AI Article Synopsis
- Phylodynamic methods are important for studying how diseases spread geographically and demographically, but they depend heavily on prior beliefs about model parameters.
- The PrioriTree utility helps researchers understand and manage how sensitive their results are to these prior beliefs, specifically in discrete-geographic analyses.
- PrioriTree, available as an R package, includes features for creating input and output files for BEAST analyses, aiding in robust Bayesian inference and model fitting with empirical data.
Article Abstract
Summary: Phylodynamic methods are central to studies of the geographic and demographic history of disease outbreaks. Inference under discrete-geographic phylodynamic models-which involve many parameters that must be inferred from minimal information-is inherently sensitive to our prior beliefs about the model parameters. We present an interactive utility, PrioriTree, to help researchers identify and accommodate prior sensitivity in discrete-geographic inferences. Specifically, PrioriTree provides a suite of functions to generate input files for-and summarize output from-BEAST analyses for performing robust Bayesian inference, data-cloning analyses and assessing the relative and absolute fit of candidate discrete-geographic (prior) models to empirical datasets.
Availability And Implementation: PrioriTree is distributed as an R package available at https://github.com/jsigao/prioritree, with a comprehensive user manual provided at https://bookdown.org/jsigao/prioritree_manual/.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9841403 | PMC |
| http://dx.doi.org/10.1093/bioinformatics/btac849 | DOI Listing |
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