Abstract
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.
Highlights
Social epidemiology and systems thinking Social epidemiology is concerned with the social variation in, and the social determinants of the distribution of health and disease [1]
This branch of epidemiology is fundamentally interested in the influences of social factors–such as individual attributes [2,3]; behaviors [4,5]; constructs of social interaction [6]; contextual influences [5,7]; and the influences of the allocation of individuals in space on the distribution of health and disease in populations [8,9]
As social factors may interact in complex ways to determine health and disease risk, the current “risk factor” approach to epidemiology, which emphasizes decontextualized, independent effect measures for exposures may not be appropriate [11,12]
Summary
Social epidemiology and systems thinking Social epidemiology is concerned with the social variation in, and the social determinants of the distribution of health and disease [1]. ABMs are well-suited for research that is concerned with understanding social processes because they maintain the centrality of the individual agent and its attributes, characteristics, and behaviors in the production of population-level phenomena This is contrasted with other methods, such as regression models or differential equations (e.g., laws that determine dynamics of predators and prey), which focus on aggregated data [63]. Applications of agent-based modeling in social epidemiologic research ABMs place a focus on the individual and the individual’s characteristics and interactions in time and space They allow investigators to run multiple simulations under various model conditions, thereby isolating the effects of particular conditions on outcomes of interest. These approaches can require considerable computing resources for efficient use [62]
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