Swiss public health measures associated with reduced SARS-CoV-2 transmission using genome data

  1. Sarah A. Nadeau
  2. Timothy G. Vaughan
  3. Christiane Beckmann
  4. Ivan Topolsky
  5. Chaoran Chen
  6. Emma Hodcroft
  7. Tobias Schär
  8. Ina Nissen
  9. Natascha Santacroce
  10. Elodie Burcklen
  11. Pedro Ferreira
  12. Kim Philipp Jablonski
  13. Susana Posada-Céspedes
  14. Vincenzo Capece
  15. Sophie Seidel
  16. Noemi Santamaria de Souza
  17. Julia M. Martinez-Gomez
  18. Phil Cheng
  19. Philipp P. Bosshard
  20. Mitchell P. Levesque
  21. Verena Kufner
  22. Stefan Schmutz
  23. Maryam Zaheri
  24. Michael Huber
  25. Alexandra Trkola
  26. Samuel Cordey
  27. Florian Laubscher
  28. Ana Rita Gonçalves
  29. Sébastien Aeby
  30. Trestan Pillonel
  31. Damien Jacot
  32. Claire Bertelli
  33. Gilbert Greub
  34. Karoline Leuzinger
  35. Madlen Stange
  36. Alfredo Mari
  37. Tim Roloff
  38. Helena Seth-Smith
  39. Hans H. Hirsch
  40. Adrian Egli
  41. Maurice Redondo
  42. Olivier Kobel
  43. Christoph Noppen
  44. Louis du Plessis
  45. Niko Beerenwinkel
  46. Richard A. Neher
  47. Christian Beisel
  48. Tanja Stadler

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Abstract

Genome sequences from evolving infectious pathogens allow quantification of case introductions and local transmission dynamics. We sequenced 11,357 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from Switzerland in 2020—the sixth largest effort globally. Using a representative subset of these data, we estimated viral introductions to Switzerland and their persistence over the course of 2020. We contrasted these estimates with simple null models representing the absence of certain public health measures. We show that Switzerland’s border closures decoupled case introductions from incidence in neighboring countries. Under a simple model, we estimate an 86 to 98% reduction in introductions during Switzerland’s strictest border closures. Furthermore, the Swiss 2020 partial lockdown roughly halved the time for sampled introductions to die out. Last, we quantified local transmission dynamics once introductions into Switzerland occurred using a phylodynamic model. We found that transmission slowed 35 to 63% upon outbreak detection in summer 2020 but not in fall. This finding may indicate successful contact tracing over summer before overburdening in fall. The study highlights the added value of genome sequencing data for understanding transmission dynamics.

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  1. Version published to 10.1126/scitranslmed.abn7979
  2. Version published to 10.1101/2021.11.11.21266107v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2021.11.11.21266107: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    RNA extraction was done with either the Abbott m2000sp or Seegene
    Abbott
    suggested: (Abbott, RRID:SCR_010477)
    Both centers used the ARCTIC v3 primer scheme (27,28) to generate tiled, approximately 400bp-long amplicons.
    ARCTIC
    suggested: (ARCTIC, RRID:SCR_005989)
    Bioinformatics was done using V-pipe (29), including read trimming and filtering with PRINSEQ (30), alignment to GenBank accession MN908947 (31) with bwa (32), and consensus base calling.
    PRINSEQ
    suggested: (PRINSEQ, RRID:SCR_005454)
    We aligned the sequences to the reference genome MN908947.3 using MAFFT (35).
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    Timetree generation: We estimated the maximum likelihood phylogeny for each lineage alignment using IQ-TREE (39) under an HKY substitution model (40) with empirical base frequencies and four gamma rate categories.
    IQ-TREE
    suggested: (IQ-TREE, RRID:SCR_017254)
    Phylodynamic analysis: After identifying introductions, we performed phylodynamic inference on them using the BDSKY (birth-death skyline) method (44) in BEAST2 (45).
    BEAST2
    suggested: (BEAST2, RRID:SCR_017307)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

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  4. Version published to 10.1101/2021.11.11.21266107v1 on medRxiv