Amanda Castel, MD, MPH, and Marcos Perez-Losada, PhD, MS
Efforts to track the spread of HIV in Washington, DC have relied primarily on routinely collected health department surveillance and laboratory data, and cross-sectional surveys; however, these methods are not able to assess the real-time spread of HIV as they are not very efficient in their ability to characterize HIV transmission patterns and networks. Combining epidemiologic and molecular sequence data can help describe transmission rates and time between transmission events. The collaborators on this grant are currently working to describe HIV transmission patterns among newly diagnosed persons who are participating in a longitudinal observational cohort study of HIV+ persons in care in DC. The proposed supplement activities would provide additional data on viremic persons (i.e., viral loads >1500 copies/ml) who are potential transmitters.
Using data from these patients, the researchers seek to conduct real-time characterization of the population dynamics of HIV in a group of viremic persons to characterize their genetic diversity inclusive of subtypes, recombinant forms, transmission networks, drug mutations, temporal trends in diversity, and rates of genetic exchange; and identify associations between clinical and demographic variables and viral diversity and dynamics to determine if clinical and epidemiological variables co-occur with HIV diversity within an evolutionary framework. Resistance date will be gathered from the DC Department of Health as part of routine molecular HIV surveillance, clinical data, and behavioral data through cross-sectional surveys. Researchers will collect and conduct primary analysis of HIV sequence data from participant isolates and compare transmission networks based on epidemiologic, genetic and geospatial data to make inferences regarding viral genetic diversity, recombination, drug resistance mutations, and transmission patterns. The team will use a web-based platform that makes phylodynamic analysis of high-throughput biomedical data fast, accessible, and reproducible. Triangulation of these data will assist in identifying clusters of HIV transmission for targeted prevention, and inform clinicians as to the prevalence of transmitted drug resistance to guide treatment.