Nathaniel Osgood - Associate Professor, University of Saskatchewan
Office: 280.6 Thorvaldson
Phone: (306) 966 6102
fax: (306) 966 4884
Email: osgood 'at' cs.usask.ca
BS EECS (MIT), MS EECS (MIT), PhD CS (MIT)
My research is focused on providing cross-linked simulation, ubiquitous sensing, and inference tools to inform understanding of population health trends and health policy tradeoffs. Such tools can, for example, aid public health decision makers in putting into place cost-effective preventive policies, design more effective screening or treatment strategies for an illness, help provide insight into the causes underlying changes in the number of cases of a disease reported, and react more quickly to an outbreak of infectious disease when it occurs.
This work includes both application and methodological components.
On the application side, our research involves collaborating with cross-disciplinary teams to create tools to inform the design of health interventions that are high leverage, robust, and cost-effective. Such application work is almost almost always pursued in close collaboration with broader teams, frequently including those with close clinical familiarity of the diseases and/or pathogens involved (particularly doctors and nurses), epidemiologists, biostatisticians, public health nurses, and researchers or others involved in data collection and surveillance.
Please see our applications page for more information on this work.
Our methodological work reflects the fact that practitioners of dynamic modelling for public health currently face many hurdles and challenges in building, validating, executing, modifying and understanding their models.
Please see our methodological work page for additional information.
Materials from our recent bootcamp on conducting mHealth studies with iEpi (Ethica Health). Click here to see my youtube playlist with mHealth videos. Materials (slides, etc.) are also available. On the health big data analysis/data science class side, please for my graduate class on use of Scala and Spark for Health Data Science (or "Health Big Data") videos and exercises, slides, example data, and code fragments. For information on iEpi/Ethica Health epidemiological monitoring system, please see the Ethica Data Services website and my iEpi page. It bears emphasis that a number of the publications on that page demonstrate how sensed data can be used in conjuntion with agent-based modeling.
A (youtube streaming) screencast of my presentation from the Institute for Systems Science and Health 2011 demonstrates how we can leverage such data using 3 systems science modeling techniques: Agent-Based Modeling, classic compartmental System Dynamics modeling, and Social Network Analysis.
Presentation delivered at the 2012 Annual Meeting for the Society for Epidemiological Research focuses on how sensing can inform the design of rich simulation models, but also comments on the synergy between sensing and dynamic models.
Please see my publications page and Curriculum Vitae for lists of some recent publications.
The latest versions of my course on Agent-Based modeling and Hybrid dynamic modeling for health can be found at this course page (which features videos interlinked with exercises, slides, and AnyLogic 7 example models).
Notice: 2017 Bootcamp and Incubator: Information and registration for our Agent-Based Modeling Bootcamp and Incubator for Health Researchers 2017 (running August 21-26, 2017 will be coming soon. The bootcamp will include coverage of a wide variety of topics in agent-based modeling and extensive partnerships for helping the participants in strategic planning and hands-on assistance in building Agent-based models customized to their research interests.
Custom sponsored versions of agent-based modeling bootcamp: Please note that I deliver customized versions of this bootcamp worldwide. Past and forthcoming such bootcamps have been held at MIT (as a semester course), UCLA, Flinders University, NCSU/UNC, Duke-NUS, at the Sax Institute. If you are interested in the possibility of sponsoring a custom bootcamp at your institution, please send me an email, and I would be pleased to explore the idea with you.
Videos for my graduate course on use of Scala and Spark for Health Data Science (or "Health Big Data") can be found at this youtube playlist page. The graduate class materials (including code fragments, slides, exercises, and associated data) can be found this zipped archive. I hope that this material will be of use for those seeking to learn more about the rapidly growing applications of data science in the health sciences, particularly data science in epidemiology (digital epidemiology/big data in epidemiology). If you do end up using these materials for teaching of your own, I would be grateful if you could provide a pointer to the source, and send me an email to notify me of their use.
I teach graduate courses that provide an introduction to dynamic (simulation) modeling for public health. These courses provide a basic hands-on introduction to the theory and practice of Agent-Based and System Dynamics modeling in the context of health issues, including for Agent-Based modeling and System Dynamics modeling. Screencasts and presentations from the lectures & tutorials of this class are available below. I hope that this material will be of value for those seeking to learn more about simulation modeling.
My current undergraduate courses are CMPT 394 (Simulation principles) and CMPT 371.
I have also taught a variety of smaller, discrete tutorial-style sessions related to modeling for Public Health. These sessions are taught using Vensim and AnyLogic software.
I further teach undergraduate courses in Software Project Management (CMPT 371) and co-located undergraduate & graduate courses on Advanced Software Engineering (CMPT 470 & CMPT 816).
I am glad to share the materials for these courses with interested parties.
Videos of some of my talks and selected tutorials can be found here.
I currently supervise a broad set of students trained in Agent-Based Modeling, System Dynamics Modeling, and in the use of ubiquitous portable, wireless sensor systems for health insight and decision-making. These students operate at the undergraduate, M.Sc. and Ph.D. levels, and offer a wide range of skill levels in the health sciences, Computer Science, Information Technology and mathematics. Many such students value internship opportunities and knowing about post-graduation career opportunities. Some students are also interested in consulting options. Please be encouraged to write if you have opportunities available, as my students may be interested in learning about them.
Please see my page on student supervision.
I am anticipating taking on a new M.Sc. or Ph.D. student in September, 2014. Because a student holding a pre-existing scholarships is not subject to important constraints limiting eligibility for working in our Computational Epidemiology and Public Health Informatics Laboratory, students who will hold Scholarships such as -- but not limited to -- the CSC Scholarship (via the China Scholarship Council) and UofS-VIED DSF (Doctoral Scholar Fellowship) will receive priority review.
We are grateful to receive financial support for our research from a variety of funders. We would like to express here our gratitude for their support.
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