Future directions in analytics for infectious disease intelligence

被引：33
作者：
Han, Barbara A. [1 ]
Drake, John M. [2 ,3 ]
机构：
[1] Cary Inst Ecosyst Studies, Millbrook, NY 12545 USA
[2] Univ Georgia, Odum Sch Ecol, Athens, GA 30602 USA
[3] Univ Georgia, Ctr Ecol Infect Dis, Athens, GA 30602 USA
来源：
EMBO REPORTS | 2016年 / 17卷 / 06期
关键词：
D O I：
10.15252/embr.201642534
中图分类号：
Q5 [生物化学]; Q7 [分子生物学];
学科分类号：
071010 ; 081704 ;
摘要：
Anticipating the emergence of infectious diseases would be more effective in reducing their toll, rather than reacting to an outbreak. Machine learning and simulation tools that use environmental, epidemiological and molecular data could help manage and analyse the flow or risks, and inform appropriate actions by public health authorities. © 2016 The Authors.
引用
收藏
页码：785 / 789
页数：5
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