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Imperfect vaccines and the evolution of pathogen virulence

Author

Listed:
  • Sylvain Gandon

    (Institute of Cell, Animal and Population Biology, The University of Edinburgh)

  • Margaret J. Mackinnon

    (Institute of Cell, Animal and Population Biology, The University of Edinburgh)

  • Sean Nee

    (Institute of Cell, Animal and Population Biology, The University of Edinburgh)

  • Andrew F. Read

    (Institute of Cell, Animal and Population Biology, The University of Edinburgh)

Abstract

Vaccines rarely provide full protection from disease. Nevertheless, partially effective (imperfect) vaccines may be used to protect both individuals and whole populations1,2,3. We studied the potential impact of different types of imperfect vaccines on the evolution of pathogen virulence (induced host mortality) and the consequences for public health. Here we show that vaccines designed to reduce pathogen growth rate and/or toxicity diminish selection against virulent pathogens. The subsequent evolution leads to higher levels of intrinsic virulence and hence to more severe disease in unvaccinated individuals. This evolution can erode any population-wide benefits such that overall mortality rates are unaffected, or even increase, with the level of vaccination coverage. In contrast, infection-blocking vaccines induce no such effects, and can even select for lower virulence. These findings have policy implications for the development and use of vaccines that are not expected to provide full immunity, such as candidate vaccines for malaria4.

Suggested Citation

  • Sylvain Gandon & Margaret J. Mackinnon & Sean Nee & Andrew F. Read, 2001. "Imperfect vaccines and the evolution of pathogen virulence," Nature, Nature, vol. 414(6865), pages 751-756, December.
    • Handle: RePEc:nat:nature:v:414:y:2001:i:6865:d:10.1038_414751a
    DOI: 10.1038/414751a
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    Cited by:

    1. Gaeta, Giuseppe, 2022. "Mass vaccination in a roaring pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    2. Siming You & Man Pun Wan, 2015. "A Risk Assessment Scheme of Infection Transmission Indoors Incorporating the Impact of Resuspension," Risk Analysis, John Wiley & Sons, vol. 35(8), pages 1488-1502, August.
    3. Wang, Lianwen & Liu, Zhijun & Zhang, Xingan, 2016. "Global dynamics of an SVEIR epidemic model with distributed delay and nonlinear incidence," Applied Mathematics and Computation, Elsevier, vol. 284(C), pages 47-65.
    4. Reza Yaesoubi & Shiying You & Qin Xi & Nicolas A. Menzies & Ashleigh Tuite & Yonatan H. Grad & Joshua A. Salomon, 2023. "Generating simple classification rules to predict local surges in COVID-19 hospitalizations," Health Care Management Science, Springer, vol. 26(2), pages 301-312, June.
    5. Peng, Xiao-Long & Li, Chun-Yan & Qi, Hong & Sun, Gui-Quan & Wang, Zhen & Wu, Yong-Ping, 2022. "Competition between awareness and epidemic spreading in homogeneous networks with demography," Applied Mathematics and Computation, Elsevier, vol. 420(C).
    6. Lecorvaisier, Florian & Pontier, Dominique & Soubeyrand, Benoît & Fouchet, David, 2024. "Using a dynamical model to study the impact of a toxoid vaccine on the evolution of a bacterium: The example of diphtheria," Ecological Modelling, Elsevier, vol. 487(C).
    7. Deka, Aniruddha & Bhattacharyya, Samit, 2022. "The effect of human vaccination behaviour on strain competition in an infectious disease: An imitation dynamic approach," Theoretical Population Biology, Elsevier, vol. 143(C), pages 62-76.
    8. Alizon, Samuel & van Baalen, Minus, 2008. "Transmission–virulence trade-offs in vector-borne diseases," Theoretical Population Biology, Elsevier, vol. 74(1), pages 6-15.

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