Social network analysis and agent-based modeling in social epidemiology

Department of Public Health, University of Oxford, Oxford, UK. .
Epidemiologic Perspectives & Innovations (Impact Factor: 1.58). 02/2012; 9(1):1. DOI: 10.1186/1742-5573-9-1
Source: PubMed


The past five years have seen a growth in the interest in systems approaches in epidemiologic research. These approaches may be particularly appropriate for social epidemiology. Social network analysis and agent-based models (ABMs) are two approaches that have been used in the epidemiologic literature. Social network analysis involves the characterization of social networks to yield inference about how network structures may influence risk exposures among those in the network. ABMs can promote population-level inference from explicitly programmed, micro-level rules in simulated populations over time and space. In this paper, we discuss the implementation of these models in social epidemiologic research, highlighting the strengths and weaknesses of each approach. Network analysis may be ideal for understanding social contagion, as well as the influences of social interaction on population health. However, network analysis requires network data, which may sacrifice generalizability, and causal inference from current network analytic methods is limited. ABMs are uniquely suited for the assessment of health determinants at multiple levels of influence that may couple with social interaction to produce population health. ABMs allow for the exploration of feedback and reciprocity between exposures and outcomes in the etiology of complex diseases. They may also provide the opportunity for counterfactual simulation. However, appropriate implementation of ABMs requires a balance between mechanistic rigor and model parsimony, and the precision of output from complex models is limited. Social network and agent-based approaches are promising in social epidemiology, but continued development of each approach is needed.

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    • "Overall, we demonstrate that the intersection of SCT and S-D logic provides insights that lead to an advanced understanding of resource integration in service ecosystems, and we argue that SCT should play a more prominent role in supporting S-D logic research. network theory has been criticized as overly simplistic and underestimating or insufficiently accounting for human or interrelational qualities (El-Sayed et al., 2012; Helbing, 2012; Smith, 2010). "
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    • "ABM embraces basic features of complex systems such as self-organization, chaos and adaptation, which otherwise could have very difficult to achieve using mathematical formulation. Such modeling adapts to simulate complex systems in scenarios like economics, integrative biology, social network analysis and urban planning among others (Taghawi-Nejad, 2013; Holcombe et al., 2012; El-Sayed et al., 2012; Schwarz et al., 2012). More complex phenomena can be modelled by integrating ABM with other evolutionary algorithms (Bonabeau, 2002; Bouarfa et al., 2013). "
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    • "For complex problems involving health systems and adaptive behavior computational methods become very popular (9, 19). Agent-based models (ABMs) have recently become powerful tools to estimate the effects of interventions on community and population health (20, 21). At the same time, ABMs allow one to track an individual and examine what could potentially happen to this specific individual over the course of time under different scenarios (22). "
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