ArticleVirus evolution2018
TreeTime: Maximum-likelihood phylodynamic analysis.
Article in Virus evolution, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 855 papers, 1 of them a synthesis that pooled it.
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855 citing papers in PubMed, 1 synthesis or guideline pooled it, 1,349 citations in OpenAlex.
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- Emergence and spread of NA-I223V and NA-S247N double-mutant A(H1N1)pdm09 influenza viruses with reduced oseltamivir susceptibility in the Netherlands and beyond, 2023 to 2026.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2026Article
- Phylodynamic reconstruction of global evolution and geographic structure of maize chlorotic mottle virus.The Journal of general virology · 2026Article
- Genomic epidemiology of coxsackievirus A24 variant during the 2024 acute hemorrhagic conjunctivitis outbreak in Coastal Kenya.PLoS pathogens · 2026Article
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795 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mutations that accumulate in the genome of cells or viruses can be used to infer their evolutionary history. In the case of rapidly evolving organisms, genomes can reveal their detailed spatiotemporal spread. Such phylodynamic analyses are particularly useful to understand the epidemiology of rapidly evolving viral pathogens. As the number of genome sequences available for different pathogens has increased dramatically over the last years, phylodynamic analysis with traditional methods becomes challenging as these methods scale poorly with growing datasets. Here, we present TreeTime, a Python-based framework for phylodynamic analysis using an approximate Maximum Likelihood approach. TreeTime can estimate ancestral states, infer evolution models, reroot trees to maximize temporal signals, estimate molecular clock phylogenies and population size histories. The runtime of TreeTime scales linearly with dataset size.
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