Spatial modeling of Mycobacterium tuberculosis transmission with dyadic genetic relatedness data.

ABSTRACT: Understanding factors that contribute to the increased likelihood of pathogen transmission between two individuals is important for infection control. However, analyzing measures of pathogen relatedness to estimate these associations is complicated due to correlation arising from the presence of the same individual across multiple dyadic outcomes, potential spatial correlation caused by unmeasured transmission dynamics, and the distinctive distributional characteristics of some of the outcomes. We develop two novel hierarchical Bayesian spatial methods for analyzing dyadic pathogen genetic relatedness data, in the form of patristic distances and transmission probabilities, that simultaneously address each of these complications. Using individual-level spatially correlated random effect par

SUBMITTER: Warren JL 

PROVIDER: S-EPMC10404301 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature