Characterization of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection Clusters Based on Integrated Genomic Surveillance, Outbreak Analysis and Contact Tracing in an Urban Setting

  1. Andreas Walker
  2. Torsten Houwaart
  3. Patrick Finzer
  4. Lutz Ehlkes
  5. Alona Tyshaieva
  6. Maximilian Damagnez
  7. Daniel Strelow
  8. Ashley Duplessis
  9. Jessica Nicolai
  10. Tobias Wienemann
  11. Teresa Tamayo
  12. Malte Kohns Vasconcelos
  13. Lisanna Hülse
  14. Katrin Hoffmann
  15. Nadine Lübke
  16. Sandra Hauka
  17. Marcel Andree
  18. Martin P Däumer
  19. Alexander Thielen
  20. Susanne Kolbe-Busch
  21. Klaus Göbels
  22. Rainer Zotz
  23. Klaus Pfeffer
  24. Jörg Timm
  25. Alexander T Dilthey
  26. German COVID-19 OMICS Initiative (DeCOI)
  27. Janine Altmüller
  28. Angel Angelov
  29. Anna C Aschenbrenner
  30. Robert Bals
  31. Alexander Bartholomäus
  32. Anke Becker
  33. Daniela Bezdan
  34. Michael Bitzer
  35. Helmut Blum
  36. Ezio Bonifacio
  37. Peer Bork
  38. Nicolas Casadei
  39. Thomas Clavel
  40. Maria Colome-Tatche
  41. Inti Alberto De La Rosa Velázquez
  42. Andreas Diefenbach
  43. Alexander Dilthey
  44. Nicole Fischer
  45. Konrad Förstner
  46. Sören Franzenburg
  47. Julia-Stefanie Frick
  48. Gisela Gabernet
  49. Julien Gagneur
  50. Tina Ganzenmüller
  51. Marie Gauder
  52. Alexander Goesmann
  53. Siri Göpel
  54. Adam Grundhoff
  55. Hajo Grundmann
  56. Torsten Hain
  57. André Heimbach
  58. Michael Hummel
  59. Thomas Iftner
  60. Angelika Iftner
  61. Stefan Janssen
  62. Jörn Kalinowski
  63. René Kallies
  64. Birte Kehr
  65. Andreas Keller
  66. Oliver Keppler
  67. Sarah Kim-Hellmuth
  68. Christoph Klein
  69. Michael Knop
  70. Oliver Kohlbacher
  71. Karl Köhrer
  72. Jan Korbel
  73. Peter G Kremsner
  74. Denise Kühnert
  75. Ingo Kurth
  76. Markus Landthaler
  77. Yang Li
  78. Kerstin Ludwig
  79. Oliwia Makarewicz
  80. Manja Marz
  81. Alice McHardy
  82. Christian Mertes
  83. Maximilian Münchhoff
  84. Sven Nahnsen
  85. Markus Nöthen
  86. Francine Ntoumi
  87. Peter Nürnberg
  88. Uwe Ohler
  89. Stephan Ossowski
  90. Jörg Overmann
  91. Silke Peter
  92. Klaus Pfeffer
  93. Anna R Poetsch
  94. Ulrike Protzer
  95. Alfred Pühler
  96. Nikolaus Rajewsky
  97. Markus Ralser
  98. Olaf Rieß
  99. Stephan Ripke
  100. Ulisses Rocha
  101. Philip Rosenstiel
  102. Emmanuel Saliba
  103. Leif Erik Sander
  104. Birgit Sawitzki
  105. Simone Scheithauer
  106. Philipp Schiffer
  107. Jonathan Schmid-Burgk
  108. Wulf Schneider
  109. Eva-Christina Schulte
  110. Joachim Schultze
  111. Alexander Sczyrba
  112. Mariam L Sharaf
  113. Yogesh Singh
  114. Michael Sonnabend
  115. Oliver Stegle
  116. Jens Stoye
  117. Fabian Theis
  118. Janne Vehreschild
  119. Thirumalaisamy P Velavan
  120. Jörg Vogel
  121. Max von Kleist
  122. Andreas Walker
  123. Jörn Walter
  124. Dagmar Wieczorek
  125. Sylke Winkler
  126. John Ziebuhr

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Abstract

Background

Tracing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission chains is still a major challenge for public health authorities, when incidental contacts are not recalled or are not perceived as potential risk contacts. Viral sequencing can address key questions about SARS-CoV-2 evolution and may support reconstruction of viral transmission networks by integration of molecular epidemiology into classical contact tracing.

