Jacob Konikoff, Ph.D. candidate
Department of Biostatistics
UCLA Fielding School of Public Health
Tracking and surveillance of the HIV epidemic depend on accurate estimation of the number of new infections in the population. The rate at which these infections occur, known as the incidence, is also critical for effectively designing, targeting, and evaluating prevention efforts. This talk focuses on recent advances in estimating HIV incidence through cross-sectional surveys. These studies do not require longitudinal follow-up of individuals. We develop and discuss biomarker-based sequential classification algorithms that mark individuals in an early disease stage as recent infections. Our findings show both the limitations and growing promise of cross-sectional methods for estimating HIV incidence.