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Inferring HIV-1 transmission networks and sources of epidemic spread in Africa with deep-sequence phylogenetic analysis

Author

Listed:
  • Oliver Ratmann

    (Imperial College London
    Imperial College London)

  • M. Kate Grabowski

    (Johns Hopkins School of Medicine
    Rakai Health Sciences Program)

  • Matthew Hall

    (University of Oxford)

  • Tanya Golubchik

    (University of Oxford)

  • Chris Wymant

    (Imperial College London
    University of Oxford)

  • Lucie Abeler-Dörner

    (University of Oxford)

  • David Bonsall

    (University of Oxford)

  • Anne Hoppe

    (University College London)

  • Andrew Leigh Brown

    (University of Edinburgh)

  • Tulio Oliveira

    (University of KwaZulu-Natal)

  • Astrid Gall

    (Wellcome Genome Campus)

  • Paul Kellam

    (Imperial College London)

  • Deenan Pillay

    (University College London
    Africa Health Research Institute)

  • Joseph Kagaayi

    (Rakai Health Sciences Program)

  • Godfrey Kigozi

    (Rakai Health Sciences Program)

  • Thomas C. Quinn

    (Johns Hopkins School of Medicine
    NIH)

  • Maria J. Wawer

    (Rakai Health Sciences Program
    Johns Hopkins Bloomberg School of Public Health)

  • Oliver Laeyendecker

    (Johns Hopkins School of Medicine
    NIH)

  • David Serwadda

    (Rakai Health Sciences Program
    Makerere University School of Public Health)

  • Ronald H. Gray

    (Johns Hopkins School of Medicine
    Rakai Health Sciences Program
    Johns Hopkins Bloomberg School of Public Health)

  • Christophe Fraser

    (University of Oxford)

Abstract

To prevent new infections with human immunodeficiency virus type 1 (HIV-1) in sub-Saharan Africa, UNAIDS recommends targeting interventions to populations that are at high risk of acquiring and passing on the virus. Yet it is often unclear who and where these ‘source’ populations are. Here we demonstrate how viral deep-sequencing can be used to reconstruct HIV-1 transmission networks and to infer the direction of transmission in these networks. We are able to deep-sequence virus from a large population-based sample of infected individuals in Rakai District, Uganda, reconstruct partial transmission networks, and infer the direction of transmission within them at an estimated error rate of 16.3% [8.8–28.3%]. With this error rate, deep-sequence phylogenetics cannot be used against individuals in legal contexts, but is sufficiently low for population-level inferences into the sources of epidemic spread. The technique presents new opportunities for characterizing source populations and for targeting of HIV-1 prevention interventions in Africa.

Suggested Citation

  • Oliver Ratmann & M. Kate Grabowski & Matthew Hall & Tanya Golubchik & Chris Wymant & Lucie Abeler-Dörner & David Bonsall & Anne Hoppe & Andrew Leigh Brown & Tulio Oliveira & Astrid Gall & Paul Kellam , 2019. "Inferring HIV-1 transmission networks and sources of epidemic spread in Africa with deep-sequence phylogenetic analysis," Nature Communications, Nature, vol. 10(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-09139-4
    DOI: 10.1038/s41467-019-09139-4
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