English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Whole genome sequencing of pathogens from multiple hosts in an epidemicoffers the potential to investigate who infected whom with unparalleledresolution, potentially yielding important insights into disease dynamics andthe impact of control measures. We considered disease outbreaks in a settingwith dense genomic sampling, and formulated stochastic epidemic models toinvestigate person-to-person transmission, based on observed genomic andepidemiological data. We constructed models in which the genetic distancebetween sampled genotypes depends on the epidemiological relationship betweenthe hosts. A data augmented Markov chain Monte Carlo algorithm was used tosample over the transmission trees, providing a posterior probability for anygiven transmission route. We investigated the predictive performance of ourmethodology using simulated data, demonstrating high sensitivity andspecificity, particularly for rapidly mutating pathogens with lowtransmissibility. We then analyzed data collected during an outbreak ofmethicillin-resistant Staphylococcus aureus in a hospital, identifying probabletransmission routes and estimating epidemiological parameters. Our approachovercomes limitations of previous methods, providing a framework with theflexibility to allow for unobserved infection times, multiple independentintroductions of the pathogen, and within-host genetic diversity, as well asallowing forward simulation.

Reconstructing transmission trees for communicable diseases using densely sampled genetic data