究,这在该领域研究中较为少见。 2. 利用多来源的开源信息对传染病疫情进行监控。疾病监测数据对疾病防控十分重要,但传统方法不能及时有效的对突发性流行病进行疫情数据收集。 本文以北京市甲型 H1N1 疫情为例进行了监测系统方案的构建,通过实验表明,该方法较之传统监测方法更加及时有效。 论文成果主要体现在对北京市SARS传播网络的充分挖掘、疾病爆发预测的建模及开展疾病预防和控制的创新方法,这些研究结果对未来可能面临的急性传染病流行的科学、及时、有效的应急反应有重要意义,研究方法能够为类似传染病的研究提供借鉴。
Other AbstractInfectious diseases constitute an important source of safety threats to public health. In the current context of globalization and urbanization, population density in urban areas is rising, human interactions are becoming increasingly frequent, the world is getting smaller, and the risk of sudden epidemics is ever growing, thus raising new challenges to the prevention and control of abrupt infectious diseases. Mastering patterns of the spread and diffusion of infectious diseases, topological characteristics of disease spread networks, development trends of epidemic situations, and so on can provide a scientific basis for the design of effective emergency response plans. In this thesis, with the 2003 SARS epidemic and the 2009 Influenza A(H1N1) in Beijing as research objects, we investigate network construction and missing relationship inference, network analysis, modeling of multiple open source-based surveillance, and social computing-based approach to prevention and control strategies. More specifically, the main contents of the thesis are as follows. 1. We study the spread network of the 2003 Beijing SARS outbreak using the means of complex networks. We infer missing relationships and detect community structures from this network. As is rare in related research, we conduct an empirical study of the Beijing SARS spread network. 2. We advocate multiple open source-based surveillance of epidemic situations. The timely collection of data about epidemic situations is very important for epidemic prevention and control. However, traditional data collection methods do not allow timely effective collection of data about the progress of sudden epidemics. Using the A H1N1 influenza activity in Beijing as a case, we construct a plan for a surveillance system. Experimental results show that this method is more timely and effective than traditional ones. The main contributions of the thesis include a comprehensive analysis of the Beijing SARS spread network, modeling of the prediction of disease outbreaks, and a novel method for disease prevention and control. The research outcomes have important implications to timely and effective emergency response against potential future acute infectious disease epidemics. Our methods provide exemplars to the study of similar epidemics.
shelfnumXWLW1555
Other Identifier200728017029219
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/7525
Collection毕业生_硕士学位论文
Recommended Citation
GB/T 7714
罗媛. 基于复杂网络的传染病传播行为特征和防控策略研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2010.
Files in This Item:
File Name/Size DocType Version Access License
CASIA_20072801702921(6651KB) 暂不开放CC BY-NC-SAApplication Full Text
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[罗媛]'s Articles
Baidu academic
Similar articles in Baidu academic
[罗媛]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[罗媛]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.