Prediction of HIV Transmission Cluster Growth With Statewide Surveillance Data.

ABSTRACT:

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

SUBMITTER: Billock RM 

PROVIDER: S-EPMC6692892 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature