Multichart Schemes for Detecting Changes in Disease Incidence.


Journal

Computational and mathematical methods in medicine
ISSN: 1748-6718
Titre abrégé: Comput Math Methods Med
Pays: United States
ID NLM: 101277751

Informations de publication

Date de publication:
2020
Historique:
received: 06 11 2019
revised: 06 03 2020
accepted: 27 03 2020
entrez: 9 6 2020
pubmed: 9 6 2020
medline: 27 4 2021
Statut: epublish

Résumé

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.

Identifiants

pubmed: 32508978
doi: 10.1155/2020/7267801
pmc: PMC7245694
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

7267801

Informations de copyright

Copyright © 2020 Gideon Mensah Engmann and Dong Han.

Déclaration de conflit d'intérêts

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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Auteurs

Gideon Mensah Engmann (GM)

School of Mathematical Sciences, Shanghai Jiao Tong University, 200240 Shanghai, China.
Department of Statistics, University for Development Studies, Navrongo, Ghana.

Dong Han (D)

School of Mathematical Sciences, Shanghai Jiao Tong University, 200240 Shanghai, China.

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Classifications MeSH