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Icon for: Sophia Giebultowicz


University of North Carolina at Chapel Hill


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Integrating Spatial and Social Network Analysis in Disease Transmission Studies

This project develops new methods that integrate two substantive research areas, social networks and neighborhood effects. Although both potentially influence health outcomes and are separately treated in research, the simultaneous effects of social interaction and local environment remain relatively unexplored. This study uniquely combines and integrates social network and spatial analytic methods to better understand how social connectivity and interaction, coupled with local neighborhood and environmental effects, affect disease transmission risk. The incidence of two diarrheal illnesses, cholera and shigellosis, is analyzed both in space and within kinship-based social networks in Matlab, Bangladesh. For our analysis, we use spatially referenced longitudinal demographic data on a study population of approximately 200,000, from 1986 to 2009, and all laboratory-confirmed cholera and shigellosis cases during the same time frame. We create matrices of kinship ties between households using a complete network design, along with distance matrices to model spatial relationships. Using Moran’s I statistics, clustering is measured within both social and spatial matrices; combined spatial effects-spatial disturbance models are built to simultaneously analyze spatial and social effects while controlling for local environmental context. Results indicate that cholera and shigellosis consistently cluster in space, with less frequent clustering within social networks. These findings suggest that neighborhood effects and the local environment are most important for understanding transmission of both diseases; however, kinship-based social networks also influence transmission, but to a lesser extent. This research offers valuable insight for prevention efforts, while also illustrating the utility and benefit of combining spatial and social methodological approaches.