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Inferring the sources of HIV infection in Africa from deep‐sequence data with semi‐parametric Bayesian Poisson flow models

Author

Listed:
  • Xiaoyue Xi
  • Simon E. F. Spencer
  • Matthew Hall
  • M. Kate Grabowski
  • Joseph Kagaayi
  • Oliver Ratmann
  • Rakai Health Sciences Program and PANGEA‐HIV

Abstract

Pathogen deep‐sequencing is an increasingly routinely used technology in infectious disease surveillance. We present a semi‐parametric Bayesian Poisson model to exploit these emerging data for inferring infectious disease transmission flows and the sources of infection at the population level. The framework is computationally scalable in high‐dimensional flow spaces thanks to Hilbert Space Gaussian process approximations, allows for sampling bias adjustments, and estimation of gender‐ and age‐specific transmission flows at finer resolution than previously possible. We apply the approach to densely sampled, population‐based HIV deep‐sequence data from Rakai, Uganda, and find substantive evidence that adolescent and young women were predominantly infected through age‐disparate relationships in the study period 2009–2015.

Suggested Citation

  • Xiaoyue Xi & Simon E. F. Spencer & Matthew Hall & M. Kate Grabowski & Joseph Kagaayi & Oliver Ratmann & Rakai Health Sciences Program and PANGEA‐HIV, 2022. "Inferring the sources of HIV infection in Africa from deep‐sequence data with semi‐parametric Bayesian Poisson flow models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 517-540, June.
    • Handle: RePEc:bla:jorssc:v:71:y:2022:i:3:p:517-540
    DOI: 10.1111/rssc.12544
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