Multichart Schemes for Detecting Changes in Disease Incidence

Overview

Journal Comput Math Methods Med

Publisher Hindawi

Specialty Medical Informatics

Date 2020 Jun 9

PMID 32508978

Citations 1

Authors

Gideon Mensah Engmann

Dong Han

Affiliations

Soon will be listed here.

Abstract

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.

Citing Articles

Setting clinically relevant thresholds for the notification of canine disease outbreaks to veterinary practitioners: an exploratory qualitative interview study.

Cuartero C, Szilassy E, Radford A, Newton J, Sanchez-Vizcaino F Front Vet Sci. 2024; 11:1259021.

PMID: 38482169 PMC: 10936540. DOI: 10.3389/fvets.2024.1259021.

References

Tzala E, Best N . Bayesian latent variable modelling of multivariate spatio-temporal variation in cancer mortality. Stat Methods Med Res. 2007; 17(1):97-118. DOI: 10.1177/0962280207081243. View

Goldenberg A, Shmueli G, Caruana R, Fienberg S . Early statistical detection of anthrax outbreaks by tracking over-the-counter medication sales. Proc Natl Acad Sci U S A. 2002; 99(8):5237-40. PMC: 122753. DOI: 10.1073/pnas.042117499. View

Wong W, Moore A, Cooper G, Wagner M . WSARE: What's Strange About Recent Events?. J Urban Health. 2003; 80(2 Suppl 1):i66-75. PMC: 3456546. DOI: 10.1007/pl00022317. View

Fricker Jr R, Hegler B, Dunfee D . Comparing syndromic surveillance detection methods: EARS' versus a CUSUM-based methodology. Stat Med. 2008; 27(17):3407-29. DOI: 10.1002/sim.3197. View

Le Strat Y, Carrat F . Monitoring epidemiologic surveillance data using hidden Markov models. Stat Med. 1999; 18(24):3463-78. DOI: 10.1002/(sici)1097-0258(19991230)18:24<3463::aid-sim409>3.0.co;2-i. View

Bravata D, McDonald K, Smith W, Rydzak C, Szeto H, Buckeridge D . Systematic review: surveillance systems for early detection of bioterrorism-related diseases. Ann Intern Med. 2004; 140(11):910-22. DOI: 10.7326/0003-4819-140-11-200406010-00013. View

Reis B, Pagano M, Mandl K . Using temporal context to improve biosurveillance. Proc Natl Acad Sci U S A. 2003; 100(4):1961-5. PMC: 149941. DOI: 10.1073/pnas.0335026100. View

Bedubourg G, Le Strat Y . Evaluation and comparison of statistical methods for early temporal detection of outbreaks: A simulation-based study. PLoS One. 2017; 12(7):e0181227. PMC: 5513450. DOI: 10.1371/journal.pone.0181227. View

Rossi G, Lampugnani L, Marchi M . An approximate CUSUM procedure for surveillance of health events. Stat Med. 1999; 18(16):2111-22. DOI: 10.1002/(sici)1097-0258(19990830)18:16<2111::aid-sim171>3.0.co;2-q. View

10.

Diggle P . Spatio-temporal point processes, partial likelihood, foot and mouth disease. Stat Methods Med Res. 2006; 15(4):325-36. DOI: 10.1191/0962280206sm454oa. View

11.

Corberan-Vallet A . Prospective surveillance of multivariate spatial disease data. Stat Methods Med Res. 2012; 21(5):457-77. PMC: 3464351. DOI: 10.1177/0962280212446319. View

12.

Vial F, Wei W, Held L . Methodological challenges to multivariate syndromic surveillance: a case study using Swiss animal health data. BMC Vet Res. 2016; 12(1):288. PMC: 5168866. DOI: 10.1186/s12917-016-0914-2. View

13.

Sebastiani P, Mandl K, Szolovits P, Kohane I, Ramoni M . A Bayesian dynamic model for influenza surveillance. Stat Med. 2006; 25(11):1803-16. PMC: 4128871. DOI: 10.1002/sim.2566. View

14.

Cowling B, Ho L, Riley S, Leung G . Statistical algorithms for early detection of the annual influenza peak season in Hong Kong using sentinel surveillance data. Hong Kong Med J. 2013; 19 Suppl 4:4-5. View