Assessing biases in phylodynamic inferences in the presence of super-spreaders.

ABSTRACT: Phylodynamic analyses using pathogen genetic data have become popular for making epidemiological inferences. However, many methods assume that the underlying host population follows homogenous mixing patterns. Nevertheless, in real disease outbreaks, a small number of individuals infect a disproportionately large number of others (super-spreaders). Our objective was to quantify the degree of bias in estimating the epidemic starting date in the presence of super-spreaders using different sample selection strategies. We simulated 100 epidemics of a hypothetical pathogen (fast evolving foot and mouth disease virus-like) over a real livestock movement network allowing the genetic mutations in pathogen sequence. Genetic sequences were sampled serially over the epidemic, which were then used to

SUBMITTER: Hidano A 

PROVIDER: S-EPMC6764146 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature