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Evolution of resistance to COVID-19 vaccination with dynamic social distancing

Author

Listed:
  • Gabriela Lobinska

    (Weizmann Institute of Science)

  • Ady Pauzner

    (Tel Aviv University)

  • Arne Traulsen

    (Max-Planck-Institute for Evolutionary Biology)

  • Yitzhak Pilpel

    (Weizmann Institute of Science)

  • Martin A. Nowak

    (Harvard University
    Harvard University)

Abstract

The greatest hope for a return to normalcy following the COVID-19 pandemic is worldwide vaccination. Yet, a relaxation of social distancing that allows increased transmissibility, coupled with selection pressure due to vaccination, will probably lead to the emergence of vaccine resistance. We analyse the evolutionary dynamics of COVID-19 in the presence of dynamic contact reduction and in response to vaccination. We use infection and vaccination data from six different countries. We show that under slow vaccination, resistance is very likely to appear even if social distancing is maintained. Under fast vaccination, the emergence of mutants can be prevented if social distancing is maintained during vaccination. We analyse multiple human factors that affect the evolutionary potential of the virus, including the extent of dynamic social distancing, vaccination campaigns, vaccine design, boosters and vaccine hesitancy. We provide guidelines for policies that aim to minimize the probability of emergence of vaccine-resistant variants.

Suggested Citation

  • Gabriela Lobinska & Ady Pauzner & Arne Traulsen & Yitzhak Pilpel & Martin A. Nowak, 2022. "Evolution of resistance to COVID-19 vaccination with dynamic social distancing," Nature Human Behaviour, Nature, vol. 6(2), pages 193-206, February.
    • Handle: RePEc:nat:nathum:v:6:y:2022:i:2:d:10.1038_s41562-021-01281-8
    DOI: 10.1038/s41562-021-01281-8
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    2. Xiyun Zhang & Zhongyuan Ruan & Muhua Zheng & Jie Zhou & Stefano Boccaletti & Baruch Barzel, 2022. "Epidemic spreading under mutually independent intra- and inter-host pathogen evolution," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    3. Nie, Yanyi & Zhong, Xiaoni & Lin, Tao & Wang, Wei, 2023. "Pathogen diversity in meta-population networks," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).

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