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

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
stochastic processes with stationary increments 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://github.com/mrc-ide/skygrowth.