Social network analysis has become increasingly central to the study of HIV/STI transmission dynamics and disparities. For this approach to reach its full potential, the theory and methodology must be empirically grounded. This requires a comprehensive analytic strategy that starts with a feasible plan for sampling networks, and uses a statistical framework to leverage the sample information for understanding of population level network structure and dynamics. In this project, our team seeks to build on an innovative research agenda that has developed the statistical theory, methods and programs needed to establish a principled approach to network epidemiology. We seek to extend the current methodology to support comprehensive modeling for dynamic networks.
Our specific aims are to: 1. develop the statistical theory and methodology for modeling network dynamics a. Refine and extend the current ERGM approach to modeling partnership dynamics. b. Develop extensions of ERGM to handle changing population size and composition. c. Create new network visualization tools designed specifically for epidemiological applications. 2. Integrate the new methods into the stat net software for network analysis and simulation a. Develop robust code for implementing the new methods b. Develop comprehensive help functions, manuals, and tutorials c. Ensure public access by publishing the code on CRAN and exploiting internet-based tools. 3. Develop training resources for researchers from different field's a. Develop a 5 day workshop in network theory, study design, analysis, simulation and visualization. b. Develop a 2 day training workshop on using stat net for network analysis. c. Develop 1 day training workshops for interdisciplinary outreach. Our project team comprises an interdisciplinary group of individuals with a long track record of working together, deep expertise in the application of network methods to STI research and prevention, experience in capacity building, and a commitment to the development of publicly accessible software. Our goal is to advance the science of network analysis, provide innovative research tools for research in epidemiology and public health, and lower the barriers to accessing these new tools.
Social network theory and methods are increasingly important in the study of HIV transmission, and for understanding the large persistent disparities in HIV prevalence both globally and in the United States. This project seeks to extend the capabilities of current methods for empirical network research through a combination of theoretical development, computer package implementation, and training workshops.
|Goodreau, Steven M; Carnegie, Nicole B; Vittinghoff, Eric et al. (2014) Can male circumcision have an impact on the HIV epidemic in men who have sex with men? PLoS One 9:e102960|
|Krivitsky, Pavel N; Handcock, Mark S (2014) A Separable Model for Dynamic Networks. J R Stat Soc Series B Stat Methodol 76:29-46|
|Knopf, Amelia; Agot, Kawango; Sidle, John et al. (2014) "This is the medicine:" A Kenyan community responds to a sexual concurrency reduction intervention. Soc Sci Med 108:175-84|
|Khanna, Aditya S; Goodreau, Steven M; Gorbach, Pamina M et al. (2014) Modeling the impact of post-diagnosis behavior change on HIV prevalence in Southern California men who have sex with men (MSM). AIDS Behav 18:1523-31|
|Khanna, Aditya S; Dimitrov, Dobromir T; Goodreau, Steven M (2014) What can mathematical models tell us about the relationship between circular migrations and HIV transmission dynamics? Math Biosci Eng 11:1065-90|
|Hunter, David R; Goodreau, Steven M; Handcock, Mark S (2013) ergm.userterms: A Template Package for Extending statnet. J Stat Softw 52:i02|
|Goodreau, Steven M (2013) Is 2 a "high number of partners"? Modeling, data, and the power of concurrency. Sex Transm Dis 40:61|
|Knopf, Amelia; Morris, Martina (2012) Lack of association between concurrency and HIV infection: an artifact of study design. J Acquir Immune Defic Syndr 60:e20-1; author reply e21|