Please wait a minute...
 首页  期刊介绍 期刊订阅 联系我们
 
最新录用  |  预出版  |  当期目录  |  过刊浏览  |  阅读排行  |  下载排行  |  引用排行  |  百年期刊
Journal of Tsinghua University(Science and Technology)    2016, Vol. 56 Issue (1) : 97-101     DOI: 10.16511/j.cnki.qhdxxb.2016.23.014
ENGINEERING PHYSICS |
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
Download: PDF(1312 KB)  
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
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.
Keywords large-scale epidemic spreading      epidemic modeling      containment strategy      complex network     
ZTFLH:  R183  
Issue Date: 15 January 2016
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
NI Shunjiang
WENG Wenguo
ZHANG Hui
Cite this article:   
NI Shunjiang,WENG Wenguo,ZHANG Hui. Simulation of strategies for large-scale spread containment of infectious diseases[J]. Journal of Tsinghua University(Science and Technology), 2016, 56(1): 97-101.
URL:  
http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2016.23.014     OR     http://jst.tsinghuajournals.com/EN/Y2016/V56/I1/97
  
  
  
  
  
  
[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.
[1] LI Ziyuan, SUN Hao, LI Linbo. Analysis of intercity travel in the Yangtze River Delta based on mobile signaling data[J]. Journal of Tsinghua University(Science and Technology), 2022, 62(7): 1203-1211.
[2] XIE Lixia, SUN Honghong, YANG Hongyu, ZHANG Liang. Key node recognition in complex networks based on the K-shell method[J]. Journal of Tsinghua University(Science and Technology), 2022, 62(5): 849-861.
[3] YU Yong, WANG Yinggang, LUO Zhengguo, YANG Yan, WANG Xinkai, GAO Tao, YU Qian. Link prediction algorithm based on clustering coefficient and node centrality[J]. Journal of Tsinghua University(Science and Technology), 2022, 62(1): 98-104.
[4] DING Ying, ZHANG Jianqin, YANG Mu, GONG Peng, JIA Lipeng, DENG Shaocun. Communicable disease transmission model for the prevention and control of COVID-19 in Wuhan City, China[J]. Journal of Tsinghua University(Science and Technology), 2021, 61(12): 1452-1461.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
Copyright © Journal of Tsinghua University(Science and Technology), All Rights Reserved.
Powered by Beijing Magtech Co. Ltd