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Genetic evidence for the association between COVID-19 epidemic severity and timing of non-pharmaceutical interventions

Author

Listed:
  • Manon Ragonnet-Cronin

    (Imperial College London)

  • Olivia Boyd

    (Imperial College London)

  • Lily Geidelberg

    (Imperial College London)

  • David Jorgensen

    (Imperial College London)

  • Fabricia F. Nascimento

    (Imperial College London)

  • Igor Siveroni

    (Imperial College London)

  • Robert A. Johnson

    (Imperial College London)

  • Marc Baguelin

    (Imperial College London)

  • Zulma M. Cucunubá

    (Imperial College London)

  • Elita Jauneikaite

    (Imperial College London)

  • Swapnil Mishra

    (Imperial College London)

  • Oliver J. Watson

    (Imperial College London)

  • Neil Ferguson

    (Imperial College London)

  • Anne Cori

    (Imperial College London)

  • Christl A. Donnelly

    (Imperial College London
    University of Oxford)

  • Erik Volz

    (Imperial College London)

Abstract

Unprecedented public health interventions including travel restrictions and national lockdowns have been implemented to stem the COVID-19 epidemic, but the effectiveness of non-pharmaceutical interventions is still debated. We carried out a phylogenetic analysis of more than 29,000 publicly available whole genome SARS-CoV-2 sequences from 57 locations to estimate the time that the epidemic originated in different places. These estimates were examined in relation to the dates of the most stringent interventions in each location as well as to the number of cumulative COVID-19 deaths and phylodynamic estimates of epidemic size. Here we report that the time elapsed between epidemic origin and maximum intervention is associated with different measures of epidemic severity and explains 11% of the variance in reported deaths one month after the most stringent intervention. Locations where strong non-pharmaceutical interventions were implemented earlier experienced much less severe COVID-19 morbidity and mortality during the period of study.

Suggested Citation

  • Manon Ragonnet-Cronin & Olivia Boyd & Lily Geidelberg & David Jorgensen & Fabricia F. Nascimento & Igor Siveroni & Robert A. Johnson & Marc Baguelin & Zulma M. Cucunubá & Elita Jauneikaite & Swapnil M, 2021. "Genetic evidence for the association between COVID-19 epidemic severity and timing of non-pharmaceutical interventions," Nature Communications, Nature, vol. 12(1), pages 1-7, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-22366-y
    DOI: 10.1038/s41467-021-22366-y
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    Cited by:

    1. Liang, Zhenglin & Jiang, Chen & Sun, Muxia & Xue, Zongqi & Li, Yan-Fu, 2023. "Resilience analysis for confronting the spreading risk of contagious diseases," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    2. Hong, Xiao & Han, Yuexing & Wang, Bing, 2023. "Impacts of detection and contact tracing on the epidemic spread in time-varying networks," Applied Mathematics and Computation, Elsevier, vol. 439(C).
    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. King, Aaron A. & Lin, Qianying & Ionides, Edward L., 2022. "Markov genealogy processes," Theoretical Population Biology, Elsevier, vol. 143(C), pages 77-91.

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