Nathaniel Osgood

Nathaniel Osgood - Professor, University of Saskatchewan
Office: 280.6 Thorvaldson
Phone: (306) 966 6102
fax: (306) 966 4884
Email: osgood 'at'


Professor, Department of Computer Science

Associate Faculty, Department of Community Health & Epidemiology

Associate Faculty, Bioengineering Division

Research Area -- Computational Epidemiology & Public Health Informatics: Cross-Leveraging Systems Science, Data Science and Computational Statistics for Public Health Insight

Curriculum Vitae

Visitors may also be interested in knowing about or registering for our 2018 bootcamps, running in July and August 2018. Please write to for additional information.

Research Interests

Some items of current focus

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.

Application areas: helping inform configuration and delivery of health care services in Saskatchewan (via SCPOR), Gestational and Type 2 Diabetes in the Australian Capital Territory and in Saskatchewan, Obesity in the ACT and in Los Angeles, Suicidal ideation, Copycat suicide and interaction of Suicidal Ideation and Domestic Violence and Addictions, TB, Measles, Pertussis, Chickenpox, Dementia, service dogs for those with PTSD, Foodborne Illness, Parkinson's Disease, diverse health and nutrition apps.

Methodological areas: novel computer languages and language engineering innovation in support of agent-based and hybrid modeling, supporting particle filtering and particle MCMC with System Dynamics and Agent-Based models, systems for visualizing state space reconstruction and for Convergent Cross Mapping (CCM), advancing Spark based machine learning and dynamic modeling toolsets, GPU-based computational statistics algorithms (PMCMC and Particle Filtering).

Tools: Smartphone and wearable based data collection (particularly via the Ethica data system, which emerged from our iEpi project), social media and search mining. To help make sense of such data and make it actionable at policy, health services and clinical levels, we use a variety of tools, including Agent-Based modeling and ODE (System Dynamics, particularly when leveraged by Particle Filtering) and Discrete Event modeling and hybrids, MCMC, and PMCMC, Machine Learning tools (especially Hidden Markov Models and Bayesian Networks), CCM and State-Space Reconstruction. Where they fill a key gap, we also develop apps for smartphone and web platforms. Apache Spark, Scala and R serve as Additional key tools in our toolbox.

iEpi/Ethica Health: Our Smartphone-Based Ubiquitous Epidemiological Sensing & Survey Platform

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.

Selected Recent Publications

Please see my publications page and Curriculum Vitae for lists of some recent publications.

Videos & Screencasts

Classes & Tutorials

The latest versions of my course on Agent-Based modeling and Hybrid dynamic modeling for health can be found at this course page. For interested students, I also teach a course at the University of Saskatchewan.

A systems science in health model inventory (linked to a repository of such models using System Dynamics, Agent-Based Modeling, Discrete Event Modeling, and Hybrids thereof) can be found here.

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, UMN, Deakin University, and several 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.

Talks and Selected Tutorials

Videos of some of my talks and selected tutorials can be found here.

Trained Students Available

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.

Student Supervision and Traineeship Opportunities

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.

Other links

Last updated November 14, 2011. Please report problems to