Methods

In collaboration with local public health authorities, we set up an integrated system of genomic surveillance in an urban setting, combining a) viral surveillance sequencing, b) genetically based identification of infection clusters in the population, c) integration of public health authority contact tracing data, and d) a user-friendly dashboard application as a central data analysis platform.

Results

Application of the integrated system from August to December 2020 enabled a characterization of viral population structure, analysis of 4 outbreaks at a maximum care hospital, and genetically based identification of 5 putative population infection clusters, all of which were confirmed by contact tracing. The system contributed to the development of improved hospital infection control and prevention measures and enabled the identification of previously unrecognized transmission chains, involving a martial arts gym and establishing a link between the hospital to the local population.

Conclusions

Integrated systems of genomic surveillance could contribute to the monitoring and, potentially, improved management of SARS-CoV-2 transmission in the population.

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  1. Version published to 10.1093/cid/ciab588
  2. ScreenIT

    SciScore for 10.1101/2021.02.13.21251678: (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
    SARS-CoV-2 dashboard application: The SARS-CoV-2 dashboard web application consists of a Python/Flask back-end and a browser front-end utilizing Bootstrap (https://getbootstrap.com/), JQuery (https://jquery.com/) and D3 [Bostock et al. 2011] for visualization.
    Python/Flask
    suggested: None
    Phylogenetic and minimum spanning tree analyses: Phylogenetic trees were computed with RAxML [Stamatakis 2014] based on multiple sequence alignments computed with Geneious 10.2.6 (https://www.geneious.com) and visualized with iTol [Letunic and Bork 2016].
    RAxML
    suggested: (RAxML, RRID:SCR_006086)
    Geneious
    suggested: (Geneious, RRID:SCR_010519)
    Minimum spanning trees of hospital outbreaks and related samples from the surveillance cohort and GISAID were computed with the Python library networkx version 2.5 and were visualized with Cytoscape version 3.8.2 and Inkscape version 0.92.
    Python
    suggested: (IPython, RRID:SCR_001658)
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)
    Inkscape
    suggested: (Inkscape, RRID:SCR_014479)
    Fast Nanopore sequencing experiment: The following modifications were applied to the Nanopore sequencing and analysis pipeline described above to improve total turn-around time from sample receipt to generation of the consensus sequence: (i) cDNA synthesis and PCR amplification were carried out as described in the Artic network SARS-CoV-2 protocol; however, library preparation was done by two trained personnel resulting in 10.5h from sample to MinION loading (Supplementary Figure 3); (ii) live high-accuracy Guppy base calling was set up on the MinION control workstation, utilizing a GeForce RTX 2080 Ti GPU; (iii) at defined times after the start of the sequencing run (15h, 17h, 19h, 21h, 23h, 25h, 27h, 37h, 48h, 63h, 72h), the Artic bioinformatics pipeline (see above; using only Medaka; runtime 0.5 - 1.5 hours on the MinION control workstation with 40 Intel Xeon Silver 4114 cores) was applied to the generated sequencing data, and genome completeness per sample (here defined as the number of non-N bases in the generated consensus sequences) was measured.
    MinION
    suggested: (MinION, RRID:SCR_017985)
    After the run had finished, final analysis was performed with the full Artic pipeline and curation of the resulting assemblies was based on both the variants called by Medaka and Nanopolish.
    Nanopolish
    suggested: (Nanopolish, RRID:SCR_016157)

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Limitations of this study include the utilized convenience sampling scheme, the relatively low proportion of sequenced positive cases over the sampling period, and the retrospective integration of genetic and contact tracing data. Addressing these in a follow-up study aiming for comprehensive case coverage, ultra-rapid turnaround times, real-time data sharing and integration with local public health authorities would be a natural extension of the work presented here and will demonstrate the full potential of integrated genomic surveillance with respect to the SARS-CoV-2 pandemic.

    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.

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  3. Version published to 10.1101/2021.02.13.21251678v1 on medRxiv