Department of Population Health, New York University School of Medicine, New York, New York.
Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York.
Am J Epidemiol. 2019 May 1;188(5):862-865. doi: 10.1093/aje/kwy262.
Systems science models use computer-based algorithms to model dynamic interactions between study units within and across levels and are characterized by nonlinear and feedback processes. They are particularly valuable approaches that complement the traditional epidemiologic toolbox in cases in which real data are not available and in cases in which traditional epidemiologic methods are limited by issues such as interference, spatial dependence, and dynamic feedback processes. In this commentary, we propose 2 key contributions that systems models can make to epidemiology: 1) the ability to test assumptions about underlying mechanisms that give rise to population distributions of disease; and 2) help in identifying the types of interventions that have the greatest potential to reduce population rates of disease in the future or in new sites where they have not yet been implemented. We discuss central challenges in the application of systems science approaches in epidemiology, propose potential solutions, and predict future developments in the role that systems science can play in epidemiology.
系统科学模型使用基于计算机的算法来模拟研究单位在不同层次之间的动态相互作用，其特点是非线性和反馈过程。在真实数据不可用的情况下，或者在传统流行病学方法受到干扰、空间依赖性和动态反馈过程等问题限制的情况下，这些模型是特别有价值的方法，可以补充传统的流行病学工具。在这篇评论中，我们提出了系统模型可以为流行病学做出的 2 个重要贡献：1）能够检验导致疾病人群分布的潜在机制的假设；2）有助于确定哪些干预措施最有可能在未来降低人群疾病率，或者在尚未实施的新地点降低人群疾病率。我们讨论了在流行病学中应用系统科学方法的核心挑战，提出了潜在的解决方案，并预测了系统科学在流行病学中可能发挥的作用的未来发展。
