Inferring epidemiological dynamics with Bayesian coalescent inference: the merits of deterministic and stochastic models.

ABSTRACT: Estimation of epidemiological and population parameters from molecular sequence data has become central to the understanding of infectious disease dynamics. Various models have been proposed to infer details of the dynamics that describe epidemic progression. These include inference approaches derived from Kingman's coalescent theory. Here, we use recently described coalescent theory for epidemic dynamics to develop stochastic and deterministic coalescent susceptible-infected-removed (SIR) tree priors. We implement these in a Bayesian phylogenetic inference framework to permit joint estimation of SIR epidemic parameters and the sample genealogy. We assess the performance of the two coalescent models and also juxtapose results obtained with a recently published birth-death-sampling model fo

SUBMITTER: Popinga A 

PROVIDER: S-EPMC4317665 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature