The goal of this project is to integrate subject data from local research, clinical and public health entities that are screening for and treating HIV infected individual. Data from these sources will be obtained in a de?identified manner, parsed and then organized into a HIPAA compliant database containing socio?demographic, geographic and phylogenetic information for each subject. The database will be set up to update itself in real?time as new HIV infections are identified in our catchment area. In addition, we will use the viral sequence data to map out the phylogenetic network structure of our local epidemic. We will utilize a background of nationwide sequences obtained HIV sequence repositories to improve the signal in our phylogenetic structures, and use Bayesian maximum likelihood analysis to build these networks in a robust manner, leveraging the bioinformatics expertise at our institution, the University of California, San Diego. We will also update this network structure in a real?time fashion as new infections are identified. Finally we will use this constructed surveillance system to map the socio?demographic, geographic and phylogenetic locations of newly identified acute and early HIV infections, and use this information to direct community specific prevention resources (i.e. needle exchange, education resources etc?) with the ultimate goal of preventing HIV transmission clusters from developing or expanding.
This project will build a real?time database with socio?demographic, geographic and phylogenetic data of HIV infected individuals in the San Diego region. This database will be then used to target HIV prevention resources in a efficient manner to hopefully reduce new transmission of HIV.
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