Minority Health and Health Equity Archive

Permanent URI for this collectionhttp://hdl.handle.net/1903/21769

Welcome to the Minority Health and Health Equity Archive (MHHEA), an electronic archive for digital resource materials in the fields of minority health and health disparities research and policy. It is offered as a no-charge resource to the public, academic scholars and health science researchers interested in the elimination of racial and ethnic health disparities.

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Subject: search.filters.subject.Agent-based models

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    Extremism Propagation in Social Networks with Hubs
    (2008) Franks, Daniel W.; Noble, Jason; Kaufman, Peter; Stagl, Sigrid
    One aspect of opinion change that has been of academic interest is the impact of people with extreme opinions (extremists) on opinion dynamics. An agent-based model has been used to study the role of small-world social network topologies on general opinion change in the presence of extremists. It has been found that opinion convergence to a single extreme occurs only when the average number of network connections for each individual is extremely high. Here, we extend the model to examine the effect of positively skewed degree distributions, in addition to small-world structures, on the types of opinion convergence that occur in the presence of extremists. We also examine what happens when extremist opinions are located on the well-connected nodes (hubs) created by the positively skewed distribution. We find that a positively skewed network topology encourages opinion convergence on a single extreme under a wider range of conditions than topologies whose degree distributions were not skewed. The importance of social position for social influence is highlighted by the result that, when positive extremists are placed on hubs, all population convergence is to the positive extreme even when there are twice as many negative extremists. Thus, our results have shown the importance of considering a positively skewed degree distribution, and in particular network hubs and social position, when examining extremist transmission.
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    Interorganizational Network Structures and Diffusion of Information through a Health System
    (2007) Gibbons, Deborah
    Objectives. I used computational models to test the relationship between interorganizational network structures and diffusion of moderate- to high-priority health information throughout a system. I examined diffusion effects of mean and variance in organizational partnering tendencies, arrangement of ties among subgroups of the system, and the diffusing organization’s effective network size. Methods. I used agent-based models to simulate local information-sharing processes and observe the outcomes of system-level diffusion. Graphs of diffusion curves demonstrated differences among intergroup structures, and regression models were used to test effects of parameterized and emergent network variables on diffusion. Results. The average tendency of participating organizations to engage in partnerships with other network members influenced diffusion of information, but variance in partnering tendencies had little effect. Fully connected subgroup structures outperformed hierarchical connections among subgroups, and all outperformed group-to-group chains. Introduction of a small proportion of randomness in connections among members of the chain structure improved diffusion without increasing network density. Finally, greater effective size in the diffusing organization’s network increased diffusion of information. Conclusions. Small interventions that build connecting structures among subgroups within a health system can be particularly effective at facilitating natural dissemination of information.
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    Virtual epidemic in a virtual city: simulating the spread of influenza in a US metropolitan area
    (2008) Lee, Bruce; Bedford, Virginia; Roberts, Mark; Carley, Kathleen
    A wide variety of biologic, physiologic, social, economic, and geographic factors may affect the transmission, spread, and impact of influenza. Recent concerns about an impending influenza epidemic have generated a need for predictive computer simulation models to forecast the spread of influenza and the effectiveness of prevention and control strategies. We designed an agent-based computer simulation of a theoretical influenza epidemic in Norfolk, Va, that included extensive city-level details and computer representations of every Norfolk citizen, including their expected behavior and social interactions. The simulation introduced 200 infected cases on November 27, 2002 (day 87), and tracked the progress of the epidemic. On average, the prevalence peaked on day 178 (12.2% of the population). Our model showed a cyclical variation in influenza cases by day of the week with fewer people being exposed on weekends, differences in emergency room and clinic visits by day of the week, an earlier peak in influenza cases, and persistent high prevalence among people age 65 or older and the daily prevalence of infection among health-care workers. The level of detail included in our simulation model made these findings possible. Compared with other existing models, our model has a very extensive and detailed social network, which may be important because individuals with more social interactions and extensive social networks may be more likely to spread influenza. Our simulation may serve as a virtual laboratory to better understand the way different factors and interventions affect the spread of influenza.