Thresholds versus Anomaly Detection for Surveillance of Pneumonia and Influenza Mortality

Details

• Alternative Title:
  Emerg Infect Dis
• Personal Author:
  Wiemken, Timothy L. ; Rutschman, Ana Santos ; Niemotka, Samson L. ; Hoft, Daniel
• Description:
  Computational surveillance of pneumonia and influenza mortality in the United States using FluView uses epidemic thresholds to identify high mortality rates but is limited by statistical issues such as seasonality and autocorrelation. We used time series anomaly detection to improve recognition of high mortality rates. Results suggest that anomaly detection can complement mortality reporting.
• Subjects:
  Data Science Dispatch Immunization Infection Influenza Machine Learning Pneumonia Respiratory Infections Seasonality Surveillance Thresholds Versus Anomaly Detection For Surveillance Of Pneumonia And Influenza Mortality Time Series Vaccine Vaccine-preventable Diseases Viruses

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• Source:
  Emerg Infect Dis. 26(11):2733-2735
• Pubmed ID:
  33079038
• Pubmed Central ID:
  PMC7588519
• Document Type:
  Journal Article
• Volume:
  26
• Issue:
  11
• Collection(s):
  Emerging Infectious Diseases
• Main Document Checksum:
  urn:sha256:d7de80a48140abb68247e9be2c11f7b0fc59617963244b57fd09abffe7bb612e
• Download URL:
  https://stacks.cdc.gov/view/cdc/96904/cdc_96904_DS1.pdf
• File Type:
  [PDF - 1.15 MB ]