Inferring the sources of HIV infection in Africa from deep-sequence data with semi-parametric Bayesian Poisson flow models
File(s)
Author(s)
Type
Journal Article
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, al-lows for sampling bias adjustments, and estimation of gender- and age-specific transmis-sion 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 sub-stantive evidence that adolescent and young women are predominantly infected through age-disparate relationships.
Date Issued
2022-06-01
Date Acceptance
2022-01-17
Citation
Journal of the Royal Statistical Society Series C: Applied Statistics, 2022, 71 (3), pp.517-540
ISSN
0035-9254
Publisher
Royal Statistical Society
Start Page
517
End Page
540
Journal / Book Title
Journal of the Royal Statistical Society Series C: Applied Statistics
Volume
71
Issue
3
License URL
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and repro-duction in any medium, provided the original work is properly cited.© 2022 The Authors.Journal of the Royal Statistical Society: Series C (Applied Statistics)published by John Wiley & Sons Ltd on behalf ofRoyal Statistical Society
Sponsor
Bill & Melinda Gates Foundation
Grant Number
1705CR001/LD1
Subjects
Science & Technology
Physical Sciences
Statistics & Probability
Mathematics
flow models
Gaussian process
infectious disease epidemiology
origin-destination models
phylodynamics
Stan
TRANSMISSION
INFERENCE
PREVENTION
NETWORKS
DISEASE
MEN
Statistics & Probability
0104 Statistics
Publication Status
Published
Date Publish Online
2022-03-13