Abstract
Phylodynamic methods reveal the spatial and temporal dynamics of viral geographic spread, and have featured prominently in studies of the COVID-19 pandemic. Virtually all such studies are based on phylodynamic models that assume-despite direct and compelling evidence to the contrary-that rates of viral geographic dispersal are constant through time. Here, we: (1) extend phylodynamic models to allow both the average and relative rates of viral dispersal to vary independently between pre-specified time intervals; (2) implement methods to infer the number and timing of viral dispersal events between areas; and (3) develop statistics to assess the absolute fit of discrete-geographic phylodynamic models to empirical datasets. We first validate our new methods using simulations, and then apply them to a SARS-CoV-2 dataset from the early phase of the COVID-19 pandemic. We show that: (1) under simulation, failure to accommodate interval-specific variation in the study data will severely bias parameter estimates; (2) in practice, our interval-specific discrete-geographic phylodynamic models can significantly improve the relative and absolute fit to empirical data; and (3) the increased realism of our interval-specific models provides qualitatively different inferences regarding key aspects of the COVID-19 pandemic-revealing significant temporal variation in global viral dispersal rates, viral dispersal routes, and the number of viral dispersal events between areas-and alters interpretations regarding the efficacy of intervention measures to mitigate the pandemic.
Keywords: biogeographic history; epidemiology; phylodynamic models; phylogeography.
【저자키워드】 Epidemiology, biogeographic history, phylodynamic models, phylogeography., 【초록키워드】 COVID-19, SARS-CoV-2, Efficacy, pandemic, COVID-19 pandemic, Variation, Intervention, Spread, Simulation, Statistics, Interpretation, phylogeography, dataset, phylodynamic, temporal dynamics, Evidence, dispersal, Inference, failure, early phase, average, measure, contrary, datasets, parameter, Alter, mitigate, event, IMPROVE, develop, significantly, provide, 【제목키워드】 Dynamics, Model, Inference, New,