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Published March 17, 2023 | Version 1.0
Dataset Open

Phylogenetic and epidemiologic data relating to age-specific HIV incidence and transmission in Rakai, Uganda, 2003-2018.

Authors/Creators

  • 1. Imperial College London
  • 2. Rakai Health Science Program
  • 3. Johns Hopkins Medicine
  • 4. University of Oxford
  • 5. Bill and Melinda Gates Foundation
  • 6. Johns Hopkins
  • 7. The University of Sydney Institute for Infectious Diseases
  • 8. Johns Hopkins University
  • 9. Centers for Disease Control and Prevention,
  • 10. Columbia Mailman School of Public Health,
  • 11. School of Medicine, Makerere University,
  • 12. University of Warwick
  • 13. Johns Hopkins University
  • 14. Makerere University
  • 15. London School of Infectious and Tropical Diseases
  • 16. Makerere University School of Public Health
  • 17. Johns Hopkins School of Medicine

Contributors

  • 1. Imperial College London
  • 2. Rakai Health Sciences Program
  • 3. imperial College London
  • 4. Johns Hopkins School of Medicine
  • 5. Big Data Institute
  • 6. Bill and Melinda Gates Foundation
  • 7. Nuffield Department of Medicine, University of Oxford
  • 8. Johns Hopkins University
  • 9. National Institute of Allergy and Infectious Diseases
  • 10. US Centers for Disease Control and Prevention
  • 11. Columbia Mailman School of Public Health
  • 12. Makerere University College of Health Sciences
  • 13. University of Warwick
  • 14. Johns Hopkins School of Public Health
  • 15. London School of Hygiene and Tropical Medicine

Description

This repository contains the data for the analyses presented in the paper Growing gender inequity in HIV infection in Africa: sources and policy implications by M. Monod, A. Brizzi, R. Galiwango, R. Ssekubugu, Y. Chen, X. Xi et al. available in the pre-print https://doi.org/10.1101/2023.03.16.23287351 

We thank all contributors, program staff and participants to the Rakai Community Cohort Study; all members of the PANGEA-HIV consortium, the Rakai Health Sciences Program, and CDC Uganda for comments on an earlier version of the manuscript.

We also extend our gratitude to the Imperial College Research Computing Service and the Biomedical Research Computing Cluster at the University of Oxford for providing the computational resources to perform this study. Additionally, we thank the Office of Cyberinfrastructure and Computational Biology at the National Institute for Allergy and Infectious Diseases for data management support; and Zulip for sponsoring team communications through the Zulip Cloud Standard chat app. 

All analysis code is available from https://github.com/MLGlobalHealth/phyloSI-RakaiAgeGender.

Notes

Project leaders: Oliver Ratmann, Kate Grabowski, Joseph Kagaayi Researchers: all others Keywords: HIV prevention; HIV transmission; phylogenetics Grants: OR, MKG, CF, DB report grants from the Bill & Melinda Gates Foundation during the conduct of this study. MKG, MJW, RHG, LCW report grants from the National Institutes of Health during the conduct of this study. ABr, CY, XX report an EPSRC PhD studentship during the conduct of this study. Dr. Wawer and Dr. Gray are paid consultants to the Rakai Health Sciences Program and serves on its Board of Directors. This arrangement has been reviewed and approved by the Johns Hopkins University in accordance with its conflict of interest policies. Licencse: CC-BY Communities: AfricArXiv - the pan-African Open Access portal; Africa Research Database: Zenodo

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Additional details

Related works

Is supplement to
Preprint: 10.1101/2023.03.16.23287351 (DOI)

References

  • Golubchik, Tanya et al (2022). HIV-phyloTSI: Subtype-independent estimation of time since HIV-1 infection for cross-sectional measures of population incidence using deep sequence data
  • Wymant, Chris et al (2017). PHYLOSCANNER: Inferring Transmission from Within- and Between-Host Pathogen Genetic Diversity
  • Xi, Xiaoyue et al (2022). Inferring the Sources of HIV Infection in Africa from Deep-Sequence Data with Semi-Parametric Bayesian Poisson Flow Models