Accommodating individual travel history, global mobility, and unsampled diversity in phylogeography: a SARS-CoV-2 case study

  1. Philippe Lemey
  2. Samuel Hong
  3. Verity Hill
  4. Guy Baele
  5. Chiara Poletto
  6. Vittoria Colizza
  7. Áine O’Toole
  8. John T. McCrone
  9. Kristian G. Andersen
  10. Michael Worobey
  11. Martha I. Nelson
  12. Andrew Rambaut
  13. Marc A. Suchard

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Abstract

Spatiotemporal bias in genome sequence sampling can severely confound phylogeographic inference based on discrete trait ancestral reconstruction. This has impeded our ability to accurately track the emergence and spread of SARS-CoV-2, the virus responsible for the COVID-19 pandemic. Despite the availability of unprecedented numbers of SARS-CoV-2 genomes on a global scale, evolutionary reconstructions are hindered by the slow accumulation of sequence divergence over its relatively short transmission history. When confronted with these issues, incorporating additional contextual data may critically inform phylodynamic reconstructions. Here, we present a new approach to integrate individual travel history data in Bayesian phylogeographic inference and apply it to the early spread of SARS-CoV-2, while also including global air transportation data. We demonstrate that including travel history data for each SARS-CoV-2 genome yields more realistic reconstructions of virus spread, particularly when travelers from undersampled locations are included to mitigate sampling bias. We further explore methods to ameliorate the impact of sampling bias by augmenting the phylogeographic analysis with lineages from undersampled locations in the analyses. Our reconstructions reinforce specific transmission hypotheses suggested by the inclusion of travel history data, but also suggest alternative routes of virus migration that are plausible within the epidemiological context but are not apparent with current sampling efforts. Although further research is needed to fully examine the performance of our travel-aware phylogeographic analyses with unsampled diversity and to further improve them, they represent multiple new avenues for directly addressing the colossal issue of sample bias in phylogeographic inference.

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    SciScore for 10.1101/2020.06.22.165464: (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
    We aligned the remaining 286 genomes using MAFFT 21 and partially trimmed the 5’ and 3’ ends.
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    Upon visualizing root-to-tip divergence as a function of sampling time using TempEst 22 based on an ML tree inferred with IQ-TREE 23, we removed one potential outlier.
    TempEst
    suggested: (TempEst, RRID:SCR_017304)
    IQ-TREE
    suggested: (IQ-TREE, RRID:SCR_017254)
    We summarize posterior tree distributions using maximum clade credibility (MCC) trees and visualize them using FigTree.
    FigTree
    suggested: (FigTree, RRID:SCR_008515)
    A new BEAST tree sample tool (TaxaMarkovJumpHistoryAnalyzer available in the BEAST codebase at https://github.com/beast-dev/beast-mcmc) and associated R package constructs these estimates.
    BEAST
    suggested: (BEAST, RRID:SCR_010228)

    Results from OddPub: Thank you for sharing your code.

    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.

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  2. Version published to 10.1101/2020.06.22.165464v1 on bioRxiv