Imperial College London
Alternate

DrErikVolz

Faculty of Medicine, School of Public Health

Reader in Population Biology of Infectious Diseases
 
 
 
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Contact

 

+44 (0)20 7594 1933e.volz Website

 
 
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Location

 

UG10Norfolk PlaceSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@unpublished{Volz:2017:10.1101/210054,
author = {Volz, E and Didelot, X},
doi = {10.1101/210054},
publisher = {bioRxiv},
title = {Modeling the growth and decline of pathogen effective population size provides insight into epidemic dynamics and drivers of antimicrobial resistance},
url = {http://dx.doi.org/10.1101/210054},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - UNPB
AB  - Non-parametric population genetic modeling provides a simple and flexible approach for studying demographic history and epidemic dynamics using pathogen sequence data. Existing Bayesian approaches are premised on stationary stochastic processes which may provide an unrealistic prior for epidemic histories which feature extended period of exponential growth or decline. We show that non-parametric models defined in terms of the growth rate of the effective population size can provide a more realistic prior for epidemic history. We propose a non-parametric autoregressive model on the growth rate as a prior for effective population size, which corresponds to the dynamics expected under many epidemic situations. We demonstrate the use of this model within a Bayesian phylodynamic inference framework. Our method correctly reconstructs trends of epidemic growth and decline from pathogen genealogies even when genealogical data is sparse and conventional skyline estimators erroneously predict stable population size. We also propose a regression approach for relating growth rates of pathogen effective population size and time-varying variables that may impact the replicative fitness of a pathogen. The model is applied to real data from rabies virus and Staphylococcus aureus epidemics. We find a close correspondence between the estimated growth rates of a lineage of methicillin-resistant S. aureus and population-level prescription rates of beta-lactam antibiotics. The new models are implemented in an open source R package called skygrowth which is available at https://mrc-ide.github.io/skygrowth/.
AU  - Volz,E
AU  - Didelot,X
DO  - 10.1101/210054
PB  - bioRxiv
PY  - 2017///
TI  - Modeling the growth and decline of pathogen effective population size provides insight into epidemic dynamics and drivers of antimicrobial resistance
UR  - http://dx.doi.org/10.1101/210054
UR  - https://www.biorxiv.org/content/10.1101/210054v1
UR  - http://hdl.handle.net/10044/1/66617
ER  -
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