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Proc Natl Acad Sci U S A. 2015 Jun 2;112(22):7039-44. doi: 10.1073/pnas.1501598112. Epub 2015 May 18.

Rodent reservoirs of future zoonotic diseases.

Author information

1
Cary Institute of Ecosystem Studies, Millbrook, NY 12545; and hanb@caryinstitute.org.
2
Odum School of Ecology, University of Georgia, Athens, GA 30602.

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.

KEYWORDS:

disease forecasting; generalized boosted regression trees; machine learning; pace-of-life hypothesis; prediction

PMID:
26038558
PMCID:
PMC4460448
DOI:
10.1073/pnas.1501598112
[Indexed for MEDLINE]
Free PMC Article

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