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Amplification of Near Full-length HIV-1 Proviruses for Next-Generation Sequencing

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Reconstructing contact network parameters from viral phylogenies.

Rosemary M McCloskey¹, Richard H Liang¹, Art F Y Poon²

¹BC Centre for Excellence in HIV/AIDS, Vancouver, Canada.

Virus Evolution

|November 8, 2016

Summary

This study introduces a new method to analyze disease spread using contact network models and approximate Bayesian computation (ABC). It reveals preferential attachment patterns in HIV transmission networks, highlighting the importance of social structures in epidemics.

Keywords:

approximate Bayesian computation contact network human immunodeficiency virus.phylodynamics phylogenetics transmission tree

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Area of Science:

Epidemiology
Network Science
Computational Biology

Background:

Traditional disease spread models often assume homogeneous mixing, ignoring complex social contact structures.
These social structures significantly influence epidemic dynamics and transmission rates.
Contact network models explicitly represent transmission pathways, offering a more realistic approach.

Purpose of the Study:

To develop a novel method for estimating contact network parameters from viral phylogenies.
To investigate the role of preferential attachment in disease spread within human populations.
To apply this method to real-world HIV sequence data.

Main Methods:

Developed a likelihood-free inference strategy using approximate Bayesian computation (ABC).
Combined adaptive sequential Monte Carlo, Gillespie simulation, and a kernel-based tree similarity score.
Applied the method to simulated transmission trees and empirical HIV sequence datasets.

Main Results:

Accurately estimated preferential attachment (PA) strength and the number of infected nodes in simulated data.
Observed sub-linear PA power across all HIV datasets, with higher PA in injection drug user networks.
Found that mean network degree and total node count were not reliably estimated by ABC.

Conclusions:

Contact network structure is crucial for accurate phylodynamic inference.
The developed ABC method can quantitatively explore epidemic contact structures.
Findings emphasize the need to incorporate social network complexity into disease modeling.