Bootcamp on Systems-Data Science - Practical Combining Data Science and Systems Science for Health

The Bootcamp on Systems-Data Science presents industrial-strength methods combining the power of machine learning, big data and dynamic modeling to improve health decision making. In addition to more than a dozen previous health uses, these methods have been at the heart of regular reporting by the instructor to inform health system decision-making in SK (daily), for each Canadian province (via PHAC), and for First Nations reserves across 9 Canadian provinces (via FNIHB).

This bootcamp characterizes these techniques, how to use them effectively, provides overviews of how they work, describes their use with other approaches, case studies & example implementations.  The event offers a particular focus on how these methods can be used with “big data” offering high volume, velocity, variety, and veracity, with examples showing the particular ways that they can regularly reground models with data from wastewater sampling, social media, smartphones/wearables, and search data in addition to traditional epidemiological data.

In greater detail, systems science and data science are two rapidly developing areas of computational science that have been demonstrated to offer tremendous capacity for informing health understanding, and which are applied by a growing number of projects in health and health care.  While each of these approaches taps the power of computational models, they have traditionally largely been pursued in isolation from each other.  Such fragmentation is particularly unfortunate, because the techniques are not merely highly compatible -- for example, in each using computational or informatics mechanisms to provide temporally and locationally fine grained longitudinal understanding across multiple generative pathways -- but synergistic, with each tradition opening strong opportunities for empowering the other, and with the combination of both yielding opportunities for insight and improved decision making far beyond the sum of what each can bring in isolation.  We present here a proven set of Systems-Data Science methods that achieve this “whole greater than the sum of its parts” and that allow each approach to better fulfill its full potential.