@article{7b5f33d958284f1c819db812885181ec,
title = "Inferring the sources of HIV infection in Africa from deep-sequence data with semi-parametric Bayesian Poisson flow models",
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.",
keywords = "Gaussian process, Stan, flow models, infectious disease epidemiology, origin-destination models, phylodynamics",
author = "{Rakai Health Sciences Program and PANGEA-HIV} and Xiaoyue Xi and Spencer, {Simon E.F.} and Matthew Hall and Grabowski, {M. Kate} and Joseph Kagaayi and Oliver Ratmann",
note = "Funding Information: This study was supported by the Bill & Melinda Gates Foundation (OPP1175094, OPP1084362), the National Institute of Allergy and Infectious Diseases (R01AI110324, U01AI100031, U01AI075115, R01AI102939, K01AI125086‐01), National Institute of Mental Health (R01MH107275), the National Institute of Child Health and Development (RO1HD070769, R01HD050180), the Division of Intramural Research of the National Institute for Allergy and Infectious Diseases, the World Bank, the Doris Duke Charitable Foundation, the Johns Hopkins University Center for AIDS Research (P30AI094189), and the Presidents Emergency Plan for AIDS Relief through the Centers for Disease Control and Prevention (NU2GGH000817). We acknowledge data management support provided in part by the Office of Cyberinfrastructure and Computational Biology at the National Institute for Allergy and Infectious Diseases, computational support through the Imperial College Research Computing Service, doi: 10.14469/hpc/2232 . We thank the participants of the Rakai Community Cohort Study and the many staff and investigators who made this study possible, as well as the PANGEA‐HIV steering committee, the RCCS leadership, and two anonymous reviewers for their helpful comments on this manuscript. Publisher Copyright: {\textcopyright} 2022 The Authors. Journal of the Royal Statistical Society: Series C (Applied Statistics) published by John Wiley & Sons Ltd on behalf of Royal Statistical Society.",
year = "2022",
month = jun,
doi = "10.1111/rssc.12544",
language = "English (US)",
volume = "71",
pages = "517--540",
journal = "Journal of the Royal Statistical Society. Series C: Applied Statistics",
issn = "0035-9254",
publisher = "Wiley-Blackwell",
number = "3",
}