Impact of molecular sequence data completeness on HIV cluster detection and a network science approach to enhance detection.

Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE

Original Publication: London : Nature Publishing Group, copyright 2011-

HIV Infections* ; Epidemics*; Humans ; Cluster Analysis ; Molecular Epidemiology ; Molecular Sequence Data ; Phylogeny

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.
(© 2022. The Author(s).)

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P30 AI036214 United States AI NIAID NIH HHS; UL1 TR001442 United States TR NCATS NIH HHS; NU62PS924515 United States CC CDC HHS; R01 AI135992 United States AI NIAID NIH HHS

Date Created: 20221110 Date Completed: 20221114 Latest Revision: 20240124

20240124

PMC9648870

10.1038/s41598-022-21924-8

36357480