A graph-based evidence synthesis approach to detecting outbreak clusters: An application to dog rabies.

ABSTRACT: Early assessment of infectious disease outbreaks is key to implementing timely and effective control measures. In particular, rapidly recognising whether infected individuals stem from a single outbreak sustained by local transmission, or from repeated introductions, is crucial to adopt effective interventions. In this study, we introduce a new framework for combining several data streams, e.g. temporal, spatial and genetic data, to identify clusters of related cases of an infectious disease. Our method explicitly accounts for underreporting, and allows incorporating preexisting information about the disease, such as its serial interval, spatial kernel, and mutation rate. We define, for each data stream, a graph connecting all cases, with edges weighted by the corresponding pairwise distan

SUBMITTER: Cori A 

PROVIDER: S-EPMC6312344 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature