HumNat Lab – PI Matteo Convertino, University of Minnesota Twin-Cities

Systems science can serve as a crucial tool in public health. The goal with CSAPH is to arm researchers with the tools to better identify intervention points in a given cause, design better public policy laws, pinpoint intervention strategies that are no longer working, and have the ability to create more effective ones. Very often, in engineering problems one has to understand and manage the many different factors that can impact a particular outcome; there is always a complex network of different factors that affect a particular outcome, and one has to model how these factors interact, and then design strategies that control these interactions optimally. Public health and medical researchers are at a point where they have a very good understanding of many of the factors affecting health —from environmental causes to social issues to age — but now they are finding that the interaction of these factors is often more important in determining health outcomes. Consequently, it is important to understand how these different factors interact with each other. It’s not just one factor that is going to matter; the interactions between factors are going to matter much more.

Health System Computational Complexity can be a cross-disciplinary research field that takes a unique system dynamics view of health, examining all aspects from the molecular level all the way up to healthcare and governance itself. This Computational Complexity approach is aligned with world-wide initiatives to model together or separately cells, individuals, health systems in which they function, and local/global populations as a function of natural and man-induced external stressors. Health System Complexity is a way to bring together experts from different disciplines (engineers, doctors, veterinarians, biologists, artists, etc.) to solve basic and applied research questions and developing technological tools for these questions.

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