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

Predicting hosts and vectors During outbreaks of mysterious infections, events can rapidly become dangerous and confusing. A combination of increasing experience with outbreaks and genome-sequencing technology now means the pathogen can often be identified within days. But for some of the most frightening viral pathogens, the originating hosts and possible vectors often remain obscure. Babayan et al. took sequence data from more than 500 single-stranded RNA viruses (see the Perspective by Woolhouse) and used machine-learning algorithms to extract evolutionary signals imprinted in the virus sequence that offer information about its original hosts and if an arthropod vector, and what type, plays a part in the virus's natural ecology. Science, this issue p. 577; see also p. 524 Machine learning algorithms detect coevolutionary biases in viral genomes that predict hosts. 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.