Multichart Schemes for Detecting Changes in Disease Incidence.
Algorithms
Biosurveillance
/ methods
Computational Biology
Computer Simulation
Disease Outbreaks
/ statistics & numerical data
Ghana
/ epidemiology
Humans
Incidence
Likelihood Functions
Models, Statistical
Monte Carlo Method
Poisson Distribution
Public Health Surveillance
/ methods
Sentinel Surveillance
Tuberculosis
/ epidemiology
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
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
7267801Informations 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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