Founded and headed by James Moody, the Duke Network Analysis Center (DNAC), also housed in the Duke Social Science Research Institute (SSRI), is a key partner of DPRC in developing and promoting research in network sampling, network tools and models to advance the understanding of interdependent effects in population and health research.
Moody, who also leads DPRC’s Development Core, integrates DNAC expertise with DPRC’s research priorities especially under DPRC’s Interconnected Social Systems and Population Health thematic research area. Published extensively in the social networks and social theory fields, his research focuses on the structural dynamics of social networks, with substantive emphasis on adolescent social network structure and the spread of HIV/AIDS.
DNAC is a world-class network science and analysis program which highlights Duke's cutting-edge network scholarship, promotes new collaborations, introduces new researchers to network science, and trains scholars in network science methods and applications. DNAC also offers network analysis research services to the wider Duke community, enhancing the university’s network science leadership role both locally and nationally.
Moody, Lisa Keister and M. Giovanna Merli lead DPRC’s signature training activities in social networks and health through the Social Networks and Health Scholar Training program. This program, jointly implemented by DNAC and DPRC, offers an intensive week-long training course in networks methods rarely included in the standard social-science methods sequences regularly taught to health and health-policy scholars.
DNAC and DPRC leadership work with the Duke Medical School and the computer science, machine learning, statistical science and population health sciences to identify opportunities to leverage other innovative research tools and relevant data. With these collaborations, DPRC is leading a Duke initiative to use newly available electronic administrative data in new and untapped ways. Availalbe “big data” includes hospital records, prescription or other electronic medical records (EMR); free-form electronic interaction/trace data (such as those left by online social media activity or purchasing records); and linkages across historically disparate data sources (government, health-systems, and survey-based sources).
This new data-rich—but theory-poor environment—opens many exciting challenges for population scientists. Access to local NC "big data" makes DPRC a leading voice in building best-practices in this new data-rich domain that will be elaborated through the Durham Population Lab (DPL) initiative. New opportunities include integrating health records data with personal interviews, applying social science models to administrative records, exploring best practices collecting sensitive data and addressing crucial questions concerning population generalizability and representation. Tapping into this big data also furthers an examination of how interacting social systems fit together and mutually shape each other in ways that are not possible with population-representative sample surveys.