Apr 26, 2016

Assessing the accuracy of Approximate Bayesian Computation approaches to infer epidemiological parameters from phylogenies

BioRxiv : the Preprint Server for Biology
Emma SaulnierOlivier Gascuel

Abstract

Phylodynamics typically rely on likelihood-based methods to infer epidemiological parameters from dated phylogenies. These methods are essentially based on simple epidemiological models because of the difficulty in expressing the likelihood function analytically. Computing this function numerically raises additional challenges, especially for large phylogenies. Here, we use Approximate Bayesian Computation (ABC) to circumvent these problems. ABC is a likelihood-free method of parameter inference, based on simulation and comparison between target data and simulated data, using summary statistics. We simulated target trees under several epidemiological scenarios in order to assess the accuracy of ABC methods for inferring epidemiological parameter such as the basic reproduction number ( R ), the mean duration of infection, and the effective host population size. We designed many summary statistics to capture the information in a phylogeny and its corresponding lineage-through-time plot. We then used the simplest ABC method, called rejection, and its modern derivative complemented with adjustment of the posterior distribution by regression. The availability of machine learning techniques including variable selection, motivated us ...Continue Reading

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Mentioned in this Paper

Lenalidomide
Size
Hemorrhagic Fever, Ebola
Trees (plant)
Likelihood Functions
Bone Cysts, Aneurysmal
Maximum Likelihood Estimation
Dorsal
Simulation
Epidemiology

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