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清华大学学报(自然科学版)  2016, Vol. 56 Issue (1): 97-101    DOI: 10.16511/j.cnki.qhdxxb.2016.23.014
  工程物理 本期目录 | 过刊浏览 | 高级检索 |
大规模传染病传播围堵策略的模拟研究
倪顺江, 翁文国, 张辉
清华大学 工程物理系, 公共安全研究院, 北京 100084
Simulation of strategies for large-scale spread containment of infectious diseases
NI Shunjiang, WENG Wenguo, ZHANG Hui
Institute of Public Safety Research, Department of Engineering Physics, Tsinghua University, Beijing 100084, China
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摘要 该文研究了4种典型的围堵策略: 隔离治疗、接触追踪、预防治疗和免疫接种等对传染病传播过程的影响。首先建立了一个基于复杂网络理论和个体模拟的大规模传染病传播模型, 然后将上述围堵策略对模型参数的影响进行量化, 从而得到围堵策略作用下的传染病传播模型。模拟结果显示: 这些围堵策略均能够有效抑制传染病的传播, 而通过各种围堵策略的优化组合, 可以显著降低围堵策略的实施成本。研究结果可为公共卫生部门在传染病的预防和控制的科学决策中提供参考。
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倪顺江
翁文国
张辉
关键词 大规模传染病传播传染病建模围堵策略复杂网络    
Abstract:This paper investigates the inhibitory effects of four typical containment strategies, including quarantine, contact tracing, antiviral prophylaxis and vaccination, on the transmission process of infectious diseases. A large-scale individual-based simulation model for the spread of infectious diseases was built based on the complex network theory, with the containment strategies mentioned above then introduced into the model by quantifying the model parameters to rebuild an epidemic model. Simulation results show that these containment strategies can effectively inhibit the spread of infectious diseases, with the implementation cost significantly reduced through optimal combination of the containment strategies. The results can generate insights into scientific decision-making in the prevention and control of infectious disease spread for the public health sector.
Key wordslarge-scale epidemic spreading    epidemic modeling    containment strategy    complex network
收稿日期: 2011-05-25      出版日期: 2016-01-15
ZTFLH:  R183  
引用本文:   
倪顺江, 翁文国, 张辉. 大规模传染病传播围堵策略的模拟研究[J]. 清华大学学报(自然科学版), 2016, 56(1): 97-101.
NI Shunjiang, WENG Wenguo, ZHANG Hui. Simulation of strategies for large-scale spread containment of infectious diseases. Journal of Tsinghua University(Science and Technology), 2016, 56(1): 97-101.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2016.23.014  或          http://jst.tsinghuajournals.com/CN/Y2016/V56/I1/97
  图1 基于铁路和民航网络的大规模传染病传播模型
  图2 考虑围堵策略的SEIR 传播模型示意图
  图3 隔离治疗+接触追踪对传播过程的影响
  图4 在隔离治疗+接触追踪的基础上不同比例的预防治疗对传播过程的影响
  图5 在隔离治疗+接触追踪+预防治疗的基础上不同免疫接种策略对传播过程的影响
  图6 在隔离治疗+接触追踪+预防治疗的基础上不同比例的免疫接种对传播过程的影响
[1] Liang W N, Zhu Z H, Guo J Y, et al. Severe acute respiratory syndrome, Beijing, 2003 [J]. Emerging Infectious Diseases, 2004, 10(1): 25-31.
[2] Ferguson N M, Cummings D A T. Strategies for mitigating an influenza pandemic [J]. Nature, 2006, 442(7101): 448-452.
[3] Longini I M, Nizam A, Xu S F, et al. Containing pandemic influenza at the source [J]. Science, 2005, 309(5737): 1083-1087.
[4] Keeling M J, Eames K T D. Networks and epidemic models [J]. J Roy Soc Interface, 2005, 2(4): 295-307.
[5] Newman M E J. The structure and function of complex networks [J]. Siam Rev, 2003, 45(2): 167-256.
[6] Lipsitch M, Cohen T. Transmission dynamics and control of severe acute respiratory syndrome [J]. Science, 2003, 300(5627): 1966-1970.
[7] Riley S, Fraser C, Donnelly C A, et al. Transmission dynamics of the etiological agent of SARS in Hong Kong: Impact of public health interventions [J]. Science, 2003, 300(5627): 1961-1966.
[8] Meyers L A, Pourbohloul B, Newman M E J, et al. Network theory and SARS: Predicting outbreak diversity [J]. Journal of Theoretical Biology, 2005, 232(1): 71-81.
[9] Dye C, Gay N. Modeling the SARS epidemic [J]. Science, 2003, 300(5627): 1884-1885.
[10] Riley S. Large-scale spatial-transmission models of infectious disease [J]. Science, 2007, 316(5829): 1298-1301.
[11] Ni S J, Weng W G. Impact of travel patterns on epidemic dynamics in heterogeneous spatial metapopulation networks [J]. Phys Rev E, 2009, 79(016111): 1-5.
[12] 倪顺江. 基于复杂网络理论的传染病动力学建模与研究 [D]. 北京: 清华大学, 2009.NI Shunjiang. Research on modeling of infectious disease spreading based on complex network theory [D]. Beijing: Tsinghua University, 2009. (in Chinese)
[13] Fan W C, Ni S J. Modeling the SARS epidemic in China based on dynamical passenger flow in railway and airline networks [C]//9th International Symposium on New Technologies for Urban Safety of Mega Cities in Asia. Kobe, Japan, 2010: 108-119.
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