Model misspecification misleads inference of the spatial dynamics of disease outbreaks.

ABSTRACT: Epidemiology has been transformed by the advent of Bayesian phylodynamic models that allow researchers to infer the geographic history of pathogen dispersal over a set of discrete geographic areas [1, 2]. These models provide powerful tools for understanding the spatial dynamics of disease outbreaks, but contain many parameters that are inferred from minimal geographic information (i.e., the single area in which each pathogen was sampled). Consequently, inferences under these models are inherently sensitive to our prior assumptions about the model parameters. Here, we demonstrate that the default priors used in empirical phylodynamic studies make strong and biologically unrealistic assumptions about the underlying geographic process. We provide empirical evidence that these unrealistic pri

SUBMITTER: Gao J 

PROVIDER: S-EPMC10089176 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature