Nathaniel Osgood

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.