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Looking for the limits to particle-filter based inference

Banff International Research Station for Mathematical Innovation and Discovery

Emboldened by a string of insights gleaned from time series using likelihood-based inference and stochastic dynamical systems models, we undertook to exploit age-specific disease incidence data using an age-structured stochastic transmission model. In this talk, I explain the study's motivation, in questions surrounding the current resurgence of pertussis in countries with high vaccine coverage, and describe the model we formulated to address these questions. I point out the interesting features of the model implementation and the critical aspects of the inference methodology, with special attention to the challenges associated with this high-dimensional context. I highlight the surprises among the scientific conclusions we drew and conclude by speculating on the unreasonable effectiveness of stochastic models in population biology.

Mathematics; Statistics; Partial differential equations

video/mp4

Author affiliation: University of Michigan

2018-03-29

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/65013

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/