Prediction of HIV Transmission Cluster Growth With Statewide Surveillance Data

Background: Prediction of HIV transmission cluster growth may help guide public health action. We developed a predictive model for cluster growth in North Carolina (NC) using routine HIV surveillance data. Methods: We identified putative transmission clusters with >= 2 members through pairwise genetic distances <= 1.5% from HIV-1 pol sequences sampled November 2010-December 2017 in NC. Clusters established by a baseline of January 2015 with any sequences sampled within 2 years before baseline were assessed for growth (new diagnoses) over 18 months. We developed a predictive model for cluster growth incorporating demographic, clinical, temporal, and contact tracing characteristics of baseline cluster members. We internally and temporally externally validated the final model in the periods January 2015-June 2016 and July 2016-December 2017. Results: Cluster growth was predicted by larger baseline cluster size, shorter time between diagnosis and HIV care entry, younger age, shorter time since the most recent HIV diagnosis, higher proportion with no named contacts, and higher proportion with HIV viremia. The model showed areas under the receiver-operating characteristic curves of 0.82 and 0.83 in the internal and temporal external validation samples. Conclusions: The predictive model developed and validated here is a novel means of identifying HIV transmission clusters that may benefit from targeted HIV control resources.