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Phylodynamic Model Adequacy Using Posterior Predictive Simulations.

Sebastian Duchene¹, Remco Bouckaert^2,3, David A Duchene⁴

¹Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Melbourne, Australia.

Systematic Biology

|June 27, 2018

Summary

This summary is machine-generated.

Phylodynamic models help infer pathogen spread from genetic data. TreeModelAdequacy, a new BEAST2 package, assesses model fit for viral outbreaks like Ebola and H1N1 influenza.

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Area of Science:

Evolutionary biology
Epidemiology
Computational biology

Background:

Pathogen evolution and epidemiology occur on similar timescales, enabling phylogenetic inference of infectious spread.
Phylodynamic models are crucial for analyzing pathogen genetic data but require careful model selection.
Model adequacy testing is essential to ensure that chosen models accurately represent key data features.

Purpose of the Study:

To introduce TreeModelAdequacy, a novel package for BEAST2 software.
To provide a method for assessing the adequacy of phylodynamic models.
To evaluate model performance using real-world viral outbreak data.

Main Methods:

Development of the TreeModelAdequacy package for BEAST2.
Application of model adequacy testing to phylogenetic trees from viral outbreaks.
Comparison of different phylodynamic models, including coalescent exponential-growth and birth-death SIR models.

Main Results:

The coalescent exponential-growth model adequately described key features of Ebola virus outbreak data.
The birth-death susceptible-infected-recovered model best explained the H1N1 influenza data.
TreeModelAdequacy successfully differentiated between adequate and inadequate models for specific datasets.

Conclusions:

Phylodynamic model choice significantly impacts inferences about pathogen spread.
TreeModelAdequacy provides a robust framework for validating phylodynamic models.
Accurate model selection is critical for reliable epidemiological insights from viral evolution data.