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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Identifying the animal origins of RNA viruses requires years of field and laboratory studies that stall responses to emerging infectious diseases. Using large genomic and ecological datasets, we demonstrate that animal reservoirs and the existence and identity of arthropod vectors can be predicted directly from viral genome sequences via machine learning. We illustrate the ability of these models to predict the epidemiology of diverse viruses across most human-infective families of single-stranded RNA viruses, including 69 viruses with previously elusive or never-investigated reservoirs or vectors. Models such as these, which capitalize on the proliferation of low-cost genomic sequencing, can narrow the time lag between virus discovery and targeted research, surveillance, and management.

Predicting reservoir hosts and arthropod vectors from evolutionary signatures in RNA virus genomes