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Controlling the pandemic during the SARS-CoV-2 vaccination rollout

Author

Listed:
  • João Viana

    (University Medical Center Utrecht, Utrecht University
    Universidade de Lisboa)

  • Christiaan H. Dorp

    (Los Alamos National Laboratory)

  • Ana Nunes

    (Universidade de Lisboa
    Universidade de Lisboa)

  • Manuel C. Gomes

    (Universidade de Lisboa)

  • Michiel Boven

    (University Medical Center Utrecht, Utrecht University)

  • Mirjam E. Kretzschmar

    (University Medical Center Utrecht, Utrecht University)

  • Marc Veldhoen

    (Universidade de Lisboa)

  • Ganna Rozhnova

    (University Medical Center Utrecht, Utrecht University
    Universidade de Lisboa)

Abstract

There is a consensus that mass vaccination against SARS-CoV-2 will ultimately end the COVID-19 pandemic. However, it is not clear when and which control measures can be relaxed during the rollout of vaccination programmes. We investigate relaxation scenarios using an age-structured transmission model that has been fitted to age-specific seroprevalence data, hospital admissions, and projected vaccination coverage for Portugal. Our analyses suggest that the pressing need to restart socioeconomic activities could lead to new pandemic waves, and that substantial control efforts prove necessary throughout 2021. Using knowledge on control measures introduced in 2020, we anticipate that relaxing measures completely or to the extent as in autumn 2020 could launch a wave starting in April 2021. Additional waves could be prevented altogether if measures are relaxed as in summer 2020 or in a step-wise manner throughout 2021. We discuss at which point the control of COVID-19 would be achieved for each scenario.

Suggested Citation

  • João Viana & Christiaan H. Dorp & Ana Nunes & Manuel C. Gomes & Michiel Boven & Mirjam E. Kretzschmar & Marc Veldhoen & Ganna Rozhnova, 2021. "Controlling the pandemic during the SARS-CoV-2 vaccination rollout," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-23938-8
    DOI: 10.1038/s41467-021-23938-8
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    Cited by:

    1. Meng, Xueyu & Lin, Jianhong & Fan, Yufei & Gao, Fujuan & Fenoaltea, Enrico Maria & Cai, Zhiqiang & Si, Shubin, 2023. "Coupled disease-vaccination behavior dynamic analysis and its application in COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).
    2. Smriti Prasad & Erkan Kalafat & Helena Blakeway & Rosemary Townsend & Pat O’Brien & Edward Morris & Tim Draycott & Shakila Thangaratinam & Kirsty Le Doare & Shamez Ladhani & Peter von Dadelszen & Laur, 2022. "Systematic review and meta-analysis of the effectiveness and perinatal outcomes of COVID-19 vaccination in pregnancy," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    3. 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.
    4. Otilia Boldea & Adriana Cornea-Madeira & João Madeira, 2023. "Disentangling the effect of measures, variants, and vaccines on SARS-CoV-2 infections in England: a dynamic intensity model," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 444-466.
    5. Quang Dang Nguyen & Mikhail Prokopenko, 2022. "A general framework for optimising cost-effectiveness of pandemic response under partial intervention measures," Papers 2205.08996, arXiv.org, revised Nov 2022.

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