The UCLA Department of Community Health Sciences and the Center for Social Statistics presents:
Dynamic Modeling for Health in the Age of Big Data
Traditional approaches to public health concerns have conferred great advances in the duration and quality of life. Public health interventions – from improved sanitation efforts, to vaccination campaigns, to contact tracing and environmental regulations – have helped reduce common risks to health throughout many areas of the world. Unfortunately, while traditional methods from the health sciences have proven admirably suited for addressing traditional challenges, a troubling crop of complex health challenges confront the nation and the world, and threaten to stop – and even reverse the – rise in length and quality of life that many have taken for granted. Examples include multi-factorial problems such as obesity and obesity-related chronic disease, the spread of drug-resistant and rapidly mutating pathogens that evade control efforts, and “syndemics” of mutually reinforcing health conditions (such as Diabetes and TB; substance abuse, violence and HIV/AIDS; obesity & stress). Such challenges have proven troublingly policy resistant, with interventions being thwarted by “blowback” from the complex feedbacks involved, and attendant costs threaten to overwhelm health care systems. In the face of such challenges public health decision makers are increasingly supplementing their toolbox using “system science” techniques. Such methods – also widely known as “complex systems approaches” – provide a way to understand a system’s behavior as a whole and as more than the sum of its parts, and a means of anticipating and managing the behavior of a system in more judicious and proactive fashion. However, such approaches offer substantially greater insight and power when combined with rich data sources. Within this talk, we will highlight the great promise afforded by combining of Systems Science techniques and rich data sources, particularly emphasizing the role of cross-linking models with “big data” offering high volume, velocity, variety and veracity. Examples of such data include fine-grained temporal and spatial information collected by smartphone-based and wearable as well as building and municipal sensors, data from social media posts and search behavior, helpline calls, website accesses and rich cross-linked databases. Decision-oriented models grounded by such novel data sources can allow for articulated theory building regarding difficult-to-observe aspects of human behavior. Such models can also aid in informing evaluation of and judicious selection between sophisticated interventions to lessen the health burden of a wide variety of health conditions. Such models are particularly powerful when complemented by machine learning and computational statistics techniques that permit recurrent model regrounding in the newest evidence, and which allow a model to knit together holistic portrait of the system as a whole, and which support grounded investigation of between intervention strategies tradeoffs.
Sponsored by The Department of Community Health Sciences along with the Center for Social Statistics and the California Center for Population Research
Nathaniel Osgood, Professor, Department of Computer Science, Associate Faculty at Department of Community Health & Epidemiology and Bioengineering Division at the University of Saskatchewan