ArticleMolecular biology and evolution2020
Online Bayesian Phylodynamic Inference in BEAST with Application to Epidemic Reconstruction.
Article in Molecular biology and evolution, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed.
- Parallel algorithms for phylogenetic inference under a structured coalescent approximation.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Biological causes and impacts of rugged tree landscapes in phylodynamic inference.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Confronting the known unknown: historical lessons and future strategies for Disease X.Frontiers in public health · 2026Review
- Phylogenetic and evolutionary analysis of VP1 coding sequences of foot-and-mouth disease virus serotypes A, O, and SAT2 in Egypt.Virology journal · 2025Article
- Parallel algorithms for phylogenetic inference under a structured coalescent approximation.bioRxiv : the preprint server for biology · 2025Article
- Infinite Mixture Models for Improved Modeling of Across-Site Evolutionary Variation.Molecular biology and evolution · 2025Article
- Evolutionary trajectory and spread of respiratory syncytial virus group A in neonatal cohorts in Pakistan amidst the COVID-19 pandemic.Scientific reports · 2025Article
- Biological causes and impacts of rugged tree landscapes in phylodynamic inference.bioRxiv : the preprint server for biology · 2025Article
- Phylogenetic Tree Instability After Taxon Addition: Empirical Frequency, Predictability, and Consequences For Online Inference.Systematic biology · 2025Article
- Molecular Evolutionary Analyses ofMicroorganisms · 2024Article
- Ranked Subtree Prune and Regraft.Bulletin of mathematical biology · 2024Article
- Online Phylogenetics with matOptimize Produces Equivalent Trees and is Dramatically More Efficient for Large SARS-CoV-2 Phylogenies than de novo and Maximum-Likelihood Implementations.Systematic biology · 2023Article
- Online tree expansion could help solve the problem of scalability in Bayesian phylogenetics.Systematic biology · 2023Article
- The Principles of SARS-CoV-2 Intervariant Competition Are Exemplified in the Pre-Omicron Era of the Colombian Epidemic.Microbiology spectrum · 2023Article
- Genofunc: genome annotation and identification of genome features for automated pipelining analysis of virus whole genome sequences.BMC bioinformatics · 2023Article
- Scalable Bayesian phylogenetics.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022Review
- matOptimize: a parallel tree optimization method enables online phylogenetics for SARS-CoV-2.Bioinformatics (Oxford, England) · 2022Article
- Online Phylogenetics using Parsimony Produces Slightly Better Trees and is Dramatically More Efficient for Large SARS-CoV-2 Phylogenies thanbioRxiv : the preprint server for biology · 2022Article
- Real-Time and Remote MCMC Trace Inspection with Beastiary.Molecular biology and evolution · 2022Article
- Statistical Challenges in Tracking the Evolution of SARS-CoV-2.Statistical science : a review journal of the Institute of Mathematical Statistics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Reconstructing pathogen dynamics from genetic data as they become available during an outbreak or epidemic represents an important statistical scenario in which observations arrive sequentially in time and one is interested in performing inference in an "online" fashion. Widely used Bayesian phylogenetic inference packages are not set up for this purpose, generally requiring one to recompute trees and evolutionary model parameters de novo when new data arrive. To accommodate increasing data flow in a Bayesian phylogenetic framework, we introduce a methodology to efficiently update the posterior distribution with newly available genetic data. Our procedure is implemented in the BEAST 1.10 software package, and relies on a distance-based measure to insert new taxa into the current estimate of the phylogeny and imputes plausible values for new model parameters to accommodate growing dimensionality. This augmentation creates informed starting values and re-uses optimally tuned transition kernels for posterior exploration of growing data sets, reducing the time necessary to converge to target posterior distributions. We apply our framework to data from the recent West African Ebola virus epidemic and demonstrate a considerable reduction in time required to obtain posterior estimates at different time points of the outbreak. Beyond epidemic monitoring, this framework easily finds other applications within the phylogenetics community, where changes in the data-in terms of alignment changes, sequence addition or removal-present common scenarios that can benefit from online inference.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.