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

Research output: Contribution to journalArticle

  • Oliver Ratmann
  • M Kate Grabowski
  • Matthew Hall
  • Tanya Golubchik
  • Chris Wymant
  • Lucie Abeler-Dörner
  • David Bonsall
  • Anne Hoppe
  • Tulio de Oliveira
  • Astrid Gall
  • Paul Kellam
  • Deenan Pillay
  • Joseph Kagaayi
  • Godfrey Kigozi
  • Thomas C Quinn
  • Maria J Wawer
  • Oliver Laeyendecker
  • David Serwadda
  • Ronald H Gray
  • Christophe Fraser

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Original languageEnglish
Article number1411
JournalNature Communications
Volume10
Early online date1 Mar 2019
DOIs
Publication statusPublished - 29 Mar 2019

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.

    Research areas

  • ethics, HIV infections, Phylogenetics, software

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