Inferring epidemiological parameters from phylogenies using regression-ABC: A comparative study.

ABSTRACT: Inferring epidemiological parameters such as the R0 from time-scaled phylogenies is a timely challenge. Most current approaches rely on likelihood functions, which raise specific issues that range from computing these functions to finding their maxima numerically. Here, we present a new regression-based Approximate Bayesian Computation (ABC) approach, which we base on a large variety of summary statistics intended to capture the information contained in the phylogeny and its corresponding lineage-through-time plot. The regression step involves the Least Absolute Shrinkage and Selection Operator (LASSO) method, which is a robust machine learning technique. It allows us to readily deal with the large number of summary statistics, while avoiding resorting to Markov Chain Monte Carlo (MCMC) te

SUBMITTER: Saulnier E 

PROVIDER: S-EPMC5358897 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature