Copula-based CUSUM charts for monitoring infectious disease using Markovian Poisson processes

Wu, Cang; Si, Shubin; Huang, Wenpo; Jiang, Wei

doi:10.1016/j.cie.2022.108536

Cited by 6 publications

(3 citation statements)

References 64 publications

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“…Parpoula 22 applied a distribution-free control charting technique based on changepoint analysis to detect epidemics. Copula-based CUSUM charts to monitor infectious disease by adopting Markovian Poisson processes were presented by Wu et al 23 Bersimis et al 24 developed a method to monitor the number of disease events and their spatial distribution using convex hull-based control charts. Wang and Zwetsloot 25 proposed a transfer learning-based multivariate chart for detecting dengue outbreaks in target areas with limited observations in Hong Kong.…”

Section: Introductionmentioning

confidence: 99%

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Directionally sensitive adaptive MEWMA charts with fixed and variable sampling rates

Haq,

Khoo

2024

Quality & Reliability Eng

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A primary objective in detecting disease outbreaks is to precisely identify substantial increases in process means to control the spread of outbreaks. In this paper, we introduce a novel directionally sensitive adaptive multivariate exponentially weighted moving average chart for efficiently detecting either increases or decreases in the mean vector of a multivariate normally distributed process. In addition, the chart's sensitivity is enhanced through features such as variable sample size, variable sampling interval, and a combination of both. Monte Carlo simulation is employed to estimate zero‐state and steady‐state average time‐to‐signal (ATS) profiles. Performance measures, including expected weighted and relative ATS, are used for evaluating the performances of multivariate charts. Our results indicate that the proposed charts outperform existing charts in detecting various mean shift sizes. The application of these multivariate charts is demonstrated through illustrative examples.

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Section: Introductionmentioning

confidence: 99%

“…Parpoula 22 applied a distribution‐free control charting technique based on change‐point analysis to detect epidemics. Copula‐based CUSUM charts to monitor infectious disease by adopting Markovian Poisson processes were presented by Wu et al 23 . Bersimis et al 24 .…”

Section: Introductionmentioning

confidence: 99%

Directionally sensitive adaptive MEWMA charts with fixed and variable sampling rates

Haq,

Khoo

2024

Quality & Reliability Eng

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“…For the purpose of monitoring the process, various control charts have been proposed. Conditionally, expected value control charts [2] and copula-based cumulative sum control charts [3] are among the latest publications about control charts. The term control chart was originally developed in the 1930s by Shewhart [4] to predict the expected range of the results of the process analysis and to determine the improvement of the quality process in order to prevent any problems in the product's variation.…”

mentioning

confidence: 99%

A Type-2 Fuzzy u-Control Chart Considering Probability-Based Average Run Length

Mohd Razali,

Abdullah,

Ab Ghani

et al. 2024

Contemp. Math.

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Fuzzy sets are an emerging trend in shaping the development of control charts for statistical process control. The sets are germane to vague data that comes from incomplete or inaccurate measurements. Nevertheless, fuzzy sets are inadequate in some areas of industry since their membership functions are crisp numbers. The fuzzy sets are not fully able to compute higher levels of uncertainty, which might degrade the performance of the analysis. Therefore, type-2 fuzzy sets are proposed to be merged with control charts since these sets are hypothesized to be more capable of detecting a defect in process control. This paper aims to develop interval type-2 fuzzy u (IT2Fu) charts as a new approach to detecting defects. In addition, this paper presents a comparative analysis of performances between traditional u-control charts, type-1 fuzzy u-control charts, and type-2 fuzzy u-control charts. 23 samples of lubricant data with 48 subgroups were examined to identify the defects. The output showed that all of the control charts produced almost similar results except for data 14, which is “out of control” in IT2Fu-control charts but “in control” in traditional u-control charts and “rather in control” in type-1 fuzzy u-control charts. Furthermore, the performances of the charts were compared using a probability-based average run length (ARL), where probability type 1 error is computed. It was found that the ARL value of the IT2Fu-control chart showed the lowest value among the three types of charts. The analysis indicated that the IT2Fu-control chart outperformed the traditional u-control chart and the type-1 fuzzy u-control chart. The results obtained seem to support the idea that IT2Fu-control charts are more sensitive compared to type 1 fuzzy u-control charts and traditional u-control charts, so that IT2Fu-control charts are able to adequately support incomplete and vague data on process control.

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Generating Copula‐Correlated Random Variables: A Sequentialceptance‐Rejection Method

Dai,

Hu,

Lei

2024

Naval Research Logistics

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In this paper, we consider the problem of generating a random vector whose joint distribution is characterized by a copula function and their marginal distribution functions. We propose a sequential acceptance‐rejection method, which avoids the inversion of both marginal distributions and conditional distributions of the copula function which are needed in traditional generation methods. In our method, we first generate samples based on the marginal distributions and then use an acceptance‐rejection procedure to construct samples with the desired dependence structure. The method is also applicable in generating time series random variables. We prove that the computational cost of our method only grows linearly to their dimensionality for several types of copulas. Finally, we provide extensive numerical experiments which show our method is quite efficient in comparison with traditional methods.

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Copula-based CUSUM charts for monitoring infectious disease using Markovian Poisson processes

Cited by 6 publications

References 64 publications

Directionally sensitive adaptive MEWMA charts with fixed and variable sampling rates

Directionally sensitive adaptive MEWMA charts with fixed and variable sampling rates

A Type-2 Fuzzy u-Control Chart Considering Probability-Based Average Run Length

Generating Copula‐Correlated Random Variables: A Sequentialceptance‐Rejection Method

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