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. Several lines of work I am presently pursuing reflect the heavy performance burden of simulating large-scale populations at the individual level, and the high complexity of understanding the behaviour of such models.

• Development of scale-modelling techniques for speeding the simulation and analysis of individual-based models. This work involves the first application of principles of dimensional analysis to the design of simulation models. The approach has attracted considerable industry interest.
• Development of formal methods for analyzing the behaviour of individual-based models. Much of this work is aimed both at facilitating understanding of individual-based models and for assisting with model order reduction -- trying to formulate simpler models that capture the same essential dynamics, but are computationally frugal and easier to manipulate, evolve and understand.
• Large-scale smartphone/mobile wireless sensor network-based data collection for informing the parameterization and calibration of individual-based simulation models.Individual-based simulation models, seek to represent individual behaviour and interaction on which there is often limited data, and where self-report is unreliable. Wireless sensor mechaisms can offer benefits not only to researchers (making possible collection of far larger amounts of information for much lower resource investment), but also to policymakers for real-time epidemiological "signal detection".
• Creating tools to better manage and facilitate the dynamic modeling process.We anticipate releasing in late 2009 an open-source tool (SILVER) that seeks to reduce the bookkeeping and version-control overhead associated with models, and to lower the barriers to effective and transparent collaboration. This project is currently hosted on Google Code and should be released for beta-testing in November 2009. If you would like to serve as a beta-tester or join the development effort, please write me.
• Exploring the characteristics of and tradeoffs between aggregate and individual-based models. This work helps modelling practitioners to better understand circumstances in which each type of modelling is desirable or required.
• Application of novel data collection techniques to inform dynamic understanding of health hehaviours, status and systems.We are particularly interested in the application of longitudinal administrative data and ubiquitous, ambulatory sensors that provide fine-grained temporal data across a wide range of cross-linked sensor streams.
• Self correcting modelsLeveraging aspects of control theory and machine learning to create models whose estimates are regularly re-grounded in empirical data.
• Robust models for dynamic decision problemsBuilding on aspects of decision analysis to design models that help identify adaptive policies that are robust under a wide range of future eventualities.
• Development and software implementation of mathematical tools for understanding the dynamic behaviors of simulation models.Tools to support as eigenvalue elasticity analysis, extended Kalman filtering, and analysis of longitudinal trajectories for Differential Equation-based systems can offer significant research insight.
• Creation of software and analytic approaches for visualization and recognition of latent patterns in larger public health data sets.