ArticleSystematic biology2021
Unifying Phylogenetic Birth-Death Models in Epidemiology and Macroevolution.
Article in Systematic biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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28 citing papers in PubMed.
- PhyloCNN: Improving Tree Representation and Neural Network Architecture for Deep Learning from Trees in Phylodynamics and Diversification Studies.Systematic biology · 2026Article
- Assessing the inference of single-cell phylogenies and population dynamics from CRISPR lineage recordings.PLoS computational biology · 2026Article
- SAASI: Sampling Aware Ancestral State Inference.Nature communications · 2026Article
- Age-dependent Phylodynamics with Application to Single-cell Lineage Trees.Molecular biology and evolution · 2026Article
- Phylodynamics of Somatic Evolution: A Likelihood-Based Approach for Cellular Reproduction.Molecular biology and evolution · 2026Article
- Inferring Cell Differentiation Dynamics with Unobserved Progenitors.bioRxiv : the preprint server for biology · 2025Article
- Power and sample size calculations for testing the ratio of reproductive values in phylogenetic samples.American journal of epidemiology · 2025Article
- Exact and efficient phylodynamic simulation from arbitrarily large populations.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Accounting for contact tracing in epidemiological birth-death models.PLoS computational biology · 2025Article
- Reading Yule in light of the history and present of macroevolution.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Article
- The Fossilized Birth-Death Model Is Identifiable.Systematic biology · 2025Article
- Evolutionary and epidemic dynamics of COVID-19 in Germany exemplified by three Bayesian phylodynamic case studies.Bioinformatics and biology insights · 2025Article
- Article
- COVID-19 pandemic interventions reshaped the global dispersal of seasonal influenza viruses.Science (New York, N.Y.) · 2024Article
- Deep Learning and Likelihood Approaches for Viral Phylogeography Converge on the Same Answers Whether the Inference Model Is Right or Wrong.Systematic biology · 2024Article
- Scalable gradients enable Hamiltonian Monte Carlo sampling for phylodynamic inference under episodic birth-death-sampling models.PLoS computational biology · 2024Article
- phytools 2.0: an updated R ecosystem for phylogenetic comparative methods (and other things).PeerJ · 2024Article
- Fast and Accurate Maximum-Likelihood Estimation of Multi-Type Birth-Death Epidemiological Models from Phylogenetic Trees.Systematic biology · 2023Article
- Scalable gradients enable Hamiltonian Monte Carlo sampling for phylodynamic inference under episodic birth-death-sampling models.bioRxiv : the preprint server for biology · 2023Article
- The ClaDS rate-heterogeneous birth-death prior for full phylogenetic inference in BEAST2.Systematic biology · 2023Article
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5 authors.
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Abstract
Birth-death stochastic processes are the foundations of many phylogenetic models and are widely used to make inferences about epidemiological and macroevolutionary dynamics. There are a large number of birth-death model variants that have been developed; these impose different assumptions about the temporal dynamics of the parameters and about the sampling process. As each of these variants was individually derived, it has been difficult to understand the relationships between them as well as their precise biological and mathematical assumptions. Without a common mathematical foundation, deriving new models is nontrivial. Here, we unify these models into a single framework, prove that many previously developed epidemiological and macroevolutionary models are all special cases of a more general model, and illustrate the connections between these variants. This unification includes both models where the process is the same for all lineages and those in which it varies across types. We also outline a straightforward procedure for deriving likelihood functions for arbitrarily complex birth-death(-sampling) models that will hopefully allow researchers to explore a wider array of scenarios than was previously possible. By rederiving existing single-type birth-death sampling models, we clarify and synthesize the range of explicit and implicit assumptions made by these models. [Birth-death processes; epidemiology; macroevolution; phylogenetics; statistical inference.].
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