Properties and Use of the Shewhart Method and Its Followers

Frisén, Marianne

doi:10.1080/07474940701247164

Cited by 19 publications

(8 citation statements)

References 97 publications

(107 reference statements)

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“…Thus, the Shewhart method has optimal error probabilities when we want to discriminate between a change at the current time point and the case that no change has yet occurred for these alternatives. By several criteria, however, the Shewhart method performs poorly31 for small and moderate shifts.…”

Section: Optimal Methodsmentioning

confidence: 99%

Methods and evaluations for surveillance in industry, business, finance, and public health

Frisén

2011

Quality & Reliability Eng

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An overview on surveillance in different areas is given. Even though methods have been developed under different scientific cultures, the statistical concepts can be the same. When the statistical problems are the same, progress in one area can be used also in other areas. The aim of surveillance is to detect an important change in an underlying process as soon as possible after the change has occurred. In practice, we have complexities such as gradual changes and multivariate settings. Approaches to handling some of these complexities are discussed. The correspondence between the measures for evaluation and the aims of the application is important. Thus, the choice of evaluation measure deserves attention. The commonly used ARL criterion should be used with care.

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Section: Optimal Methodsmentioning

confidence: 99%

Methods and evaluations for surveillance in industry, business, finance, and public health

Frisén

2011

Quality & Reliability Eng

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“…The limit is chosen so that the probability of exceeding the alarm limit would be constant for each time point if we disregard the fact that we have a surveillance system with repeated decisions. The history and optimality of the Shewhart method is described by Frisn (2007). Since the Shewhart method does not utilize all available information, it is optimal only in very special situations.…”

Section: Some General Surveillance Methodsmentioning

confidence: 99%

Statistical Surveillance of Epidemics: Peak Detection of Influenza in Sweden

Bock¹,

Andersson²,

Frisén³

2008

Biometrical J

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SummaryA statistical surveillance system gives a signal as soon as data give enough evidence of an important event. We consider on-line surveillance systems for detecting changes in influenza incidence. One important feature of the influenza cycle is the start of the influenza season, and another one is the change to a decline (the peak). In this report we discuss statistical methods for on-line peak detection. One motive for doing this is the need for health resource planning. Surveillance systems were adapted for Swedish data on laboratory verified diagnoses of influenza. In Sweden, the parameters of the influenza cycles vary too much from year to year for parametric methods to be useful. We suggest a non-parametric method based on the monotonicity properties of the increase and decline around a peak. A Monte Carlo study indicated that this method has useful stochastic properties. The method was applied to Swedish data on laboratory verified diagnoses of influenza for seven periods.

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“…Within this framework, Frisén (2003, 2007) showed that the Shewhart chart method, for which p ( x s )= L ( s , s ), maximizes the probability of detecting the outbreak at time T = s , and, asymptotically (as μ 1 increases), minimizes the time to detection, for a fixed false detection probability. In the Shiryaev–Roberts method (Shiryaev, 1963; Roberts, 1966), dependence on T through w ( s , t ) is eliminated by setting w ( s , t )=1, which may be justified asymptotically as corresponding to a vanishingly low probability of an outbreak.…”

Section: Comparison and Evaluation Of Prospective Outbreak Detectimentioning

confidence: 99%

Statistical Methods for the Prospective Detection of Infectious Disease Outbreaks: A Review

Unkel

Farrington

Garthwaite

et al. 2011

Journal of the Royal Statistical Society Series A: Statistics in Society

290

257

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Unusual clusters of disease must be detected rapidly for effective public health interventions to be introduced. Over the past decade there has been a surge in interest in statistical methods for the early detection of infectious disease outbreaks. This growth in interest has given rise to much new methodological work, ranging across the spectrum of statistical methods. This paper presents a comprehensive review of the statistical approaches that have been proposed. Applications to both laboratory and syndromic surveillance data are provided to illustrate the various methods.

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Properties and Use of the Shewhart Method and Its Followers

Cited by 19 publications

References 97 publications

Methods and evaluations for surveillance in industry, business, finance, and public health

Methods and evaluations for surveillance in industry, business, finance, and public health

Statistical Surveillance of Epidemics: Peak Detection of Influenza in Sweden

Statistical Methods for the Prospective Detection of Infectious Disease Outbreaks: A Review

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