Public health monitoring using control charts based on convex hull

Bersimis, Sotirios; Sachlas, Athanasios; Polychronis, Economou

doi:10.1080/27684520.2023.2246448

Cited by 2 publications

(1 citation statement)

References 82 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

Directionally sensitive adaptive MEWMA charts with fixed and variable sampling rates

Haq,

Khoo

2024

Quality & Reliability Eng

View full text Add to dashboard Cite

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.

show abstract