Social network analysis and agent-based modeling in social epidemiology.
Social network analysis and agent-based modeling in social epidemiology.
复制标题
DOI：
10.1186/1742-5573-9-1
复制
发表时间：
2012-02-01
期刊：
影响因子：
--
通讯作者：
Galea S
中科院分区：
文献类型：
--
作者：
El-Sayed AM;Scarborough P;Seemann L;Galea S
在过去的五年里，人们对流行病学研究中的系统方法的兴趣有所增长。这些方法可能特别适用于社会流行病学。社会网络分析和基于代理的模型（ABMs）是流行病学文献中使用的两种方法。社会网络分析涉及社交网络的特征，以推断网络结构如何影响网络中的风险暴露。ABM可以促进人口水平的推理显式编程，微观层次的规则在模拟人口随着时间和空间。在本文中，我们讨论了这些模型在社会流行病学研究中的实施，突出了每种方法的优点和缺点。网络分析可能是理解社会传染以及社会互动对人群健康影响的理想方法。然而，网络分析需要网络数据，这可能会牺牲概括性，从目前的网络分析方法的因果推理是有限的。ABM是唯一适合于在多个层次的影响，可能与社会互动产生人口健康的健康决定因素的评估。ABM允许探索复杂疾病病因学中暴露和结果之间的反馈和相互作用。它们还可能提供反事实模拟的机会。然而，ABM的适当实施需要在机械严谨性和模型简约性之间取得平衡，并且复杂模型的输出精度有限。社会网络和代理人为基础的方法是有前途的社会流行病学，但每种方法的持续发展是必要的。
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.