title: How well can the exponential-growth coalescent approximate constant-rate birth-death population dynamics?
abstract: One of the central objectives in the field of phylodynamics is the quantification of population dynamic processes using genetic sequence data or in some cases phenotypic data. Phylodynamics has been successfully applied to many different processes, such as the spread of infectious diseases, within-host evolution of a pathogen, macroevolution and even language evolution. Phylodynamic analysis requires a probability distribution on phylogenetic trees spanned by the genetic data. Because such a probability distribution is not available for many common stochastic population dynamic processes, coalescent-based approximations assuming deterministic population size changes are widely employed. Key to many population dynamic models, in particular epidemiological models, is a period of exponential population growth during the initial phase. Here, we show that the coalescent does not well approximate stochastic exponential population growth, which is typically modelled by a birth-death process. We demonstrate that introducing demographic stochasticity into the population size function of the coalescent improves the approximation for values of R0 close to 1, but substantial differences remain for large R0. In addition, the computational advantage of using an approximation over exact models vanishes when introducing such demographic stochasticity. These results highlight that we need to increase efforts to develop phylodynamic tools that correctly account for the stochasticity of population dynamic models for inference.
authors: Department of Biosystems Science and Engineering, ETH Zürich, Zürich, Switzerland Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland tanja.stadler@bsse.ethz.ch.
authors: Tanja
authors: T
authors: Stadler
authors: Department of Computer Science, The University of Auckland, Auckland, New Zealand Allan Wilson Centre for Molecular Ecology and Evolution, Palmerston North, New Zealand Institute of Veterinary Animal and Biomedical Sciences, Massey University, Palmerston North, New Zealand.
authors: Timothy G
authors: TG
authors: Vaughan
authors: Department of Computer Science, The University of Auckland, Auckland, New Zealand.
authors: Alex
authors: A
authors: Gavryushkin
authors: Department of Statistics, The University of Auckland, Auckland, New Zealand LIRMM, UMR 5506, Montepellier, France.
authors: Stephane
authors: S
authors: Guindon
authors: Department of Biosystems Science and Engineering, ETH Zürich, Zürich, Switzerland Institute of Integrative Biology, ETH Zürich, Zürich, Switzerland Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.
authors: Denise
authors: D
authors: Kühnert
authors: Institute of Integrative Biology, ETH Zürich, Zürich, Switzerland.
authors: Gabriel E
authors: http://orcid.org/0000-0002-4463-166X
authors: GE
authors: Leventhal
authors: Department of Computer Science, The University of Auckland, Auckland, New Zealand Allan Wilson Centre for Molecular Ecology and Evolution, Palmerston North, New Zealand alexei@cs.auckland.ac.nz.
authors: Alexei J
authors: AJ
authors: Drummond
mesh_terms: D001723:Birth Rate; D008954:Models, Biological; D009026:Mortality; D011157:Population Dynamics; D011158:Population Growth; D013269:Stochastic Processes
keywords: birth–death model; epidemiology; phylodynamics; phylogenetics; population genetics