Open AccessThis article is
- freely available
Wavelet-Based Monitoring for Biosurveillance
Indian School of Business, Gachibowli, Hyderabad 500 032, India
Received: 5 June 2013; in revised form: 18 June 2013 / Accepted: 19 June 2013 / Published: 9 July 2013
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
Article StatisticsClick here to load and display the download statistics.
Notes: Multiple requests from the same IP address are counted as one view.
Cite This Article
MDPI and ACS Style
Shmueli, G. Wavelet-Based Monitoring for Biosurveillance. Axioms 2013, 2, 345-370.
Shmueli G. Wavelet-Based Monitoring for Biosurveillance. Axioms. 2013; 2(3):345-370.
Shmueli, Galit. 2013. "Wavelet-Based Monitoring for Biosurveillance." Axioms 2, no. 3: 345-370.