Abstract
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 pol 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.