HIV incidence is the rate at which new HIV infections occur in populations. While HIV prevalence measures overall disease burden, HIV incidence tracks the leading edge of the HIV/AIDS epidemic. Accurate HIV incidence estimates are critical for monitoring the HIV/AIDS epidemic, identifying populations at high risk of HIV acquisition, targeting prevention efforts, and designing and evaluating HIV prevention trials. Current methods for cross-sectional HIV incidence determination are insufficiently accurate. Our goal is to develop and validate accurate, cost-effective methods for cross-sectional HIV incidence determination. Our hypothesis is that diverse laboratory assays and robust statistical modeling can be combined to improve the accuracy of cross-sectional HIV incidence estimates. We will focus on analysis of HIV incidence in both subtype B (the major subtype driving the HIV/AIDS epidemic in the United States) and subtype C (the major subtype driving the HIV/AIDS epidemic in sub-Saharan Africa); other subtypes prevalent in sub-Saharan Africa will also be analyzed. The Specific Aims of the project are: Aim 1: Continue to build a repository of diverse, well-characterized samples with information on the duration of HIV infection. Analyze the samples using serologic HIV incidence assays. Aim 2: Further develop and validate a novel high resolution melting (HRM) assay for HIV diversity. Determine whether HIV diversity can be used as a biomarker to differentiate between individuals with recent vs. chronic HIV infection. Aim 3: Use statistical analysis and mathematical modeling to assess the accuracy of methods for HIV incidence determination. Apply those approaches to data from Aim 1 (CD4 cell count, HIV viral load, and data from serologic assays) and Aim 2 (data from the HRM assay) to identify methods for HIV incidence determination that perform well in a wide variety of settings, and to assess their relative costs. Our repository will include samples from at least 19 completed clinical trials, cohort studies, and research projects representing key geographic, demographic, and clinically-relevant populations. Over 10,000 of the samples are already in hand. We will integrate laboratory science with statistical analysis and mathematical modeling in all phases of the project, and will evaluate a broad range of design parameters, including use of individual assays versus multi-assay algorithms, use of different assay cutoffs, and sequential ordering of assays. We believe that this comprehensive approach will lead to identification of accurate and cost-effective methods for cross-sectional HIV incidence determination. ) )