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Planning for smallpox outbreaks

Author

Listed:
  • Neil M. Ferguson

    (Imperial College London)

  • Matt J. Keeling

    (University of Warwick)

  • W. John Edmunds

    (Health Protection Agency, CDSC)

  • Raymond Gani

    (Health Protection Agency, CAMR)

  • Bryan T. Grenfell

    (University of Cambridge)

  • Roy M. Anderson

    (Imperial College London)

  • Steve Leach

    (Health Protection Agency, CAMR)

Abstract

Mathematical models of viral transmission and control are important tools for assessing the threat posed by deliberate release of the smallpox virus and the best means of containing an outbreak. Models must balance biological realism against limitations of knowledge, and uncertainties need to be accurately communicated to policy-makers. Smallpox poses the particular challenge that key biological, social and spatial factors affecting disease spread in contemporary populations must be elucidated largely from historical studies undertaken before disease eradication in 1979. We review the use of models in smallpox planning within the broader epidemiological context set by recent outbreaks of both novel and re-emerging pathogens.

Suggested Citation

  • Neil M. Ferguson & Matt J. Keeling & W. John Edmunds & Raymond Gani & Bryan T. Grenfell & Roy M. Anderson & Steve Leach, 2003. "Planning for smallpox outbreaks," Nature, Nature, vol. 425(6959), pages 681-685, October.
    • Handle: RePEc:nat:nature:v:425:y:2003:i:6959:d:10.1038_nature02007
    DOI: 10.1038/nature02007
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    Cited by:

    1. Badham, Jennifer & Stocker, Rob, 2010. "The impact of network clustering and assortativity on epidemic behaviour," Theoretical Population Biology, Elsevier, vol. 77(1), pages 71-75.
    2. Robin N Thompson & Christopher A Gilligan & Nik J Cunniffe, 2018. "Control fast or control smart: When should invading pathogens be controlled?," PLOS Computational Biology, Public Library of Science, vol. 14(2), pages 1-21, February.
    3. Hazhir Rahmandad & John Sterman, 2008. "Heterogeneity and Network Structure in the Dynamics of Diffusion: Comparing Agent-Based and Differential Equation Models," Management Science, INFORMS, vol. 54(5), pages 998-1014, May.
    4. repec:jss:jstsof:36:i06 is not listed on IDEAS
    5. Tom Lindström & Michael Tildesley & Colleen Webb, 2015. "A Bayesian Ensemble Approach for Epidemiological Projections," PLOS Computational Biology, Public Library of Science, vol. 11(4), pages 1-30, April.
    6. Rezapour, Shabnam & Baghaian, Atefe & Naderi, Nazanin & Sarmiento, Juan P., 2023. "Infection transmission and prevention in metropolises with heterogeneous and dynamic populations," European Journal of Operational Research, Elsevier, vol. 304(1), pages 113-138.
    7. George Miller & Stephen Randolph & Jan E. Patterson, 2006. "Responding to Bioterrorist Smallpox in San Antonio," Interfaces, INFORMS, vol. 36(6), pages 580-590, December.
    8. Shams, Bita & Khansari, Mohammad, 2015. "On the impact of epidemic severity on network immunization algorithms," Theoretical Population Biology, Elsevier, vol. 106(C), pages 83-93.
    9. Robert Axtell & Joseph A. E. Shaheen, 2021. "Agent‐based models with qualitative data are thought experiments, not policy engines: A commentary on Lustick and Tetlock 2021," Futures & Foresight Science, John Wiley & Sons, vol. 3(2), June.
    10. Büyüktahtakın, İ. Esra & des-Bordes, Emmanuel & Kıbış, Eyyüb Y., 2018. "A new epidemics–logistics model: Insights into controlling the Ebola virus disease in West Africa," European Journal of Operational Research, Elsevier, vol. 265(3), pages 1046-1063.
    11. Xiaolei Gao & Jianjian Wei & Hao Lei & Pengcheng Xu & Benjamin J Cowling & Yuguo Li, 2016. "Building Ventilation as an Effective Disease Intervention Strategy in a Dense Indoor Contact Network in an Ideal City," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-20, September.
    12. Jose Angulo & Hwa-Lung Yu & Andrea Langousis & Alexander Kolovos & Jinfeng Wang & Ana Esther Madrid & George Christakos, 2013. "Spatiotemporal Infectious Disease Modeling: A BME-SIR Approach," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-12, September.
    13. Wijesundera, Isuri & Halgamuge, Malka N. & Nirmalathas, Ampalavanapillai & Nanayakkara, Thrishantha, 2016. "MFPT calculation for random walks in inhomogeneous networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 986-1002.
    14. Chengcheng Bei & Shiping Liu & Yin Liao & Gaoliang Tian & Zichen Tian, 2021. "Predicting new cases of COVID‐19 and the application to population sustainability analysis," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(3), pages 4859-4884, September.
    15. Chung‐Min Liao & Yi‐Hsien Cheng & Yi‐Jun Lin & Nan‐Hung Hsieh & Tang‐Luen Huang & Chia‐Pin Chio & Szu‐Chieh Chen & Min‐Pei Ling, 2012. "A Probabilistic Transmission and Population Dynamic Model to Assess Tuberculosis Infection Risk," Risk Analysis, John Wiley & Sons, vol. 32(8), pages 1420-1432, August.
    16. Arazi, R. & Feigel, A., 2021. "Discontinuous transitions of social distancing in the SIR model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    17. Daniel Merl & Leah R Johnson & Robert B Gramacy & Marc Mangel, 2009. "A Statistical Framework for the Adaptive Management of Epidemiological Interventions," PLOS ONE, Public Library of Science, vol. 4(6), pages 1-9, June.
    18. Dunia López-Pintado & Duncan J. Watts, 2008. "Social Influence, Binary Decisions and Collective Dynamics," Rationality and Society, , vol. 20(4), pages 399-443, November.
    19. Ali Ekici & Pınar Keskinocak & Julie L. Swann, 2014. "Modeling Influenza Pandemic and Planning Food Distribution," Manufacturing & Service Operations Management, INFORMS, vol. 16(1), pages 11-27, February.
    20. Colo, Philippe, 2021. "Expert-based Knowledge: Communicating over Scientific Models," MPRA Paper 110434, University Library of Munich, Germany.

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