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Activity Number: 414
Type: Contributed
Date/Time: Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #312633
Title: Model Misspecification and Improved Algorithms in Cases of Preferential Sampling in Population Dynamics
Author(s): Michael Karcher*+ and Julia A. Palacios and Trevor Bedford and Vladimir Minin
Companies: University of Washington and Brown University and Fred Hutchinson Cancer Research Center and University of Washington
Keywords: preferential sampling ; gaussian process ; phylodynamics ; misspecification ; INLA ; Bayesian
Abstract:

The field of phylodynamics aims at estimating population size fluctuations from molecular sequences (e.g. DNA) sampled from the population of interest. One way to accomplish this task is to first estimate a genealogy relating the sampled sequences and then to use a coalescent model to estimate fluctuations of the population size from the genealogy. Current methods do not account for preferential sampling---whereby sampling of molecular sequences is probabilistically dependent on the population size trajectory. This omission introduces model misspecification error, potentially resulting in bias when estimating population size. We propose a new model that explicitly takes preferential sampling into account. We then perform a simulation study to illustrate the nature of the model misspecification errors, and we compare the results to output from our proposed model. Finally, we compare performance of the currently used model and our modification using molecular sequences from seasonal human influenza.


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