Trigonometric Regression for Analysis of Public Health Surveillance Data
Statistical challenges in monitoring modern biosurveillance data are well described in the literature. Even though assumptions of normality, independence, and stationarity are typically violated in the biosurveillance data, statistical process control (SPC) charts adopted from industry have been wid...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2014/673293 |
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| author | Steven E. Rigdon George Turabelidze Ehsan Jahanpour |
| author_facet | Steven E. Rigdon George Turabelidze Ehsan Jahanpour |
| author_sort | Steven E. Rigdon |
| collection | DOAJ |
| description | Statistical challenges in monitoring modern biosurveillance data are well described in the literature. Even though assumptions of normality, independence, and stationarity are typically violated in the biosurveillance data, statistical process control (SPC) charts adopted from industry have been widely used in public health for communicable disease monitoring. But, blind usage of SPC charts in public health that ignores the characteristics of disease surveillance data may result in poor detection of disease outbreaks and/or excessive false-positive alarms. Thus, improved biosurveillance systems are clearly needed, and participation of statisticians knowledgeable in SPC alongside epidemiologists in the design and evaluation of such systems can be more productive. We describe and study a method for monitoring reportable disease counts using a Poisson distribution whose mean is allowed to vary depending on the week of the year. The seasonality is modeled by a trigonometric function whose parameters can be estimated by some baseline set of data. We study the ability of such a model to detect an outbreak. Specifically, we estimate the probability of detection (POD), the average number of weeks to signal given that a signal has occurred (conditional expected delay, or CED), and the false-positive rate (FPR, the average number of false-alarms per year). |
| format | Article |
| id | doaj-art-711e22685ac842fc82cc45193070618c |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-711e22685ac842fc82cc45193070618c2025-02-03T01:10:10ZengWileyJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/673293673293Trigonometric Regression for Analysis of Public Health Surveillance DataSteven E. Rigdon0George Turabelidze1Ehsan Jahanpour2Department of Biostatistics, College for Public Health and Social Justice, Saint Louis University, 3545 Lafayette Avenue, St. Louis, MO 63104, USAMissouri Department of Health and Senior Services, 220 S. Jefferson Avenue, St. Louis, MO 63103, USAMissouri Department of Health and Senior Services, 220 S. Jefferson Avenue, St. Louis, MO 63103, USAStatistical challenges in monitoring modern biosurveillance data are well described in the literature. Even though assumptions of normality, independence, and stationarity are typically violated in the biosurveillance data, statistical process control (SPC) charts adopted from industry have been widely used in public health for communicable disease monitoring. But, blind usage of SPC charts in public health that ignores the characteristics of disease surveillance data may result in poor detection of disease outbreaks and/or excessive false-positive alarms. Thus, improved biosurveillance systems are clearly needed, and participation of statisticians knowledgeable in SPC alongside epidemiologists in the design and evaluation of such systems can be more productive. We describe and study a method for monitoring reportable disease counts using a Poisson distribution whose mean is allowed to vary depending on the week of the year. The seasonality is modeled by a trigonometric function whose parameters can be estimated by some baseline set of data. We study the ability of such a model to detect an outbreak. Specifically, we estimate the probability of detection (POD), the average number of weeks to signal given that a signal has occurred (conditional expected delay, or CED), and the false-positive rate (FPR, the average number of false-alarms per year).http://dx.doi.org/10.1155/2014/673293 |
| spellingShingle | Steven E. Rigdon George Turabelidze Ehsan Jahanpour Trigonometric Regression for Analysis of Public Health Surveillance Data Journal of Applied Mathematics |
| title | Trigonometric Regression for Analysis of Public Health Surveillance Data |
| title_full | Trigonometric Regression for Analysis of Public Health Surveillance Data |
| title_fullStr | Trigonometric Regression for Analysis of Public Health Surveillance Data |
| title_full_unstemmed | Trigonometric Regression for Analysis of Public Health Surveillance Data |
| title_short | Trigonometric Regression for Analysis of Public Health Surveillance Data |
| title_sort | trigonometric regression for analysis of public health surveillance data |
| url | http://dx.doi.org/10.1155/2014/673293 |
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