Abstract
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.
Highlights
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
The objective of our study was to evaluate the utility of a novel anomaly detection algorithm for pneumonia and influenza (P&I) mortality surveillance
Using current epidemic threshold methodologies, we found that 72 (20.6%) of weekly P&I mortality rates were beyond the epidemic threshold (Figure, panel A)
Summary
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. To fulfill surveillance needs in the United States, the Centers for Disease Control and Prevention maintains FluView [4], a public-facing web interface providing detailed results of their influenza surveillance program. Mortality is monitored and reported as epidemic if the percentage of total deaths is above a value termed the epidemic threshold. This threshold is defined at a P&I death rate 1.645 SDs above the seasonal. P&I mortality rates spiked above the epidemic threshold in approximately the same weeks
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