Impact of Molecular Sequence Data Completeness on HIV Cluster Detection and a Network Science Approach to Enhance Detection

Authors

Sepideh Mazrouee
Camden J Hallmark
Ricardo Mora
Natascha Del Vecchio
Rocio Carrasco Hernandez
Michelle Carr
Marlene McNeese
Kayo Fujimoto
Joel O Wertheim

Language

English

Publication Date

11-10-2022

Journal

Scientific Reports

DOI

10.1038/s41598-022-21924-8

PMID

36357480

PMCID

PMC9648870

PubMedCentral® Posted Date

11-10-2022

PubMedCentral® Full Text Version

Post-print

Abstract

Detection of viral transmission clusters using molecular epidemiology is critical to the response pillar of the Ending the HIV Epidemic initiative. Here, we studied whether inference with an incomplete dataset would influence the accuracy of the reconstructed molecular transmission network. We analyzed viral sequence data available from ~ 13,000 individuals with diagnosed HIV (2012-2019) from Houston Health Department surveillance data with 53% completeness (n = 6852 individuals with sequences). We extracted random subsamples and compared the resulting reconstructed networks versus the full-size network. Increasing simulated completeness was associated with an increase in the number of detected clusters. We also subsampled based on the network node influence in the transmission of the virus where we measured Expected Force (ExF) for each node in the network. We simulated the removal of nodes with the highest and then lowest ExF from the full dataset and discovered that 4.7% and 60% of priority clusters were detected respectively. These results highlight the non-uniform impact of capturing high influence nodes in identifying transmission clusters. Although increasing sequence reporting completeness is the way to fully detect HIV transmission patterns, reaching high completeness has remained challenging in the real world. Hence, we suggest taking a network science approach to enhance performance of molecular cluster detection, augmented by node influence information.

Keywords

Humans, HIV Infections, Cluster Analysis, Molecular Epidemiology, Molecular Sequence Data, Epidemics, Phylogeny

Published Open-Access

yes

Recommended Citation

Mazrouee, Sepideh; Hallmark, Camden J; Mora, Ricardo; et al., "Impact of Molecular Sequence Data Completeness on HIV Cluster Detection and a Network Science Approach to Enhance Detection" (2022). Faculty, Staff and Student Publications. 727.
https://digitalcommons.library.tmc.edu/uthsph_docs/727