Systems modeling to advance the promise of data science in epidemiology

Abstract

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.