O4-2.4 Complexity, epidemiology and the understanding of “what if”

Einstein is reputed to have said that “everything should be as simple as possible, but not simpler.” These words ring ever more true to epidemiologists as we struggle to gain knowledge about complex disease processes that involve dynamic interactions between biological, behavioural, social, spatial, socioeconomic, and global determinants. Understanding the etiologic and policy implications of such a rich stew of factors challenges the “independent effects” model of understanding population health. Fortunately, there are recent developments in which bridges are being built between computer science approaches designed to model and simulate complex systems and epidemiologic researchers. We will describe the development of an agent-based in silico complex systems model that is focused on integrating dynamic, non-linear, and multilayered economic, behavioural, social, biologival, and neighbourhood determinants of health and discuss how it can be used to inform our answers to questions such as—“if poor children went to good schools, how much would it eliminate socioeconomic inequalities in hearts disease when they become adults?”

Data science · Disease · Environmental health · Inequality · Management science · Neighbourhood (mathematics) · Population · Population health · Public health · Social determinants of health · Social epidemiology · Socioeconomic status · Computer Science · Health disparities and outcomes · Medicine