期刊
NATURE COMMUNICATIONS
卷 10, 期 -, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41467-018-08082-0
关键词
-
资金
- Centers for Disease Control and Prevention's Cooperative Agreement [PPHF 11797-998G-15]
- National Institute of General Medical Sciences of the National Institutes of Health [R01GM130668]
In the presence of health threats, precision public health approaches aim to provide targeted, timely, and population-specific interventions. Accurate surveillance methodologies that can estimate infectious disease activity ahead of official healthcare-based reports, at relevant spatial resolutions, are important for achieving this goal. Here we introduce a methodological framework which dynamically combines two distinct influenza tracking techniques, using an ensemble machine learning approach, to achieve improved state-level influenza activity estimates in the United States. The two predictive techniques behind the ensemble utilize (1) a self-correcting statistical method combining influenza-related Google search frequencies, information from electronic health records, and historical flu trends within each state, and (2) a network-based approach leveraging spatio-temporal synchronicities observed in historical influenza activity across states. The ensemble considerably outperforms each component method in addition to previously proposed state-specific methods for influenza tracking, with higher correlations and lower prediction errors.
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