Author: Wiemken, Timothy L.; Rutschman, Ana Santos; Niemotka, Samson L.; Hoft, Daniel
Title: Thresholds versus Anomaly Detection for Surveillance of Pneumonia and Influenza Mortality Cord-id: 1wj7m322 Document date: 2020_11_25
ID: 1wj7m322
Snippet: 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.
Document: 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.
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