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

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, Deenan Pillay, Joseph Kagaayi, Godfrey Kigozi, Thomas C. Quinn, Maria J. Wawer, Oliver Laeyendecker, David Serwadda, Ronald H. Gray and Christophe Fraser
Additional contact information
Oliver Ratmann: Imperial College London
M. Kate Grabowski: Johns Hopkins School of Medicine
Matthew Hall: University of Oxford
Tanya Golubchik: University of Oxford
Chris Wymant: Imperial College London
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
Joseph Kagaayi: Rakai Health Sciences Program
Godfrey Kigozi: Rakai Health Sciences Program
Thomas C. Quinn: Johns Hopkins School of Medicine
Maria J. Wawer: Rakai Health Sciences Program
Oliver Laeyendecker: Johns Hopkins School of Medicine
David Serwadda: Rakai Health Sciences Program
Ronald H. Gray: Johns Hopkins School of Medicine
Christophe Fraser: University of Oxford

Nature Communications, 2019, vol. 10, issue 1, 1-13

Abstract: 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.

Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-019-09139-4 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-09139-4

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-019-09139-4

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().