Surveillance of Nonhomogeneous Poisson Processes

Richards, Sarah C.; Woodall, William H.; Purdy, Gregory T.

doi:10.1080/00401706.2014.927790

Cited by 20 publications

(3 citation statements)

References 30 publications

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“…The means of the diseases seem to be dynamic, so we seek to detect changes in the average counts of the diseases. Many researchers have developed statistical methods for detecting changes in disease incidence or rates (see Mei et al [28], Jiang et al [29] and Richards et al [30]). We proposed the multi-CUSUM chart for detecting changes in disease incidence.…”

Section: An Illustration Withmentioning

confidence: 99%

“…Jiang et al [29] compared the performance of several CUSUM methods subject to Poisson distribution. Richards et al [30] proposed an invariant Poisson control charting scheme and applied it to monitor the number of emergency arrivals observed at the Baltimore Veterans Affairs Medical Center. Most of these models or schemes monitor one variable or disease at a time.…”

Section: Introductionmentioning

confidence: 99%

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Multichart Schemes for Detecting Changes in Disease Incidence

Engmann

Han

2020

Computational and Mathematical Methods in Medicine

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Several methods have been proposed in open literatures for detecting changes in disease outbreak or incidence. Most of these methods are likelihood-based as well as the direct application of Shewhart, CUSUM and EWMA schemes. We use CUSUM, EWMA and EWMA-CUSUM multi-chart schemes to detect changes in disease incidence. Multi-chart is a combination of several single charts that detects changes in a process and have been shown to have elegant properties in the sense that they are fast in detecting changes in a process as well as being computationally less expensive. Simulation results show that the multi-CUSUM chart is faster than EWMA and EWMA-CUSUM multi-charts in detecting shifts in the rate parameter. A real illustration with health data is used to demonstrate the efficiency of the schemes.

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Section: An Illustration Withmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multichart Schemes for Detecting Changes in Disease Incidence

Engmann

Han

2020

Computational and Mathematical Methods in Medicine

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“…CUSUM control charts are frequently used in health surveillance since they are more efficient than the Shewhart chart for detection of small shifts. Recently, in the surveillance context, 1–4 control charts based on generalized linear models (GLM) 5 have been built. In these control charts, important covariate information may be included as the seasonal pattern (since more hospitalizations due to several diseases are expected during the winter), adjustment for at-risk population or other explanatory variables.…”

Section: Introductionmentioning

confidence: 99%

CUSUM chart to monitor autocorrelated counts using Negative Binomial GARMA model

Albarracin

Alencar

2017

Stat Methods Med Res

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Cumulative sum control charts have been used for health surveillance due to its efficiency to detect soon small shifts in the monitored series. However, these charts may fail when data are autocorrelated. An alternative procedure is to build a control chart based on the residuals after fitting autoregressive moving average models, but these models usually assume Gaussian distribution for the residuals. In practical health surveillance, count series can be modeled by Poisson or Negative Binomial regression, this last to control overdispersion. To include serial correlations, generalized autoregressive moving average models are proposed. The main contribution of the current article is to measure the impact, in terms of average run length on the performance of cumulative sum charts when the serial correlation is neglected in the regression model. Different statistics based on transformations, the deviance residual, and the likelihood ratio are used to build cumulative sum control charts to monitor counts with time varying means, including trend and seasonal effects. The monitoring of the weekly number of hospital admissions due to respiratory diseases for people aged over 65 years in the city São Paulo-Brazil is considered as an illustration of the current method.

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An Overview of Control Charts for High‐quality Processes

Ali

Pievatolo

Göb

2016

Quality & Reliability Eng

113

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Major difficulties in the study of high‐quality processes with traditional process monitoring techniques are a high false alarm rate and a negative lower control limit. The purpose of time‐between‐events control charts is to overcome existing problems in the high‐quality process monitoring setup. Time‐between‐events charts detect an out‐of‐control situation without great loss of sensitivity as compared with existing charts. High‐quality control charts gained much attention over the last decade because of the technological revolution. This article is dedicated to providing an overview of recent research and presenting it in a unifying framework. To summarize results and draw a precise conclusion from the statistical point of view, cross‐tabulations are also given in this article. Copyright © 2016 John Wiley & Sons, Ltd.

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Surveillance of Nonhomogeneous Poisson Processes

Cited by 20 publications

References 30 publications

Multichart Schemes for Detecting Changes in Disease Incidence

Multichart Schemes for Detecting Changes in Disease Incidence

CUSUM chart to monitor autocorrelated counts using Negative Binomial GARMA model

An Overview of Control Charts for High‐quality Processes

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