A model-based clustering method to detect infectious disease transmission outbreaks from sequence variation.

ABSTRACT: Clustering infections by genetic similarity is a popular technique for identifying potential outbreaks of infectious disease, in part because sequences are now routinely collected for clinical management of many infections. A diverse number of nonparametric clustering methods have been developed for this purpose. These methods are generally intuitive, rapid to compute, and readily scale with large data sets. However, we have found that nonparametric clustering methods can be biased towards identifying clusters of diagnosis-where individuals are sampled sooner post-infection-rather than the clusters of rapid transmission that are meant to be potential foci for public health efforts. We develop a fundamentally new approach to genetic clustering based on fitting a Markov-modulated Poisson pro

SUBMITTER: McCloskey RM 

PROVIDER: S-EPMC5703573 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature