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Improved state-level influenza activity nowcasting in the United States leveraging Internet-based data sources and network approaches via ARGONet

View ORCID ProfileFred S. Lu, Mohammad W. Hattab, Leonardo Clemente, View ORCID ProfileMauricio Santillana
doi: https://doi.org/10.1101/344580
Fred S. Lu
1Computational Health Informatics Program, Boston Children’s Hospital,, Boston, MA, USA
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Mohammad W. Hattab
2Wyss Institute for Biologically Inspired Engineering, Harvard Medical School, Boston, MA, USA
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Leonardo Clemente
3Tecnológico de Monterrey, Monterrey, Mexico
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Mauricio Santillana
1Computational Health Informatics Program, Boston Children’s Hospital,, Boston, MA, USA
4Department of Pediatrics, Harvard Medical School, Boston, MA, USA
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  • For correspondence: msantill@fas.harvard.edu
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Abstract

In the presence of population-level health threats, precision public health approaches seek to provide the right intervention to the right population at the right time. Accurate real-time surveillance methodologies that can estimate infectious disease activity ahead of official healthcare-based reports, in relevant spatial resolutions, are critical to eventually achieve this goal. We introduce a novel methodological framework for this task which dynamically combines two distinct flu tracking techniques, using ensemble machine learning approaches, to achieve improved flu activity estimates at the state level in the US. The two predictive techniques behind the proposed ensemble methodology, named ARGONet, utilize (1) a dynamic and self-correcting statistical approach to combine flu-related Google search frequencies, information from electronic health records, and historical trends within a given state, as well as (2) a data-driven network-based approach that leverages spatial and temporal synchronicities observed in historical flu activity across states to improve state-level flu activity estimates. The proposed ensemble approach considerably outperforms each individual method and any previously proposed state-specific method for flu tracking, with higher correlations and lower prediction errors.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted June 14, 2018.
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Improved state-level influenza activity nowcasting in the United States leveraging Internet-based data sources and network approaches via ARGONet
Fred S. Lu, Mohammad W. Hattab, Leonardo Clemente, Mauricio Santillana
bioRxiv 344580; doi: https://doi.org/10.1101/344580
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Improved state-level influenza activity nowcasting in the United States leveraging Internet-based data sources and network approaches via ARGONet
Fred S. Lu, Mohammad W. Hattab, Leonardo Clemente, Mauricio Santillana
bioRxiv 344580; doi: https://doi.org/10.1101/344580

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