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Improved state-level influenza nowcasting in the United States leveraging Internet-based data and network approaches
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,...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6329822/ https://www.ncbi.nlm.nih.gov/pubmed/30635558 http://dx.doi.org/10.1038/s41467-018-08082-0 |
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author | Lu, Fred S. Hattab, Mohammad W. Clemente, Cesar Leonardo Biggerstaff, Matthew Santillana, Mauricio |
author_facet | Lu, Fred S. Hattab, Mohammad W. Clemente, Cesar Leonardo Biggerstaff, Matthew Santillana, Mauricio |
author_sort | Lu, Fred S. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6329822 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-63298222019-01-15 Improved state-level influenza nowcasting in the United States leveraging Internet-based data and network approaches Lu, Fred S. Hattab, Mohammad W. Clemente, Cesar Leonardo Biggerstaff, Matthew Santillana, Mauricio Nat Commun Article 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. Nature Publishing Group UK 2019-01-11 /pmc/articles/PMC6329822/ /pubmed/30635558 http://dx.doi.org/10.1038/s41467-018-08082-0 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Lu, Fred S. Hattab, Mohammad W. Clemente, Cesar Leonardo Biggerstaff, Matthew Santillana, Mauricio Improved state-level influenza nowcasting in the United States leveraging Internet-based data and network approaches |
title | Improved state-level influenza nowcasting in the United States leveraging Internet-based data and network approaches |
title_full | Improved state-level influenza nowcasting in the United States leveraging Internet-based data and network approaches |
title_fullStr | Improved state-level influenza nowcasting in the United States leveraging Internet-based data and network approaches |
title_full_unstemmed | Improved state-level influenza nowcasting in the United States leveraging Internet-based data and network approaches |
title_short | Improved state-level influenza nowcasting in the United States leveraging Internet-based data and network approaches |
title_sort | improved state-level influenza nowcasting in the united states leveraging internet-based data and network approaches |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6329822/ https://www.ncbi.nlm.nih.gov/pubmed/30635558 http://dx.doi.org/10.1038/s41467-018-08082-0 |
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