Pathogen Effective Population Size Models for MRSA and Rabies Epidemics

2020 research by Erik M. Volz applies non-parametric autoregressive models to estimate pathogen effective population size growth rates from sequence data. The study demonstrates the method on real data from rabies virus and Staphylococcus aureus epidemics, finding a correlation between MRSA lineage growth rates and beta-lactam antibiotic prescription rates. The models are implemented in the open-source R package skygrowth.

Use Cases

• Estimate growth rates of pathogen effective population size from sequence genealogies using the skygrowth model.
• Relate estimated pathogen growth rates to time-varying variables like antibiotic prescription rates through regression analysis.
• Reconstruct trends of epidemic growth and decline for pathogens such as MRSA and rabies virus from sparse genealogical data.
• Compare non-parametric growth rate priors against conventional skyline estimators for phylodynamic inference.

Strengths

• Method validated on real-world epidemic data from two distinct pathogens: rabies virus and Staphylococcus aureus.
• Includes a regression approach for linking pathogen growth rates to external covariates like prescription data.
• Open-source implementation available in the R package skygrowth, facilitating reproducibility.

Limitations

• Dataset size, specific row count, and column structure are not provided in the input.
• The temporal and geographic coverage for the underlying pathogen sequence data is unspecified.
• Application is specialized to phylodynamic modeling, requiring domain expertise in population genetics.

Provenance

Source

Erik M. Volz via Dryad.

Collection Method

Non-parametric population genetic modeling applied to pathogen sequence data from published studies on rabies and S. aureus.

Freshness

Last updated in June 2020.