Equation-free techniques for infectious disease data

Document Type

Presentation Abstract

Presentation Date

11-21-2016

Abstract

Equation-free techniques are emerging as a promising analysis and modeling tool for the investigation of complex, dynamical systems. The surge in popularity of these methods stems from the ability to discover models directly from measurement data, making them well suited to describe systems for which the governing equations are either partially known or heuristically posited. In the life sciences, complex systems without a standard set of governing equations include neuroscience, infectious disease spread, metabolic/regulatory networks, and ecological networks. As a motivating example, equation-free methods can be applied to data collected by public health surveillance systems focused around the eradication of infectious diseases. The increased awareness for gathering high-quality data and the advent of new monitoring tools is beginning to generate large sets of data describing the spread of infectious disease. I will focus on how recently developed equation-free methods, such as Koopman operator theory, Dynamic Mode Decomposition (DMD), and Sparse Identification of Nonlinear Dynamics (SINDy), can help both characterize and analyze time-series data collected from complex systems. These techniques offer insight into high-dimensional datasets and complex systems that are nonlinear, parameterized, and multi-scale. This presentation will also include a discussion on how these methods can be used for prediction and nonlinear forecasting. Importantly, these approaches are helping modify infectious disease risk calculations at a sub-national level for countries in Africa helping guide large-scale intervention strategies to help prevent childhood disease.

Additional Details

Monday, November 21, 2016 at 3:00 p.m. in Math 103
Refreshments at 4:00 p.m. in Math Lounge 109

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