P25 Smooth Skygrid: Bayesian coalescent-based inference ofpopulation dynamics

Coalescent-based inference methods are essential in estimating population genetic parameters directly from gene sequence data under a variety of scenarios. In the last two decades, there have been several non-parametric expansions of the coalescent model for more flexible treatment towards demographic changes. The Bayesian Skygrid model is currently the most popular nonparametric coalescent model that discretizes continuous effective population size changes over an array of predefined time epochs. The effective population size in an epoch is constant and represented by a single parameter. Therefore, the change points of the effective population size parameters introduce discontinuities with respect to time and cause difficulties in the application of dynamic-integration-based samplers such as the Hamiltonian Monte Carlo method. In this poster, we introduce the original Skygrid coalescent prior, demonstrate the aforementioned discontinuities and introduce our preliminary thoughts on solving them with a new smoothed version of the Skygrid coalescent prior, and demonstrate the applicaDon on viruses such as West Nile Virus.

Digital Health (e.g., Big Data, ML, AI)

ASA Biopharmaceutical Section Regulatory-Industry Statistics Workshop 2024