title: Rodent reservoirs of future zoonotic diseases.
abstract: The increasing frequency of zoonotic disease events underscores a need to develop forecasting tools toward a more preemptive approach to outbreak investigation. We apply machine learning to data describing the traits and zoonotic pathogen diversity of the most speciose group of mammals, the rodents, which also comprise a disproportionate number of zoonotic disease reservoirs. Our models predict reservoir status in this group with over 90% accuracy, identifying species with high probabilities of harboring undiscovered zoonotic pathogens based on trait profiles that may serve as rules of thumb to distinguish reservoirs from nonreservoir species. Key predictors of zoonotic reservoirs include biogeographical properties, such as range size, as well as intrinsic host traits associated with lifetime reproductive output. Predicted hotspots of novel rodent reservoir diversity occur in the Middle East and Central Asia and the Midwestern United States.
authors.affiliation: Cary Institute of Ecosystem Studies, Millbrook, NY 12545; and hanb@caryinstitute.org.
authors.forename: Barbara A
authors.initials: BA
authors.lastname: Han
authors.affiliation: Odum School of Ecology, University of Georgia, Athens, GA 30602.
authors.forename: John Paul
authors.initials: JP
authors.lastname: Schmidt
authors.affiliation: Odum School of Ecology, University of Georgia, Athens, GA 30602.
authors.forename: Sarah E
authors.initials: SE
authors.lastname: Bowden
authors.affiliation: Odum School of Ecology, University of Georgia, Athens, GA 30602.
authors.forename: John M
authors.initials: JM
authors.lastname: Drake
mesh_terms: D000367:Age Factors; D000818:Animals; D001185:Artificial Intelligence; D019295:Computational Biology; D004197:Disease Reservoirs; D005544:Forecasting / Q000379:methods; D062305:Geographic Mapping; D005843:Geography; D006801:Humans; D011156:Population Density; D011634:Public Health / Q000379:methods*; D012044:Regression Analysis; D012098:Reproduction / Q000502:physiology; D012377:Rodentia / Q000254:growth & development*; D012741:Sexual Maturation / Q000502:physiology; D013045:Species Specificity; D015047:Zoonoses / Q000635:transmission*
keywords: disease forecasting; generalized boosted regression trees; machine learning; pace-of-life hypothesis; prediction