Use of molecular HIV surveillance data and predictive modeling to prioritize persons for transmission-reduction interventions.

Background: To develop a predictive model to prioritize persons with a transmissible HIV viral load for transmission-reduction interventions.

Methods: New York City (NYC) HIV molecular surveillance data from 2010 to 2013 were used to build a model to predict the probability that the partial pol gene of the virus of a person with a transmissible HIV viral load (>1500 copies/ml) would be genetically similar to that of a person with a new HIV infection (diagnosis at stage 0 or 1 according to the revised Centers for Disease Control and Prevention classification system). Data from 2013 to 2016 were then used to validate the model and compare it with five other selection strategies that can be used to prioritize persons for transmission-reduction interventions.

Results: A total of 10 609 persons living with HIV (PLWH) were included in the development dataset, and 8257 were included in the validation dataset. Among the six selection strategies, the predictive model had the highest area under the receiver operating characteristic curve (AUC) [0.86, 95% confidence interval (CI) 0.84--0.88], followed by the 'Young MSM' (0.79, 95% CI 0.77--0.82), 'MSM with high viral loads' (0.74, 95% CI 0.72--0.76), 'Random sample of MSM' (0.73, 95% CI 0.71--0.76), 'Persons with high viral loads' (0.56, 95% CI 0.54--0.59), and 'Random sample' (0.50, 95% CI 0.48--0.53) strategies.

Conclusions: Jurisdictions should consider applying predictive modeling to prioritize persons with a transmissible viral load for transmission-reduction interventions and to evaluate its feasibility and effectiveness.