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

Details

• Alternative Title:
  Nat Commun
• Personal Author:
  Lu, Fred S. ; Hattab, Mohammad W. ; Clemente, Cesar Leonardo ; Biggerstaff, Matthew ; Santillana, Mauricio
• 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.
• Subjects:
  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
• Volume:
  10
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:fd88a049b529fe4a5c817cef0e52687b36fe0178f9039030e28c202250e7698c
• Download URL:
  https://stacks.cdc.gov/view/cdc/77367/cdc_77367_DS1.pdf
• File Type:
  [PDF - 2.73 MB ]