Rodent reservoirs of future zoonotic diseases

被引:410
作者
Han, Barbara A. [1 ]
Schmidt, John Paul [2 ]
Bowden, Sarah E. [2 ]
Drake, John M. [2 ]
机构
[1] Cary Inst Ecosyst Studies, Millbrook, NY 12545 USA
[2] Univ Georgia, Odum Sch Ecol, Athens, GA 30602 USA
关键词
machine learning; disease forecasting; prediction; pace-of-life hypothesis; generalized boosted regression trees; IDENTIFYING RESERVOIRS; INFECTIOUS-DISEASES; LIFE; RISK; EXTINCTION; DIVERSITY; ORIGINS;
D O I
10.1073/pnas.1501598112
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页码:7039 / 7044
页数:6
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