Background: Identifying populations at high risk of HIV transmission is critical for prioritizing treatment and prevention resources and achieving the UNAIDS 90-90-90 Targets.
Methods: HIV transmission rates can be estimated from phylogenetic trees as viral lineage-level diversification rates. To identify HIV-1 transmission foci in British Columbia, Canada, we inferred diversification rates from phylogenetic trees of 36 271 HIV-1 sequences from 9630 anonymized individuals. Diversification rates were combined with sociodemographic and clinical data, then aggregated by patients' area of residence to predict the distribution of new HIV cases between 2008 and 2018. The predictive power of the model was compared with a phylogenetically uninformed model.
Findings: Aggregated diversification rate measures were predictive of new HIV cases in the subsequent year after adjusting for prevalent and incident cases in the previous year. For every one-unit increase in the mean of the top five diversification rates, the number of new HIV cases increased by on average 1·38-fold (95% CI, 1·28-1·49). In a blind prediction of 2018 cases, diversification rate improved the model's specificity by 12%, accuracy by 9%, top 20 agreement by 100%, and correlation of predicted and observed values by 162% relative to a model that incorporated epidemiological data alone.
Interpretation: By predicting the distribution of future HIV cases, a combined phylogenetic and epidemiological approach identifies hotspots where public health resources are needed most.
Funding: Canadian Institutes of Health Research, University of British Columbia, Public Health Agency of Canada, Genome Canada, Genome BC, Michael Smith Foundation for Health Research, and BC Centre for Excellence in HIV/AIDS.
Keywords: Epidemiology; HIV; Phylogenetics; Predictive model; Transmission.
Copyright © 2019. Published by Elsevier B.V.