The increasing call for community-level evaluation of "combination HIV prevention" (packages of our best evidence-based HIV prevention interventions) has brought to the forefront the need to develop novel methods of both evaluating these combinations of interventions in aggregate and estimating the relative contributions of individual components of the combined intervention. However, such comprehensive methods do not exist. The recent development of the concept of Community Viral Load (CVL), presents a significant opportunity to evaluate T2 translational research (bedside-to-community) at the community level. CVL is an aggregate biologic measure of HIV-1 viral load from surveillance data that serves as a population-level marker of treatment mediated HIV virologic suppression and HIV transmission risk. We propose to develop novel T2 translational research evaluation methods to assess the relative contribution of intervention components within a combination intervention by bringing together two developing innovations: 1) computational biology modeling of community viral load, and 2) multi-level process pathway analysis of program inputs, process measures of intervention components, and observed changes in CVL. In Phase-I, the investigators will convene to design, implement, and test a computational biology model of community viral load (CVL) based on surveillance data. In the Phase-II of the research, data from phase I will be used generate simulation data of CVL under different conditions of hypothetical multi-level/combination HIV prevention interventions and to design a HIV care continuum process pathway model incorporating observable process measures from each component of the combination intervention. The researchers will then evaluate the process pathway model's ability to identify the relative contribution of individual intervention components. In Phase-III, the investigators will apply the process pathway model to actual data from a CDC sponsored community-wide combination HIV prevention intervention in San Francisco and estimates and the performance of the methods will be reviewed and evaluated by a panel of experts.