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Thresholds versus Anomaly Detection for Surveillance of Pneumonia and Influenza Mortality
- Source :
- Emerging Infectious Diseases, Emerging Infectious Diseases, Vol 26, Iss 11, Pp 2733-2735 (2020)
- Publication Year :
- 2020
- Publisher :
- Centers for Disease Control and Prevention (CDC), 2020.
-
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.
- Subjects :
- Microbiology (medical)
medicine.medical_specialty
Epidemiology
030231 tropical medicine
lcsh:Medicine
immunization
lcsh:Infectious and parasitic diseases
Machine Learning
respiratory infections
03 medical and health sciences
0302 clinical medicine
vaccine
Influenza, Human
medicine
Humans
lcsh:RC109-216
Thresholds versus Anomaly Detection for Surveillance of Pneumonia and Influenza Mortality
viruses
030212 general & internal medicine
Epidemics
seasonality
business.industry
lcsh:R
Data Science
High mortality
Dispatch
Pneumonia
medicine.disease
infection
United States
vaccine-preventable diseases
Infectious Diseases
Population Surveillance
Emergency medicine
surveillance
Anomaly detection
time series
influenza
business
Subjects
Details
- ISSN :
- 10806059 and 10806040
- Volume :
- 26
- Database :
- OpenAIRE
- Journal :
- Emerging Infectious Diseases
- Accession number :
- edsair.doi.dedup.....6b775f62c48266fd480155b070c03654
- Full Text :
- https://doi.org/10.3201/eid2611.200706