Wavelet-Based Monitoring for Biosurveillance
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 syndr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2013-07-01
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| Series: | Axioms |
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| Online Access: | http://www.mdpi.com/2075-1680/2/3/345 |
| _version_ | 1819212465773215744 |
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| author | Galit Shmueli |
| author_facet | Galit Shmueli |
| author_sort | Galit Shmueli |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-23T06:43:24Z |
| format | Article |
| id | doaj.art-999a92d97756435ba608d5025d8d441d |
| institution | Directory Open Access Journal |
| issn | 2075-1680 |
| language | English |
| last_indexed | 2024-12-23T06:43:24Z |
| publishDate | 2013-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Axioms |
| spelling | doaj.art-999a92d97756435ba608d5025d8d441d2022-12-21T17:56:39ZengMDPI AGAxioms2075-16802013-07-012334537010.3390/axioms2030345Wavelet-Based Monitoring for BiosurveillanceGalit ShmueliBiosurveillance, 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.http://www.mdpi.com/2075-1680/2/3/345early detectionautocorrelationdisease outbreaksyndromic datadiscrete wavelet transform |
| spellingShingle | Galit Shmueli Wavelet-Based Monitoring for Biosurveillance Axioms early detection autocorrelation disease outbreak syndromic data discrete wavelet transform |
| title | Wavelet-Based Monitoring for Biosurveillance |
| title_full | Wavelet-Based Monitoring for Biosurveillance |
| title_fullStr | Wavelet-Based Monitoring for Biosurveillance |
| title_full_unstemmed | Wavelet-Based Monitoring for Biosurveillance |
| title_short | Wavelet-Based Monitoring for Biosurveillance |
| title_sort | wavelet based monitoring for biosurveillance |
| topic | early detection autocorrelation disease outbreak syndromic data discrete wavelet transform |
| url | http://www.mdpi.com/2075-1680/2/3/345 |
| work_keys_str_mv | AT galitshmueli waveletbasedmonitoringforbiosurveillance |