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Multi‐syndrome analysis of time series using PCA: A new concept for outbreak investigation
Author(s) -
Mohtashemi Mojdeh,
Kleinman Ken,
Yih W. Katherine
Publication year - 2007
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2872
Subject(s) - preprocessor , multivariate statistics , computer science , principal component analysis , data mining , multivariate analysis , sensitivity (control systems) , data pre processing , time series , parametric statistics , artificial intelligence , statistics , machine learning , mathematics , electronic engineering , engineering
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. Copyright © 2007 John Wiley & Sons, Ltd.