HIV Care Prioritization Using Phylogenetic Branch Length
Published in Journal of Acquired Immune Deficiency Syndromes, 2021
Recommended citation: Moshiri N, Smith DM, Mirarab S (2021). "HIV Care Prioritization Using Phylogenetic Branch Length." Journal of Acquired Immune Deficiency Syndromes. 86(5):626–637. doi:10.1097/QAI.0000000000002612
The structure of the HIV transmission networks can be dictated by just a few individuals. Public health intervention, such as ensuring people living with HIV adhere to antiretroviral therapy and remain virally suppressed, can help control the spread of the virus. However, such intervention requires using limited public health resource allocations. Determining which individuals are most at risk of transmitting HIV could allow public health officials to focus their limited resources on these individuals.
Molecular epidemiology can help prioritize people living with HIV by patterns of transmission inferred from their sampled viral sequences. Such prioritization has been previously suggested and performed by monitoring cluster growth. In this article, we introduce Prioritization using AnCesTral edge lengths (ProACT), a phylogenetic approach for prioritizing individuals living with HIV.
ProACT starts from a phylogeny inferred from sequence data and orders individuals according to their terminal branch length, breaking ties using ancestral branch lengths. We evaluated ProACT on a real data set of 926 HIV-1 subtype B pol data obtained in San Diego between 2005 and 2014 and a simulation data set modeling the same epidemic. Prioritization methods are compared by their ability to predict individuals who transmit most after the prioritization.
Across all simulation conditions and most real data sampling conditions, ProACT outperformed monitoring cluster growth for multiple metrics of prioritization efficacy.
The simple strategy used by ProACT improves the effectiveness of prioritization compared with state-of-the-art methods that rely on monitoring the growth of transmission clusters defined based on genetic distance.