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@article{Xi:2022:10.1111/rssc.12544,
author = {Xi, X and Spencer, SEF and Hall, M and Grabowski, MK and Kagaayi, J and Ratmann, O},
doi = {10.1111/rssc.12544},
journal = {Journal of the Royal Statistical Society Series C: Applied Statistics},
pages = {517--540},
title = {Inferring the sources of HIV infection in Africa from deep-sequence data with semi-parametric Bayesian Poisson flow models},
url = {http://dx.doi.org/10.1111/rssc.12544},
volume = {71},
year = {2022}
}

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TY  - JOUR
AB  - 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.
AU  - Xi,X
AU  - Spencer,SEF
AU  - Hall,M
AU  - Grabowski,MK
AU  - Kagaayi,J
AU  - Ratmann,O
DO  - 10.1111/rssc.12544
EP  - 540
PY  - 2022///
SN  - 0035-9254
SP  - 517
TI  - Inferring the sources of HIV infection in Africa from deep-sequence data with semi-parametric Bayesian Poisson flow models
T2  - Journal of the Royal Statistical Society Series C: Applied Statistics
UR  - http://dx.doi.org/10.1111/rssc.12544
UR  - http://hdl.handle.net/10044/1/94532
VL  - 71
ER  -

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