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• W2910597988 abstract "Abstract 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." @default.
• W2910597988 created "2019-01-25" @default.
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• W2910597988 date "2019-01-11" @default.
• W2910597988 modified "2023-10-13" @default.
• W2910597988 title "Improved state-level influenza nowcasting in the United States leveraging Internet-based data and network approaches" @default.
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• W2910597988 doi "https://doi.org/10.1038/s41467-018-08082-0" @default.
• W2910597988 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6329822" @default.
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• W2910597988 hasPublicationYear "2019" @default.
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