Strategies for mitigating an influenza pandemic

  • Nature volume 442, pages 448452 (27 July 2006)
  • doi:10.1038/nature04795
  • Download Citation


Development of strategies for mitigating the severity of a new influenza pandemic is now a top global public health priority. Influenza prevention and containment strategies can be considered under the broad categories of antiviral, vaccine and non-pharmaceutical (case isolation, household quarantine, school or workplace closure, restrictions on travel) measures1. Mathematical models are powerful tools for exploring this complex landscape of intervention strategies and quantifying the potential costs and benefits of different options2,3,4,5. Here we use a large-scale epidemic simulation6 to examine intervention options should initial containment6,7 of a novel influenza outbreak fail, using Great Britain and the United States as examples. We find that border restrictions and/or internal travel restrictions are unlikely to delay spread by more than 2–3 weeks unless more than 99% effective. School closure during the peak of a pandemic can reduce peak attack rates by up to 40%, but has little impact on overall attack rates, whereas case isolation or household quarantine could have a significant impact, if feasible. Treatment of clinical cases can reduce transmission, but only if antivirals are given within a day of symptoms starting. Given enough drugs for 50% of the population, household-based prophylaxis coupled with reactive school closure could reduce clinical attack rates by 40–50%. More widespread prophylaxis would be even more logistically challenging but might reduce attack rates by over 75%. Vaccine stockpiled in advance of a pandemic could significantly reduce attack rates even if of low efficacy. Estimates of policy effectiveness will change if the characteristics of a future pandemic strain differ substantially from those seen in past pandemics.

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We thank the National Institute of General Medical Sciences MIDAS Program (N.M.F., D.A.T.C. and D.S.B.), the Medical Research Council (N.M.F.), the Royal Society (N.M.F. and C.F.) and the Howard Hughes Medical Institute (N.M.F.) for research funding. We thank B. Schwartz, R. Robinson, B. Gellin, D. Harper, J. Edmunds, P. Grove, R. Hatchett and members of the MIDAS consortium for useful discussions. We also thank the National Center for Supercomputing Applications (NCSA) and the MIDAS informatics group for computational resources and technical advice. Author Contributions N.M.F. designed, implemented and ran the model, integrated the demographic and disease data sets used, and drafted and revised the text. All other authors edited or commented on the text. D.C. identified, collated and processed some demographic and travel data sets used and provided input on model assumptions. C.F. performed analytical modelling that aided the verification of the simulation and gave suggestions on model parameterization. J.C.C. assisted with the collation of demographic and travel data sets. P.C.C. assisted with provision of high performance computing facilities and technical advice. D.S.B. provided input into model design and assumptions, advised on the presentation of results and assisted with data collection.

Author information


  1. Department of Infectious Disease Epidemiology, Faculty of Medicine, Imperial College London, St Mary's Campus, Norfolk Place, London W2 1PG, UK

    • Neil M. Ferguson
    •  & Christophe Fraser
  2. Department of International Health, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, Maryland 21205, USA

    • Derek A. T. Cummings
    •  & Donald S. Burke
  3. RTI International Inc., P.O. 12194, 3040 Cornwallis Rd, Research Triangle Park, North Carolina 27709, USA

    • James C. Cajka
    •  & Philip C. Cooley


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Competing interests

Reprints and permissions information is available at npg.nature.com/reprintsandpermissions. The authors declare no competing financial interests.

Corresponding author

Correspondence to Neil M. Ferguson.

Supplementary information

PDF files

  1. 1.

    Supplementary Notes

    This file contains information on datasets and modelling methods used, model parameterization and sensitivity analyses.


  1. 1.

    Supplementary Video 1

    This file shows the first 140 days of a pandemic in Great Britain, assuming a moderate level of transmissibility (R0=1.7). The movie starts on day 40 of the global pandemic, just before the first cases entering the country. The video runs at the rate of three days model time per second of video time. Grey scale represents population density of uninfected, susceptible individuals. Red represents density of infected individuals, and green represents areas where the epidemic is over. 58.1 million individuals were modelled in the GB simulation.

  2. 2.

    Supplementary Video 2

    This file shows the first 170 days of a pandemic in the United States of America, assuming a moderate level of transmissibility (R0=1.7). The movie starts on day 40 of the global pandemic, just prior to the first cases entering the country. The video runs at the rate of three days model time per second of video time. Grey scale represents population density of uninfected, susceptible individuals. Red represents density of infected individuals, and green represents areas where the epidemic is over. 300 million individuals were modelled in the US simulation.


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