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Modeling the growth and decline of pathogen effective population size provides insight into epidemic dynamics and drivers of antimicrobial resistance

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Title: Modeling the growth and decline of pathogen effective population size provides insight into epidemic dynamics and drivers of antimicrobial resistance
Authors: Volz, E
Didelot, X
Item Type: Working Paper
Abstract: 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/.
Issue Date: 27-Oct-2017
URI: http://hdl.handle.net/10044/1/66617
DOI: https://dx.doi.org/10.1101/210054
Publisher: bioRxiv
Copyright Statement: © 2017 The Author(s). This preprint is made available under the terms of the Creative Commons Attribution licence (CC-BY 4.0 - https://creativecommons.org/licenses/by/4.0/)
Sponsor/Funder: Medical Research Council (MRC)
Funder's Grant Number: MR/K010174/1B
Publication Status: Published
Appears in Collections:Epidemiology, Public Health and Primary Care



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