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.
Highlights
Introduction to Modern BiosurveillanceBiosurveillance is the practice of monitoring data for the early detection of disease outbreaks.Traditional biosurveillance has focused on the collection and monitoring of medical and public health data that verify the existence of disease outbreaks
From our experience with syndromic data, we found that the detail coefficient series are well approximated by an autoregressive model of the order of seven with zero-mean
We return with further detail to the syndromic data that we described in Section 1 and plotted in International Classification of Diseases, Ninth Revision (ICD-9) codes
Summary
Biosurveillance is the practice of monitoring data for the early detection of disease outbreaks. Syndromic data include information such as over-the-counter and pharmacy medication sales, calls to nurse hotlines, school absence records, web searches for symptomatic keywords and chief complaints by patients visiting hospital emergency departments These data do not directly measure an infection, but it is assumed that they contain an earlier, though weaker, signature of a disease outbreak. Many surveillance systems that are deployed across the country routinely collect data from multiple sources on a daily basis, and these data are transferred with very little delay to the biosurveillance systems (see Fienberg and Shmueli [1] and Shmueli and Burkom [2] for a description of this process and examples from several surveillance systems) This means that the multiple syndromic data streams that are collected are very different in nature from traditional data. This has motivated our investigation of wavelet methods, which have been adopted in many fields and, in particular, are suitable for the type of data and problems in biosurveillance
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