Matteo Convertino, PhD

 
 

Assistant Professor

School of Public Health, Division of Environmental Health Sciences

& Public Health Informatics Program - Academic Health Center

Institute on the Environment

Institute for Engineering in Medicine

Institute for Advanced Study

University of Minnesota Twin-Cities


Mission

The mission of Complex System Science Approaches in Population Health within the HumNat Lab -- Laboratory for the Analysis, Modeling and Management of Complex Biological and Socio-technical Systems (``Human-Natural Systems’’) -- is to promote transdisciplinary research and foster community around systems science approaches for global public health (with particular focus on One Health) among graduate students and faculty at the University of Minnesota, and stakeholders, and private sector/government/universities nationally and internationally. System pathology and design from the cell to the population scale are two pillars of the lab via computational models.

HumNat aims to accomplish this by advocating systems thinking, raising awareness of available resources, promoting application of those resources, and providing support for students and stakeholders interested in using quantitative analysis and dynamical system simulation approaches in their research and application. Complex system risk, resilience, uncertainty, optimal decision-making and sustainability are core concepts of investigation in the lab. Studies in HumNat aim to analyze big data, develop multiscale theoretical and computational models for understanding and managing coupled human-natural systems, apply models for solving real-world issues, and communicating research findings to stakeholders via scientific publications and artistic forms (aesthetic computing, audio-visuals, dance).

   Current focus areas are related to computational ‘‘one health’’, that is the development of mathematical models and derived cyber-infrastructure for real time use in order to solve population health issues. In particular we are interested in water-dependent diseases (e.g. cholera and leptospirosis) in large-scale ecosystems, zoonosis, foodborne diseases and food system design, occupational health infrastructure design, and pharmacodynamics. Models used and developed by HumNat are multiscale, multiobjective, and modular, such as metacommunity models, network models, global sensitivity and uncertainty analyses models, portfolio decision models, and other models or a combination of those according to needs and objectives of the question at hand. These models are mostly based on information theory and are physically based when possible. Artificial intelligence algorithms have been developed for these models in order to make them applicable in real time. We are also developing a new System Instantaneous Unit Response theory as a new system theory based on network theory.

HumNat is constantly engaged into the development of new models and theories, and use and integration of available theories, models, and data for exciting discoveries and applications in relation to other fields. The development of predictive models allows stakeholders to explore systems as virtual systems in all their potential configurations for the selection of the configuration with the lowest systemic risk.


Broad Vision

Complex systems science can serve as a crucial tool in public health. The goal of complex systems approaches in population health 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. Complexity science in health 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.

























``The problem is not necessarily coming up with better health solutions but making sure they are deployed to the right place, at the right time, and at the right scale. Comparable to other solutions they need to be sustainable by considering social, economical, and environmental factors bounded by any constraint in place. It's an engineering problem, it's a physics problem; that's why computational science and complexity theory is needed. Transdisciplinary education and research are the foundation of the effort of real-life problem solving that is particularly worrisome in public health.’’






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HumNat Lab (Laboratory for the Analysis, Modeling, and Management

of Complex Human-Natural Systems)

 

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