Systems science simulation modeling to inform urban health policy and planning

More than half of the population in the world lives in cities and urban populations are still rapidly expanding. Increasing population growth in cities inevitably brings about the intensification of urban health problems. The multidimensional nature of factors associated with health together with the dynamic, interconnected environment of cities moderates the effects of policies and interventions that are designed to improve population health. With the emergence of the “Internet of Things” and the availability of “Big Data,” policymakers and practitioners are in need of a new set of analytical tools to comprehensively understand the social, behavioral, and environmental factors that shape population health in cities. Systems science, an interdisciplinary field that draws concepts, theories, and evidence from fields such as computer science, engineering, social planning, economics, psychology, and epidemiology, has shown promise in providing practical conceptual and analytical approaches that can be used to solve urban health problems. This chapter describes the level of complexity that characterizes urban health problems and provides an overview of systems science features and methods that have shown great promise to address urban health challenges. We provide two specific examples to showcase systems science thinking: one using a system dynamics model to prioritize interventions that involve multiple social determinants of health in Toronto, Canada, and the other using an agent-based model to evaluate the impact of different food policies on dietary behaviors in NewYork City. These examples suggest that systems science has the potential to foster collaboration among researchers, practitioners, and policymakers from different disciplines to evaluate interconnected data and address challenging urban health problems.