Multi-syndrome analysis of time series using PCA: a new concept for outbreak investigation.
To date, despite widespread availability of time series data on multiple syndromes, multivariate analysis of syndromic data remains under-explored. We present a non-parametric multivariate framework for early detection of temporal anomalies based on principal components analysis of historical data on multiple syndromes. We introduce simulated outbreaks of different shapes and magnitudes into the historical data, and compare the detection sensitivity and timeliness of the multi-syndrome detection method with those of uni-syndrome. We find that the multi-syndrome detection framework provides a powerful tool for identifying such designated abnormalities in the data and significantly improves upon the detection sensitivity and timeliness of uni-syndrome analysis. The proposed multivariate framework requires minimal preprocessing of the data and can be easily adopted in settings where temporal information on multiple syndromes are routinely collected and processed, and thus can be an integral component of real-time surveillance systems.
Mohtashemi
Mojdeh
M
MITRE Corporation, Bedford, MA 01730, USA. mojdeh@mit.edu
Kleinman
Ken
K
Yih
W Katherine
WK
D003141:Communicable Diseases / Q000453:epidemiology*; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D018562:Disease Transmission, Infectious / Q000706:statistics & numerical data; D042241:Early Diagnosis; D015991:Epidemiologic Measurements; D006305:Health Status Indicators; D006801:Humans; D008404:Massachusetts; D015233:Models, Statistical; D011237:Predictive Value of Tests; D025341:Principal Component Analysis; D040442:Public Health Informatics / Q000379:methods; D012680:Sensitivity and Specificity; D018571:Sentinel Surveillance; D013029:Space-Time Clustering; D013577:Syndrome; D018511:Systems Integration; D014481:United States