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

TL;DR

This paper introduces a scalable semi-parametric Bayesian model leveraging deep-sequence data to infer HIV transmission sources and flows in Africa, revealing key demographic infection patterns.

Contribution

It develops a novel semi-parametric Bayesian Poisson flow model that handles high-dimensional data and adjusts for sampling biases, enabling detailed transmission analysis.

Findings

Adolescent and young women mainly infected through age-disparate relationships.

Model effectively estimates gender- and age-specific transmission flows.

Framework is computationally scalable for large, complex datasets.

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 are predominantly infected through age-disparate relationships.