% pubman genre = article
@article{item_3258838,
title = {{Phylodynamic model adequacy using posterior predictive simulations}},
author = {Duchene, Sebastian and Bouckaert, Remco and Duchene, David A and Stadler, Tanja and Drummond, Alexei J},
language = {eng},
issn = {1063-5157},
doi = {10.1093/sysbio/syy048},
publisher = {Society of Systematic Biologists},
address = {Austin, Tex.},
year = {2019},
date = {2019},
abstract = {{Rapidly evolving pathogens, such as viruses and bacteria, accumulate genetic change at a similar timescaleover which their epidemiological processes occur, such that, it is possible to make inferences about their infectious spreadusing phylogenetic time-trees. For this purpose it is necessary to choose a phylodynamic model. However, the resultinginferences are contingent on whether the model adequately describes key features of the data. Model adequacy methodsallow formal rejection of a model if it cannot generate the main features of the data. We present TreeModelAdequacy, apackage for the popular BEAST2 software that allows assessing the adequacy of phylodynamic models. We illustrate itsutility by analyzing phylogenetic trees from two viral outbreaks of Ebola and H1N1 influenza. The main features of theEbola data were adequately described by the coalescent exponential-growth model, whereas the H1N1 influenza data werebest described by the birth{\textendash}death susceptible-infected-recovered model. [Bayesian phylogenetics; BEAST2; model adequacy;phylodynamics; posterior predictive simulation; viral evolution.]}},
journal = {{Systematic Biology}},
volume = {68},
number = {2},
pages = {358--364},
eid = {syy048},
}