Axioms 2013, 2(3), 345-370; doi:10.3390/axioms2030345

Wavelet-Based Monitoring for Biosurveillance

Received: 5 June 2013; in revised form: 18 June 2013 / Accepted: 19 June 2013 / Published: 9 July 2013
(This article belongs to the Special Issue Wavelets and Applications)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: Biosurveillance, focused on the early detection of disease outbreaks, relies on classical statistical control charts for detecting disease outbreaks. However, such methods are not always suitable in this context. Assumptions of normality, independence and stationarity are typically violated in syndromic data. Furthermore, outbreak signatures are typically of unknown patterns and, therefore, call for general detectors. We propose wavelet-based methods, which make less assumptions and are suitable for detecting abnormalities of unknown form. Wavelets have been widely used for data denoising and compression, but little work has been published on using them for monitoring. We discuss monitoring-based issues and illustrate them using data on military clinic visits in the USA.
Keywords: early detection; autocorrelation; disease outbreak; syndromic data; discrete wavelet transform
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MDPI and ACS Style

Shmueli, G. Wavelet-Based Monitoring for Biosurveillance. Axioms 2013, 2, 345-370.

AMA Style

Shmueli G. Wavelet-Based Monitoring for Biosurveillance. Axioms. 2013; 2(3):345-370.

Chicago/Turabian Style

Shmueli, Galit. 2013. "Wavelet-Based Monitoring for Biosurveillance." Axioms 2, no. 3: 345-370.

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