Details:

Alternative Title:
Nat Commun
Personal Author:
Lu, Fred S. ; Hattab, Mohammad W. ; Clemente, Cesar Leonardo ; Biggerstaff, Matthew ; Santillana, Mauricio
Lu, Fred S. ; Hattab, Mohammad W. ; Clemente, Cesar Leonardo ; Biggerstaff, Matthew ; Santillana, Mauricio Less -
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.
Subject:
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Data Analysis
Databases, Factual
Electronic Health Records
Epidemiological Monitoring
Humans
Influenza, Human
Internet
Search Engine

Source:
Nat Commun. 10
Pubmed ID:
30635558
Pubmed Central ID:
PMC6329822
Document Type:
Journal Article
Funding:
R01 GM130668/GM/NIGMS NIH HHS/United States
Place as Subject:
United States
Collection(s):
CDC Public Access
Main Document Checksum:
[+]
urn:sha256:fd88a049b529fe4a5c817cef0e52687b36fe0178f9039030e28c202250e7698c
File Type:

Supporting Files

• 	41467_2018_8082_MOESM1_ESM.pdf	pdf
  	41467_2018_8082_MOESM2_ESM.pdf	pdf
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