Estimating the Current Routes of Transmission in HIV-1 F1 Subtype Infected Persons in Romania: Differences Between Self-Reporting and Phylogenetic Data

Monitoring the HIV epidemic in Romania has proven challenging due to many factors, including the reluctance of newly diagnosed patients to disclose relevant epidemiological aspects during the clinical interview, such as sexual orientation or the existence of previous issues with injectable drug usag...

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Bibliographic Details
Published in:Pathogens (Basel) 2024-11, Vol.13 (11), p.960
Main Authors: Hohan, Robert, Paraschiv, Simona, Nicolae, Ionelia, Oțelea, Dan
Format: Article
Language:English
Subjects:
Acquired immune deficiency syndrome
AIDS
Bayesian analysis
Cluster analysis
clusters
Datasets
Disease transmission
Epidemics
Epidemiology
Gene sequencing
Heterosexuality
HIV
HIV (Viruses)
Human immunodeficiency virus
Infectious diseases
Maximum likelihood estimation
Nosocomial infections
Pediatrics
phylogenetic
Phylogenetics
Phylogeny
Pol gene
Risk factors
Romania
Self report
Sexual behavior
Sexually transmitted diseases
STD
transmission
Trends
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Summary:Monitoring the HIV epidemic in Romania has proven challenging due to many factors, including the reluctance of newly diagnosed patients to disclose relevant epidemiological aspects during the clinical interview, such as sexual orientation or the existence of previous issues with injectable drug usage. We propose in this study a molecular approach to mitigate this problem with the help of bioinformatic tools, such as cluster analysis of phylogenetic trees. Both a maximum likelihood estimation, as implemented with FastTree, and a Bayesian approach, as used in BEAST, have been applied to our data set of 312 HIV subtype F1 gene sequences. ClusterPicker was used in order to identify groups of sequences and indicate similarities possibly related to the route of transmission. An important observation from this analysis is that transmission between men who have sex with men (MSM) is likely occurring in networks significantly larger than previously assessed by self-reported data (65% from the phylogenetic tree versus 37% from self-declared affiliation). Cluster analysis can help identify risk factors, reveal transmission trends, and, consequently, advise prevention programs.
ISSN:2076-0817
2076-0817
DOI:10.3390/pathogens13110960